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What features cause mechano sensory adaptation?

What features cause mechano sensory adaptation?


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In relation to mechanoreceptors (e.g. pacinian corpuscles), what stops a constant stimulus from producing action potentials?

I understand that adaption is used to filter out stimuli that aren't changing, but what are the cellular mechanisms going on within the neurone that drive this process?


At the molecular level this is called receptor desensitization. This is the reason why for example spices (like red hot chili paper) taste more pungent the first time you put them in your mouth and less and less subsequent times (in this case the receptor is called TRPV1).

Mechanosensory perception is mediated, at least in part, by similar transient receptor potential (TRP) channels therefore mechanosensory perception is also subjected to desensitization at the molecular level. In this particular case it is specifically called homologous desensitization.

This is, in my opinion, the primarily reason why a constant stimulus will stop producing action potentials, in essence the receptor will be desensitized and does not provoke a membrane-potential change that mediates the transmission of a neuronal signal.


Footnote

Desensitization in TRP channels happens because those channels open to let $Ca^{2+}$ ions to enter the cell and after prolongated stimulation the intracellular $Ca^{2+}$ concentration will reach the same $Ca^{2+}$ level of the extracellular medium preventing more $Ca^{2+}$ ions to enter the cell.

$Ca^{2+}$ influx into the cell is what provokes a difference in membrane-potential (i.e. the cell is now electrically charged) which mediates a signal that is then transmitted to the neuronal circuitry.


Short answer
In case of Pacinian corpusles, the adaptation is generally ascribed to the mechanical characteristics of the outer capsule of the receptor. The capsule's onion-like structure quickly molds itself to pressure stimuli, thereby rapidly desensitizing the receptor.

Background
There are two classes of mechanoreceptors in the skin based on their rates of adaptation, namely rapidly adapting and slowly adapting receptors. The Pacinian corpuscles (and hair follicle receptors) are of the rapidly adapting type. These receptors swiftly become unresponsive (they adapt) when a pressure stimulus is applied, but faithfully transmit rapidly changing stimuli (such as vibrations).

Pacinian corpuscles are specialized vibration receptors. Their dendritic region is shaped as an onion-like structure with layers of stacked lamellae:


Specialized dendritic capsule of the Pacinian corpuscle. Source: Biologymad

These lamellae act as high-pass filters that result in a sharp drop in sensitivity below 150 Hz (Johnson, 2001). If this capsule is dissected from the receptor and a pressure stimulus is directly applied to the sensor element underneat the capsule, the receptor response to a sustained stimulus substantially increases (Mendelson & Loewenstein, 1964). Basically, these elastic lamellae mold their shape to the stimulus and pressure stimuli are only transmitted for a few milliseconds, after which the lamellae absorb the pressure. So only a one or a few spikes are generated at the start of a pressure stimulus. Upon release of the pressure stimulus another spike or two are generated. Hence, a continued pressure stimulus is not faithfully reproduced. However, vibratory stimuli generate spikes on the pressure onset as well as offset as well, and this happens on every phase of the vibration. Hence, vibratory stimuli are faithfully transmitted.


Response of Pacinian corpuscle to a sustained pressure stimulus (indentation of the skin) and superimposed vibratory stimuli. Note the vigorous response to vibrations, but the rapid adaptation to the static pressure stimulus. Source: Zavantag

Although the adaptation of the Pacinian corpuscle is generally ascribed to the mechanical properties of its capsule, further high-pass filtering may be accommodated by neurophysiological properties that limit spike initiation, as described by Cagliari2005.

References
- Johnson, Curr Opin Neuobiol 2001;11:455-461
- Mendelson & Loewenstein, Science 1964;3618:554-5

Further reading
Why are skin tactile receptors considered to be phasic receptors?


Adaptation

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Adaptation, in biology, the process by which a species becomes fitted to its environment it is the result of natural selection’s acting upon heritable variation over several generations. Organisms are adapted to their environments in a great variety of ways: in their structure, physiology, and genetics, in their locomotion or dispersal, in their means of defense and attack, in their reproduction and development, and in other respects.

The word adaptation does not stem from its current usage in evolutionary biology but rather dates back to the early 17th century, when it indicated a relation between design and function or how something fits into something else. In biology this general idea has been coopted so that adaptation has three meanings. First, in a physiological sense, an animal or plant can adapt by adjusting to its immediate environment—for instance, by changing its temperature or metabolism with an increase in altitude. Second, and more commonly, the word adaptation refers either to the process of becoming adapted or to the features of organisms that promote reproductive success relative to other possible features. Here the process of adaptation is driven by genetic variations among individuals that become adapted to—that is, have greater success in—a specific environmental context. A classic example is shown by the melanistic (dark) phenotype of the peppered moth (Biston betularia), which increased in numbers in Britain following the Industrial Revolution as dark-coloured moths appeared cryptic against soot-darkened trees and escaped predation by birds. The process of adaptation occurs through an eventual change in the gene frequency relative to advantages conferred by a particular characteristic, as with the coloration of wings in the moths.

The third and more popular view of adaptation is in regard to the form of a feature that has evolved by natural selection for a specific function. Examples include the long necks of giraffes for feeding in the tops of trees, the streamlined bodies of aquatic fish and mammals, the light bones of flying birds and mammals, and the long daggerlike canine teeth of carnivores.

All biologists agree that organismal traits commonly reflect adaptations. However, much disagreement has arisen over the role of history and constraint in the appearance of traits as well as the best methodology for showing that a trait is truly an adaptation. A trait may be a function of history rather than adaptation. The so-called panda’s thumb, or radial sesamoid bone, is a wrist bone that now functions as an opposable thumb, allowing giant pandas to grasp and manipulate bamboo stems with dexterity. The ancestors of giant pandas and all closely related species, such as black bears, raccoons, and red pandas, also have sesamoid bones, though the latter species do not feed on bamboo or use the bone for feeding behaviour. Therefore, this bone is not an adaptation for bamboo feeding.

The English naturalist Charles Darwin, in On the Origin of Species by Means of Natural Selection (1859), recognized the problem of determining whether a feature evolved for the function it currently serves:

The sutures of the skulls of young mammals have been advanced as a beautiful adaptation for aiding parturition [birth], and no doubt they facilitate, or may be indispensable for this act but as sutures occur in the skulls of young birds and reptiles, which only have to escape from a broken egg, we may infer that this structure has arisen from the laws of growth, and has been taken advantage of in the parturition of the higher animals.

Thus, before explaining that a trait is an adaptation, it is necessary to identify whether it is also shown in ancestors and therefore may have evolved historically for different functions from those that it now serves.

Another problem in designating a trait as an adaptation is that the trait may be a necessary consequence, or constraint, of physics or chemistry. One of the most common forms of constraint involves the function of anatomical traits that differ in size. For example, canine teeth are larger in carnivores than in herbivores. This difference in size is often explained as an adaptation for predation. However, the size of canine teeth is also related to overall body size (such scaling is known as allometry), as shown by large carnivores such as leopards that have bigger canines than do small carnivores such as weasels. Thus, differences in many animal and plant characteristics, such as the sizes of young, duration of developmental periods (e.g., gestation, longevity), or patterns and sizes of tree leaves, are related to physical size constraints.

Adaptive explanations in biology are difficult to test because they include many traits and require different methodologies. Experimental approaches are important for showing that any small variability, as in many physiological or behavioral differences, is an adaptation. The most rigorous methods are those that combine experimental approaches with information from natural settings—for example, in showing that the beaks of different species of Galapagos finch are shaped differently because they are adapted to feed on seeds of different sizes.

The comparative method, using comparisons across species that have evolved independently, is an effective means for studying historical and physical constraints. This approach involves using statistical methods to account for differences in size (allometry) and evolutionary trees (phylogenies) for tracing trait evolution among lineages.


Why We Experience Sensory Adaptation

Sensory adaptation is a reduction in sensitivity to a stimulus after constant exposure to it. While sensory adaptation reduces our awareness of a constant stimulus, it helps free up our attention and resources to attend to other stimuli in the environment around us. All five of our senses can experience sensory adaptation. Our senses are constantly adjusting to what's around us, as well as to us individually and what we are experiencing, such as aging or disease.

Just imagine what it would be like if you didn't experience sensory adaptation. You might find yourself overwhelmed by the pungent smell of onions coming from the kitchen or the blare of the television from the living room. Since constant exposure to a sensory stimulus reduces our sensitivity to it, we are able to shift our attention to other things in our environment rather than focusing on one overwhelming stimulus.


Chapter 4 Questions

1. What is the most basic sense?

2. Why is this sense necessary for the most primitive life-forms?

3. How are stretch-sensitive mechanosensory designs fundamentally different from photosensory and chemosensory mechanisms?

5. Lateral inhibition is a form of adaptation. What signal function does it accomplish?

6. How is a military fighter pilot like the green lacewing?

7. How is coarse coding manifested in the human auditory system?

8. What are the three middle-ear ossicles, and what is their function?

9. How are static and dynamic equilibrium changes sensed in the human auditory system?

10. Neurons can fire at most 1kHz, or at a rate of 1ms between action potentials. Nyquist sampling requires two samples per highest-frequency wave period, which means that such a neuronal firing rate can only encode up to 500 Hz. How is it that humans can discern components beyond 20 times that amount (10kHz)?

11. What were some of the significant results from Webb’s robotic implementation of cricket phonotaxis?

12. Why is it so amazing that we must “cut corners” in computational processing to get our electronic models to simulate real-time behavior of biological sensory systems?

13. What are the two information pathways in the auditory system of the barn owl?

14. How do first-order systems, such as differentiators and integrators, and second-order systems differ in their step responses?

15. What is the basic idea behind the “See-Hear” system?

16. What advantages does the MEMS-based silicon cochlea have over the analog VLSI-based silicon cochlea?


Materials and methods

Experiments were performed on adult locusts (Schistocerca gregariaForskål) of either sex taken from our crowded laboratory colony. Animals were restrained ventral side up in Plasticine™. The middle leg could be restrained independently and positioned for adequate access to the different mechanoreceptors. Parts of the ventral cuticle were removed in order to expose the meso- and metathoracic ganglia as well as thoracic nerves and muscles.

Intracellular recordings

A wax-coated steel platform was used to stabilize the meso- and metathoracic ganglia. The ganglionic sheath of the mesothoracic ganglion was treated for 2 min with a 1% (w/v) solution of protease (Sigma type XIV) to facilitate penetration of the ganglion with glass microelectrodes.

Microelectrodes were either filled with 2 mol l -1 potassium acetate or 1 mol l -1 lithium chloride when used for later staining with Lucifer yellow (only in the tips), giving a tip resistance of 40–80 MΩ.

The dye was applied iontophoretically by 500 ms pulses of negative current at 1 Hz. Motoneurons were identified by correlating the spikes recorded intracellularly from neuropilar processes, while extracellular potentials were recorded from the efferent nerve 3C2 with a pair of 50 μm steel wires. The M103d fast motoneuron could also be identified as eliciting visible twitch-contractions of M103d upon stimulation (nomenclature of thoracic nerves according to Campbell,1961).

Afferent spike recordings

Spikes from single tibial hair sensilla were recorded by placing a saline-filled microelectrode over the cut shaft of the trichoid sensillum(Hodgson et al., 1955). Afferent spikes from the exteroceptive campaniform sensilla (CS) at the base of each tibial spur were recorded with hook electrodes at the peripheral nerves 5B3 (anterior row of spurs) or 5B4 (posterior row of spurs) located just beneath the ventral cuticle close to the receptors(Mücke, 1991).

In order to record afferent discharges selectively via the proprioceptive CS on various leg segments, an electrolytically sharpened tungsten wire was carefully pushed through the dome-shaped structure of the sensillum to make contact with the receptor haemolymph.

Trochanteral groups of CS were recorded in isolated legs with suction electrodes from the proximal stumps of their afferent nerve (5B2a), in which at least one large trachea was opened to the air at the saline surface, while the persistent pumping of the myogenic accessory leg heart of the trochanter(Hustert, 1999) maintained saline flow in the leg. This expands viability of the preparation from several minutes to several hours.

Motoneuron identification

Backfills of motor nerves were made to reveal the innervation of the mesothoracic depressor trochanteris muscle and central branching pattern of each motoneuron. After removal of the thoracic ganglia from the animal, the cut end of the particular nerve was placed in a miniature Vaseline™well, filled with a near-saturation solution of 3000 Mrdextrane conjugated with the fluorescent dyes fluorescein isothiocyanate(FITC) or tetraethyl-rhodamine-isothiocyanate (TRITC) (obtained from Molecular Probes Europe, Leiden, The Netherlands). The preparations were immersed in saline and incubated for 24 h at 4°C to allow diffusion of the dye throughout the neurons. After dissecting out the ganglia, they were fixated in 4% paraformaldehyde, dehydrated in ethanol and, after clearing in methyl salicylate, were viewed under a Leitz Aristoplan epifluorescence microscope and drawn or photographed from whole mounts.

Receptor stimulation

All receptors were stimulated mechanically. In the case of the hair sensilla, a blunt microelectrode was glued to a piezoelectric tongue driven by a function generator and mounted onto a micromanipulator. Ramp-like deflections were used to stimulate the hair sensilla. The tibial spurs were deflected by a minuten pin fixed to the piezoelectric tongue. The proprioceptive CS were stimulated directly by applying pressure perpendicular to the surface of the cuticle close to the receptor with a minuten pin. In some cases, the tungsten electrode itself was pushed carefully towards the sensillum to elicit spikes.

In order to define delays between afferent spike generation of two different receptor types, e.g. a tibial spur and a trochanteral CS, a two-channel function generator driving two piezoelectric devices was used for the exact timing of receptor stimulation.

Conduction measurements

Afferent conduction times to the CNS were measured by recording extracellularly from a peripheral site of the particular nerve close to the mechanoreceptor and at the main leg nerve 5 where it enters the ganglion. Central latencies could thus be estimated by subtracting this time of spike propagation from the overall delay to the postsynaptic potential(Laurent and Hustert, 1988). In most cases, signal averaging was used.

Mechano-sensory conduction

Delays between impact-like tension changes onto the tarsus and first afferent spikes in the proximal CS were measured in middle legs, excised carefully at the thoracocoxal joint. The leg was positioned as in the standing animal, with the coxa, trochanter and femur horizontal and the tibia vertical. Only the coxa was fixed on a small platform ventrally, and dorsal parts of the coxa and trochanter levator muscles were removed. The tendon of the trochanter depressor was also pinned with a minuten pin to the platform. This avoided dorsal excursions of the leg when mechanical stimuli directed dorsally at the tarsus were applied by a piezoelectric bender from below.

Force measurements

Measurements of the time required for the transfer of force from the tip to the base of a leg were also performed on a fresh, isolated middle leg in the natural positions of still stance. The ventral coxa was mounted onto a force transducer while forces from the tarsus were applied via one pad(pulvillus). A piezoelectric tongue (bimorphic piezoceramic strip Valvo PXE70 Valvo, Hamburg, Germany) with a minuten needle extending from its moveable end was mounted on another force transducer. The strain produced by the piezoelectric tongue during ramp-like deflection (generated by a function generator) was monitored when the minuten pin indented the highly elastic tarsal pulvillus. By mounting the device on a micromanipulator, the tip of the pin could touch the tarsal pulvilli very delicately so that just the area around one of the canal sensilla (Kendall,1970) was indented by the stimulus. This strain was sufficient to be recorded via the whole leg as a force at the coxa-attached transducer.

Recording and analysis

Recordings were displayed on a digital oscilloscope (Hitachi, Fukuoka,Japan) and stored on magnetic tape for later computer analysis by Neurolab 7.0(Hedwig and Knepper, 1992) and Datapac 2000 (RUN Technologies, Mission Viejo, CA, USA) software.


RESULTS AND DISCUSSION

The intrinsic response characteristics of the three main types of mechano-sensory neuron to sinusoidal current injections at frequencies between 0.2 and 20 Hz were investigated in Hirudo medicinalis. Sinusoidal current injections can be used to approximate the input-output characteristics of neurons. This approximation does not necessarily reflect the peripheral sensory input instead it highlights the central processing capabilities.

N cells show a high sensory threshold, as relatively strong forces must be applied to the skin to elicit their supra-threshold response (Lewis and Kristan, 1998 Nicholls and Baylor, 1968 Pinato and Torre, 2000). Here, N cells were identified by their characteristic location (Nicholls and Baylor, 1968 Yau, 1976) (Fig. 1A) and action potential after-hyperpolarization (Fig. 1B). Recorded N cells had an average resting membrane potential of −41.3±2.7 mV (n=9). Sinusoidal current injections of 10 cycles ranging from 0.2 to 20 Hz were applied to the somatic region. The current amplitude was adjusted to regularly elicit action potentials to a current sinewave delivered at 1 Hz (i.e. slightly over threshold). N cells responded in a frequency-dependent fashion. The number of evoked action potential decreased with increasing stimulation frequency (Fig. 1C). The average of the maximally fired number of action potentials (20.4±4.2 Hz n=9) was elicited at 0.2 Hz. In only one of nine cells, the maximal action potential number was elicited at 1 Hz, not at 0.2 Hz. At 20 Hz, only one of these cells was still able to generate an action potential (Fig. 1D). To compare the firing behavior across frequencies in more detail, we first compensated for the duration of stimulation by calculating the action potential frequency. Second, we subtracted this response rate from the stimulation frequency. This analysis generates positive values when multiple action potentials occur during a single stimulation cycle (Fig. 1E). When no action potential is elicited, a value equal to the stimulation frequency is obtained (Fig. 1E). This analysis showed that N-cells only responded with multiple action potentials to sine-stimulations <1 Hz, with one action potential at 1 Hz, and stopped responding at an average rate of 6.2±2.3 Hz. Taken together, these results indicated that the N cells responded with low-pass filter properties to sinusoidal stimulations.

Intrinsic frequency response profile of N cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded N cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. Action potential waveform shows the N cell characteristics long after hyperpolarization. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 0.5 Hz middle, 2 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=9). (E) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D. Inset, magnified low stimulation frequencies in a linear graph.

Intrinsic frequency response profile of N cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded N cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. Action potential waveform shows the N cell characteristics long after hyperpolarization. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 0.5 Hz middle, 2 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=9). (E) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D. Inset, magnified low stimulation frequencies in a linear graph.

P cells are classically characterized by their intermediate to high sensory threshold to skin stimulation (Lewis and Kristan, 1998 Nicholls and Baylor, 1968 Pinato and Torre, 2000), and therefore their sensory threshold overlaps substantially with N cells. Here, P cells were identified by their characteristic location (Nicholls and Baylor, 1968 Yau, 1976) and their onset response to strong stimulation with a square pulse current (Fig. 2A,B). The average P cell membrane resting potential of −46.6±1.6 mV (n=8) resembled that reported previously (Schlue and Deitmer, 1984). The strength of sinusoidal stimulation intensity was adjusted to reliably elicit action potentials at a frequency of 5 Hz (Fig. 2C), as this appeared to be the lowest frequency at which low amplitudes of injected current were sufficient to drive P cells efficiently. At this intensity, low stimulation frequency failed to generate supra-threshold responses. The lowest stimulation frequency that elicited action potentials in P cells was 4.6±0.7 Hz, on average (n=8) (Fig. 2D,E). P cells, however, followed increasing sinusoidal stimulation frequency with reliable action potential firing largely up to ∼15 Hz. The maximal response of P cells was found at an average stimulation frequency of 9.8±1.6 Hz. However, in only one of eight recorded P cells, did the response rate decrease below 50% at higher stimulation frequencies (Fig. 2D,E). Overall, the P cell response dropped by only 25% (Fig. 3G) at a stimulation frequency of 20 Hz. Taken together, these results indicated that P cell membrane properties can be regarded as acting as an intrinsic high-pass filter.

Intrinsic frequency response profile of P cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded P cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. The rapid onset action potential is characteristic of P cells. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 1 Hz middle, 5 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=8). (E) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D.

Intrinsic frequency response profile of P cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded P cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. The rapid onset action potential is characteristic of P cells. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 1 Hz middle, 5 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=8). (E) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D.

Intrinsic frequency response profile of T cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded T cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. The initial burst of action potential is characteristic of T cells. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 1 Hz middle, 0.5 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=9). (E) Normalized number of action potentials as a function of stimulation frequency. The dotted horizontal line indicates half maximal firing. Symbols are as in D. (F) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D. (G) Normalized action potential firing in response to different sinusoidal stimulation frequencies of injected current for N (red), P (black) and T (blue) cells. Only stimulation frequencies where at least four cells were recorded are presented (mean±s.d.).

Intrinsic frequency response profile of T cells to sinusoidal current injections. (A) Fluorescent labeling of a recorded T cell was used to identify the cell location post-hoc by comparison to a schematic drawing of the known location of leech sensory neurons. (B) Square pulse current injection just below (left) and above (right) the action potential threshold. The initial burst of action potential is characteristic of T cells. (C) Voltage responses to different sinusoidal stimulation frequencies: left, 1 Hz middle, 0.5 Hz right, 10 Hz. (D) Number of supra-threshold responses summed over the 10 sinusoidal cycles as a function of stimulation frequency. Each symbol represents the response of a single cell (n=9). (E) Normalized number of action potentials as a function of stimulation frequency. The dotted horizontal line indicates half maximal firing. Symbols are as in D. (F) Action potential firing rate from which the stimulation rate was subtracted is plotted as a logarithmic function of the stimulation frequency. The dotted zero line indicates the same firing rate as the number of stimulation cycles, hence faithful firing. The solid line represents the zero action potential line, where no supra-threshold response was elicited at any time during stimulation. Symbols are as in D. (G) Normalized action potential firing in response to different sinusoidal stimulation frequencies of injected current for N (red), P (black) and T (blue) cells. Only stimulation frequencies where at least four cells were recorded are presented (mean±s.d.).

T cells have a lower sensory stimulation threshold compared to N and P cells (Lewis and Kristan, 1998 Nicholls and Baylor, 1968 Pinato and Torre, 2000). Furthermore, T cells are characterized by location (Nicholls and Baylor, 1968 Yau, 1976), and by a bursting onset response to square pulse current injections (Kretzberg et al., 2007 Schlue, 1976a,b). We used the location and onset bursting to identify T cells (Fig. 3A,B). The average T cell resting potential was −42.6±1.2 mV (n=9). For the applied sine wave current injections, the intensity was adjusted to elicit reliable firing between 2 and 5 Hz (Fig. 3C). Again, these stimulation frequencies appeared to be the lowest to evoke action potentials with sinusoidal stimulations frequencies efficiently with low current amplitudes. Using this approach, only one of nine T cells responded to stimulation rates below 1 Hz. The lowest frequency that generated supra-threshold excitation was 1.85±0.46 Hz, on average. The maximal number of action potentials was elicited at an average stimulation frequency of 4.0±0.7 Hz (Fig. 3D,E). Here, the frequency of action potentials exceeded the stimulation frequency and was 6.2±1.4 Hz (Fig. 3F). At high frequencies, the number of action potentials elicited decreased for all cells tested (Fig. 3D) in only two of the nine T-cells the response remained above 50% and in six cells the response dropped below one action potential per stimulation cycle (Fig. 3E). The average stimulation frequency that still elicited 50% of maximal firing rate was 12.6±2.6 Hz, on average. It is worthy to note that in six of these cells the action potential response steadily declined. T cells thus responded with elevated firing rates to sinusoidal modulated voltage deflections between 4 and 12 Hz, and their overall ability to rate code might therefore be intrinsically tuned to behave as a band pass filter.

We have here identified different, frequency-dependent supra-threshold response patterns based on firing rates for mechano-sensory neurons in Hirudo medicinalis. N cells responded preferentially to low, P cells to high, and T cells to intermediate stimulation frequencies, illustrating a differential intrinsic tuning to sinusoidal current stimulations (Fig. 3G). These intrinsic properties thus appear to generate specific low-, band- and high-pass filters for supra-threshold firing rates, respectively.

To verify that the intrinsic membrane properties of mechano-sensory neurons generate different filters, we once more recorded from all three mechano-sensory neurons however this time varying the stimulation intensity over a reduced range of stimulation frequencies (Fig. 4). For the stimulation frequencies of 0.5, 2, 5, 10 and 20 Hz, the intensity ranged from 0.1 to 2.5 nA. Fig. 4A depicts a subset of the P cell responses when challenged with this stimulus matrix of sinusoidal current injections. The stimulation matrix was used to determine whether the different filter responses of the N, T and P cells are independent of stimulation intensity, and to see how the response profiles segregate. For this reason, the action potential number was extracted from each sinusoidal current injection and given as color-coded intensity in Fig. 4B-D. With increasing stimulation intensity, N cells responded with increasing number of action potentials especially at low stimulation frequencies (Fig. 4E). At high stimulation intensities, the voltage excursion became so large that N cells could not fire action potentials or the action potentials were masked. At high stimulation intensities, therefore, supra-threshold N cell responses are missing. T cells predominantly increased action potential firing with increasing stimulation intensities at intermediate stimulation frequencies with a maximum firing locked to 2 Hz (Fig. 4C). T cell firing remained lower at higher stimulation frequencies. For P cells, the increase in stimulation intensity led to increased firing starting at frequencies of 10 Hz, which became maximal at 5 Hz for strong stimulation intensities (Fig. 4D). Importantly, and in contrast to T and N cells, P cells were never excited at low frequencies. Presenting the number of action potentials as discrete functions of stimulation frequencies (Fig. 4E-G) illustrates the same finding. Taken together, these results indicated that N, T and P cells generate intrinsically a low-, band- and high-pass filter, respectively.

Supra-threshold response profiles are different in N, T and P cells, and depend on stimulation intensity and frequency. (A) Sub- and supra-threshold membrane potential responses (black) of a P cell to current injections (grey) of 0.5 Hz (left), 2 Hz (left center), 10 Hz (right center) and 20 Hz (right) at stimulation intensities of 0.2 nA (top), 0.15 nA (middle) and 0.05 nA (bottom). (B-D) Number of action potentials plotted as a function of stimulation intensity and frequency for (B) N, (C) T and (D) P cells. Line color spectrum from dark blue to red represents the number of action potentials from low to high numbers. (E-G) Number of action potentials elicited in response to current injections of different stimulation intensities: (E) N cell: 0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 1.0, 1.2, 1.5 and 2.0 nA (n=4-21) (F) T cell: 0.3, 0.5, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 1.7, 2.0, 2.2 and 2.5 nA (n=4-16) and (G) P cell: 0.5, 0.7, 0.9, 1.0, 1.2, 1.5, 1.7, 2.0, 2.2 and 2.5 nA as a function of the stimulation frequency (0.5, 2, 5, 10 and 20 Hz). Error bars represent s.e.m.

Supra-threshold response profiles are different in N, T and P cells, and depend on stimulation intensity and frequency. (A) Sub- and supra-threshold membrane potential responses (black) of a P cell to current injections (grey) of 0.5 Hz (left), 2 Hz (left center), 10 Hz (right center) and 20 Hz (right) at stimulation intensities of 0.2 nA (top), 0.15 nA (middle) and 0.05 nA (bottom). (B-D) Number of action potentials plotted as a function of stimulation intensity and frequency for (B) N, (C) T and (D) P cells. Line color spectrum from dark blue to red represents the number of action potentials from low to high numbers. (E-G) Number of action potentials elicited in response to current injections of different stimulation intensities: (E) N cell: 0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 1.0, 1.2, 1.5 and 2.0 nA (n=4-21) (F) T cell: 0.3, 0.5, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 1.7, 2.0, 2.2 and 2.5 nA (n=4-16) and (G) P cell: 0.5, 0.7, 0.9, 1.0, 1.2, 1.5, 1.7, 2.0, 2.2 and 2.5 nA as a function of the stimulation frequency (0.5, 2, 5, 10 and 20 Hz). Error bars represent s.e.m.

Mechano-sensory cells in leech are directly linked to mechanical transduction at the skin surface, as quenching synaptic transmission does not suppress mechano-sensory signaling in these neurons (Burgin and Szczupak, 2003 Nicholls and Baylor, 1968). Thus, mechano-sensory transduction is supposed to drive N, T and P cells directly. Assuming that voltage signaling at the periphery and the soma differs only based on compartment size, but not on active membrane properties, our observed excitability profiles allow insights into the sensory input-output transformation. The low-, band- and high-pass filter characteristics would therefore contribute to the distribution of mechano-sensory information into a central neuronal filter bank in leech. Such central filtering might be suited to segregate strong sensory stimulations that might evoke activity in each mechano-sensory cell type, especially the N and P cells.

We describe a difference in the intrinsic frequency-dependent excitability profile of mechano-sensory cells. In general, ionic conductance (Ratte et al., 2013) and cell morphologies (Mainen and Sejnowski, 1996) influence the characteristic intrinsic response patterns of neurons (Franzen et al., 2015). Besides a different morphology, the influence of which remains here enigmatic, mechano-sensory neurons in leech show differences in the strength of expressed ionic conductances. P cells have the strongest hyperpolarization activated conductance (Gerard et al., 2012), and their delayed potassium rectifier gates faster compared to N cells (Stewart et al., 1989). Both findings support the faster voltage signaling in P cells compared to N cells that we describe. N cells show the strongest and slowest action potential after-hyperpolarization of mechano-sensory neurons (Schlue, 1976a), which possibly quenches firing at high stimulation rates, thus turning these neurons into a low-pass filter. Conversely, the P cell high-pass characteristic might be enabled by a fast-delayed rectifier paired with a hyperpolarization-activated current and a small and shorter after-hyperpolarization. For P cells, additionally an inactivation of the somatic action potential generator by slow evolving depolarizations has to be postulated to suppress activation at low input frequencies. This difference in action potential generation might be supported by the distinct sodium expression profiles in the mechano-sensory neurons in leech (Blackshaw et al., 2003). However, the detailed differences in ionic conductances and their local expression profile generating the differences in the described intrinsic response profiles between N, T and P cells remain so far unclear.

In conclusion, our study shows that the intrinsic response patterns of somata of mechano-sensory neurons in leech represent an intrinsic filter bank in respect to the ideal input frequency. These different characteristic response patterns might be suited to support segregation of overlapping sensory information centrally, possibly in conjunction with the different stimulus sensitivities and central connectivities. How these intrinsic features interact with actual sensory stimulation of different frequencies at the periphery remains to be shown.


Discussion

We found that changes in statistics of speed of visual stimuli caused a large-scale reorganization of spatiotemporal contrast sensitivity. Monotonic changes in stimulus speed induced changes of sensitivity that were highly nonmonotonic across speed, forming distinct regions of gains and losses of sensitivity summarized by the change maps in Fig. 5.

The results were unlikely to arise from changes in attentional or decision strategies by our observers. Had the observers registered the changes in stimulation and dedicated more attention to the more likely stimuli (26), or had they altered their decision biases in accord with stimulation (27, 28), changes of performance would form a pattern portrayed in Fig. 2B. In the high-speed context the sensitivity would increase for high speeds and decrease for low speeds (Fig. 2B), in comparison with the sensitivity in the low-speed context. Instead, the changes of sensitivity formed clusters of gains and losses inconsistent with this stimulus account of adaptation.

The observed changes of sensitivity were consistent with the system account of adaptation (Fig. 2C) in which gains and losses of sensitivity were expected within high speeds and within low speeds. The system account rests on a theory concerned with allocation of limited neural resources in the visual system (24). According to the theory, the spatiotemporal contrast sensitivity function (Fig. 1) reflects an optimal allocation of neurons characterized by receptive fields of different sizes. The allocation optimizes sensory performance with respect to the entire ensemble of potential stimuli. Changes in stimulation are therefore expected to cause changes in characteristics of neurons sensitive to a wide range of stimuli, manifested in a large-scale transformation of the sensitivity function.

For two reasons, the present results are likely to generalize to other stimuli and tasks. First, the differences in visual performance across stimuli revealed by the spatiotemporally contrast sensitivity function were found to generalize to many stimuli and tasks (29, 30). Second, predictions of the theory of optimal resource allocation (24) are not confined to contrast sensitivity as a measure of visual performance.

What mechanisms are likely to control the efficient allocation of receptive fields? Studies of cortical neurons selective for moving stimuli have shown that, just as in the somatosensory homunculus (Fig. 1A), the number of neurons selective for a stimulus correlates with sensitivity to that stimulus. For example, the number of neurons selective for spatial and temporal frequencies of luminance modulation correlates with the contrast sensitivity at those spatial frequencies (5, 31, 32) (Fig. 1B). Therefore, it is likely that effects of adaptation observed in the present study are mediated by changing preferences of many neurons, across a broad range of preferred stimuli, and from different cortical areas where neuronal receptive fields have different sizes (33).

Previous studies of the neural mechanisms of motion adaptation support this expectation. For example, motion-sensitive neurons in cortical visual area middle temporal (MT) of behaving macaque monkeys were found to change their speed selectivity and sensitivity after a short exposure to moving visual patterns (23). These changes occurred in neurons selective for both the adapting conditions and conditions very different from the adapting. Speed sensitivity increased for some stimuli but decreased for others. Similar neuronal changes were found in other visual submodalities (34) and other sensory modalities (35).

Our behavioral results suggest that such neuronal changes should form a lawful pattern across the space of stimulus parameters, as in the change maps displayed in Fig. 5. Previous physiological studies did not allow one to test such predictions because the range of stimuli used was too narrow, or the stimuli were broadband (they had very broad representations in the frequency domain). Future studies can pursue this issue by assaying adaptation-induced changes of sensitivity in neurons with very different stimulus preferences, using stimuli with well-defined frequency content, such as luminance gratings or grating mixtures (36).

Studies of the relationship of single sensory neurons and sensory behavior have often concentrated on how signals from multiple neurons with similar preferences are combined to optimize behavior (37 ⇓ –39). Our results suggest that this question must be approached from a broader perspective, asking how multiple neurons with very different stimulus preferences are allocated to stimuli. Whether an individual neuron ought to increase or decrease its sensitivity to particular stimulus depends on that neuron’s context—i.e., where its selectivity is located in the space of stimulus parameters relative to the preferences of other neurons, and against the entire distribution of system’s sensitivity.

The theme of efficient allocation of neural resources has been long pursued in research of visual attention (40, 41). Numerous studies showed that allocation of attention in the visual system is mediated by rapid changes in preference (gain and selectivity) of individual neurons (42 ⇓ ⇓ –45). For example, motion-sensitive neurons in cortical visual area MT in macaque monkeys shifted their spatial preferences toward the attended stimulus location, and spatial preferences of many neurons at the attended location sharpened (46). Adaptation, too, was found to induce changes of neuronal preferences in this cortical area (23, 47). It is plausible that effects of adaptation and attention are mediated by the same neural circuits, even though the two processes are presumably governed by different constraints (48, 49). Adaptation and attention can therefore manifest two strategies that nervous systems deploy over different temporal scales to achieve their unmatched versatility with limited resources.


Vibration reception

Adaptation and recovery occur most rapidly among touch receptors, and they tend to respond well to repeated stimulation, even of relatively high frequency. Thus, a person can feel whether an object is vibrating above a threshold frequency of about 15 cycles per second (cps), discretely perceived tactual stimuli seem to fuse into a quite new and distinct vibratory sensation. The upper frequency limit of this vibration sense is found at several thousand cps among normal individuals, with sensitivity being maximal in the range of 200 cps (above a threshold amplitude of about 100 millimicrons). Just as pitch is discriminated in hearing, differences of about 12 to 15 percent in vibration frequencies can be distinguished by most people.

Vibration sensitivity is not limited to humans. Fish, for instance, also may respond to low-frequency water vibrations with tactile receptors. In addition, several kinds of animals have special vibration receptors. In some insects, a group of specialized structures (chordotonal sensilla) in the upper part of each tibial segment of the leg signal vibrations from the ground below. In the cockroach, the threshold amplitude for vibrational stimuli of this kind has been found to be less than 0.1 millimicron. Birds have special receptors (corpuscles of Herbst in the tibiotarsal bone of the leg) with which they can detect slight vibrations of the twig or branch on which they sit. Maximal sensitivity in birds is at about 800 cps, and the threshold amplitude is close to 20 millimicrons. Spiders also use their vibration sense to locate prey in the web.


Hair cell physiology

Resting potentials

Compared to neurons in the central nervous system, the resting membrane potentials of hair cells are moderately depolarized, lying in the range of −70 to −50 mV. On the basolateral side of the epithelium, hair cells are exposed to the typical extracellular fluid of the animal it has a high sodium concentration (120–150 mM), a low potassium concentration (2–4 mM), and a similarly low calcium concentration (2–5 mM). The primary anion in the extracellular fluid is chloride (80–110 mM). The hair cell cytosol has a low concentration of sodium (10–15 mM), a high concentration of potassium (125–140 mM), and a negligible concentration of free calcium (≪1 μM) ( Bosher and Warren, 1978 Sauer et al. 1999). The intracellular chloride concentration is also low (1.5–4 mM) and intracellular charge balance is due mainly to the presence of the fixed negative charges which are present on free amino acids and macromolecules (proteins and nucleic acids) at physiological pH. Assuming an intracellular K + concentration of 130 m M and an extracellular K + concentration of 3 mM, such a cell would have a resting membrane potential of about −98 mV if it behaved as a perfectly K + -selective Nernstian membrane (where the Nernst equilibrium potential = 60*log10<[K + ]out/[K + ]in> in mV at 20°C). That the resting potential is so far depolarized from the K + equilibrium potential suggests the presence of a resting permeability to one or more additional ions which have a more depolarized equilibrium potential. Crawford and Fettiplace observed an average resting potential of about −50 mV in auditory hair cells and estimated the K + equilibrium potential to be −80 mV for those cells ( Crawford and Fettiplace 1980). Studies of frog saccular hair cells indicate two opposing ion currents at rest, one an inward rectifier K + current (IK1) and the other a hyperpolarization-activated inward current (Ih) ( Holt and Eatock 1995). The K + current was activated at potentials negative to −60 mV and the inward current was activated at potentials negative to −50. Saccular hair cells fell into two morphological populations, spherical and cylindrical, which also differed in their resting potentials. Spherical cells had more depolarized potentials (ca. −50 mV) and lacked IK1 while possessing Ih. Cylindrical cells had more negative resting potentials (ca. −68 mV) and possessed both IK1 and Ih ( Holt and Eatock 1995). The hyperpolarization-activated inward current Ih is a member of the HCN channel family and a recent report suggests that this type of channel is necessary for normal vestibular function ( Horwitz et al. 2011). In mouse cochlear hair cells, which have a resting potential around −72 mV, voltage-sensitive K + channels of KCNQ type contribute strongly to the resting potential ( Oliver et al. 2003). Vestibular neurons express a variety of voltage-gated, inward rectifier, and Ca 2+ -activated channels, which contribute to the functional specialization of type I and type II hair cells ( Meredith and Rennie 2016).

On the apical side of the hair cell membrane, the ionic composition of extracellular fluid in the inner ear more closely resembles the cytosol ( Bosher and Warren 1978 Sauer et al. 1999). This fluid, called endolymph, is actively secreted by the stria vascularis of the scala media of the mammalian cochlea. The scala media is a fluid-filled duct which is continuous with the semicircular canals and with the fluid space that overlies hair cells in the other macular epithelia, such as the mammalian saccule and utricle. In those regions, an epithelium similar to the stria vascularis secretes endolymph ( Wilms et al. 2016). The K + concentration of cochlear endolymph in mammals is about 150 mM and the concentration of Na + is about 1 mM the Ca 2+ concentration is about 30 μM, which is about 1% of the normal extracellular value but at the same time much higher than the normal intracellular value, which is less than 1 μM ( Fettiplace 2017). Because the K + concentrations are similar on both sides of the apical membrane, the Nernst potential for K + across that membrane is close to zero (EK = 60*log10<[K + ]out/[K + ]in>. Consequently, if the resting membrane potential is −50 mV (due to resting permeabilities on the basolateral side), there exists an inward driving force for K + to enter the cell on the apical side. Because of the low Na + concentration in endolymph, the driving force for Na + is actually outward across the apical membrane (ENa = 60*log10(1/10)=-60 mV), while the driving force for Ca 2+ remains inward [ECa=(60/2)*log10(30 μ M /1 μ M) = +44 mV]. In mammals and in some birds, there is a significant positive electrical potential between endolymph and the basolateral extracellular fluid, as a result of the active ion transport that takes place in the endolymph-secreting epithelium. In mammals, this potential is about +80 mV in the cochlea and therefore adds to the inward driving force for cations across the hair cell apical membrane ( Zdebik et al. 2009 Wilms et al. 2016). By adding to the driving force for the inward flow of cationic current, this is thought to enhance the response of hair cells to high-frequency tones ( Nin et al. 2008, 2016 Fettiplace 2017).

Mechanotransduction

The appropriate mechanical stimulus for a hair cell is one that displaces the stereocilia bundle in the direction of the tallest stereocilia. This was demonstrated by Hudspeth and Corey using an isolated bullfrog saccular epithelium preparation ( Hudspeth and Corey 1977). Minute displacements of the hair bundle produced depolarizing receptor potentials of several millivolts, and displacement in the opposite direction caused a hyperpolarization, though it was smaller in amplitude. Similar experiments on mammalian outer hair cells indicate that a 250 nm deflection of the hair bundle generates a half-maximal activation of the inward current and a 500 nm deflection is saturating ( Fettiplace and Kim 2014). The hyperpolarizing response observed in bullfrog saccular hair cells indicates that some of the mechanosensitive channels must be open at rest, but they can only a small fraction of the total number since the cells were much more responsive in the depolarizing direction ( Hudspeth and Corey 1977).

The ionic current activated by hair bundle displacement is carried by cations, and the channel is permeable to Na + , K + , and Ca 2+ ( Corey and Hudspeth 1979). Potasssium ions carry most of the current, because they are the most abundant cation in the endolymph however, Ca 2+ may contribute up to 10% of the inward current despite its low concentration (20–30 μM in cochlear endolymph, 200–250 μM in vestibular endolymph) ( Ricci and Fettiplace 1998). As noted above, current carried by Na + will be in the outward direction this would not contribute to the depolarizing receptor potential but might assist in Na + homeostasis by allowing outward diffusion of Na + that diffused inward on the basolateral side. Depolarization caused by inward current across the apical membrane increases the already-high conductance to K + in the basolateral membrane by opening voltage-gated K + channels these appear to include KCNQ4 channels, and mutations of the KCNQ4 gene cause a dominant form of hereditary deafness ( Kharkovets et al. 2000).

The large outward driving force for K + on the basolateral side of the hair cell means that no energy expenditure is required to restore the K + ion concentration gradient during and after the mechanotransduction process. This is a remarkable efficiency of cellular energetics and a prime example of the physiological power of epithelia. By a combination of K + diffusion through the perilymph and cell-to-cell diffusion through gap junctions connecting supporting cells to each other and to other cells, the K + that entered through the apical membrane eventually travels back to the stria vascularis to be recycled into more endolymph ( Zdebik et al. 2009 Fettiplace 2017).

Synaptic transmission

Mature hair cells do not produce action potentials, so all synaptic transmission is based on graded receptor potentials. Hair bundle displacement produces inward currents as large as 10 pA for 1 nm of displacement, which would lead to a depolarization of 1 mV for a hair cell having an input resistance of 100 MΩ ( Fettiplace and Ricci 2006). The physiological range for receptor potentials is from the resting potential to about −20 mV ( Glowatzki et al. 2008), corresponding to hair bundle displacements of up to 50 nm under physiological conditions.

Neurotransmitter release at the afferent synapse is Ca 2+ -dependent, as at other neuronal synapses, but there are several important specializations in the hair cell synapse. Structurally, the presynaptic zone contains a prominent, oblong, electron-dense structure around which synaptic vesicles are clustered this leads it to be termed a ribbon synapse ( Fig. 1), and the appearance in transmission electron micrographs is similar to that seen in retinal photoreceptors and bipolar cells ( Wichmann and Moser 2015). The release of neurotransmitter is graded in relation to membrane depolarization and the relationship of transmitter release to Ca 2+ influx is linear ( Glowatzki et al. 2008 Rutherford and Pangrsic 2012 Fettiplace 2017). This is rather surprising, because the Ca 2+ -dependence of vesicular exocytosis at the ribbon synapse, when measured by photolysis of photo-sensitive Ca 2+ -buffers, seems to display the same 4th or 5th power relationship as that seen at other neuronal synapses ( Glowatzki et al. 2008 Johnson et al. 2017 Rutherford and Pangrsic 2012). However it occurs, the net effect of the observed linear relationship at the ribbon synapse is to make transmitter release more sensitive to small depolarizations, thereby enhancing the overall sensitivity of the system.

In the absence of stimulus-induced hair cell depolarization, neurotransmitter is continually released due to the tonic open state of L-type voltage-dependent Ca 2+ channels at the active zone of the presynaptic membrane at the normal resting potential ( Cho and von Gersdorff 2012). The neurotransmitter released is glutamate, and the postsynaptic receptors are fast, excitatory AMPA receptors ( Sadeghi et al. 2014 Kirk et al. 2017). The result of the tonic release of glutamate is that the primary afferent fibers are firing spontaneously in the absence of hair cell activation and increase their firing rate in proportion to the graded increase of glutamate caused by graded hair cell depolarization.

The hair cell ribbon synapse is specialized to release neurotransmitter with minimal delay this is important because many processes, such as sound source localization, require precise assessment of the interaural latency of sound arrival. How this is accomplished is not completely understood, but several key components of the ribbon synapse have been identified. Prominent among these is the protein ribeye, which forms the bulk of the ribbon structure ( Wichmann and Moser 2015). Ribeye is anchored to the active zone by the protein bassoon ( Fig. 3), and mice with bassoon mutations have impaired ribbon synapse function ( Wichmann and Moser 2015). The identity of the proteins that tether synaptic vesicles to ribeye is not known, but may involve the B domain of ribeye ( Wichmann and Moser 2015). Knockout of ribeye has recently been reported in mice, with the result that temporal precision of sound encoding was disrupted, though synaptic transmission continued ( Jean et al. 2018).

Schematic depiction of the ribbon synapse in a mature inner hair cell. Synaptic vesicles (small spheres) are clustered around the ribeye protein (large oval) and are linked to it by as-yet undescribed tethers. The bassoon protein (pedestal below ribeye) is positioned between ribeye and the presynaptic membrane and may serve as an anchor for ribeye. Voltage-sensitive Cav1.3 Ca 2+ channels (dark spots near bassoon) are clustered tightly around the ribeye-bassoon complex, minimizing the diffusion time between Ca 2+ entry and Ca 2+ -triggered exocytosis. Modified with permission from Wichmann and Moser (2015).

Schematic depiction of the ribbon synapse in a mature inner hair cell. Synaptic vesicles (small spheres) are clustered around the ribeye protein (large oval) and are linked to it by as-yet undescribed tethers. The bassoon protein (pedestal below ribeye) is positioned between ribeye and the presynaptic membrane and may serve as an anchor for ribeye. Voltage-sensitive Cav1.3 Ca 2+ channels (dark spots near bassoon) are clustered tightly around the ribeye-bassoon complex, minimizing the diffusion time between Ca 2+ entry and Ca 2+ -triggered exocytosis. Modified with permission from Wichmann and Moser (2015).

Besides the structural differences from central synapses, there are significant functional differences in ribbon synapses: vesicle priming factors such as Munc13 are not involved, SNARE proteins do not appear to be necessary, and synaptotagmin, the Ca +2 -binding protein that triggers exocytosis at central synapses, is completely absent from hair cell ribbon synapses ( Rutherford and Pangrsic 2012 Pangrsic et al. 2012). In place of synaptotagmin, ribbon synapses contain a related protein, otoferlin, which, like synaptotagmin, has multiple C2 domains which could bind Ca 2+ and promote vesicle exocytosis ( Cho and von Gersdorff 2012 Fettiplace, 2017 Michalski et al. 2017). Otoferlin is necessary for exocytosis from hair cell ribbon synapses, as knockout of the protein in mice causes deafness, and mutation of the otoferlin gene is associated with human deafness ( Roux et al. 2006). Further evidence in support of otoferlin as a Ca 2+ sensor and promoter of vesicle fusion comes from experiments using a knock-in mouse with a modified otoferlin having lower Ca 2+ affinity in its C2 domains ( Michalski et al. 2017). These mice had normal ribbon synapse structure and presynaptic Ca 2+ currents, but synaptic exocytosis was delayed and brainstem auditory responses were smaller ( Michalski et al. 2017). Otoferlin may also contribute to recruitment of vesicles to the ribbon synapse ( Michalski et al. 2017). Many questions remain regarding the hair cell ribbon synapse, and this system will certainly be a focus of productive research activity for many years to come.

Another specialized synapse is found in Type I vestibular hair cells, which are surrounded on the basolateral side by a calyx-like expansion of the primary afferent nerve ending ( Fig. 4). Both type I and type II hair cells form ribbon synapses onto the primary afferent, and it is notable that one primary afferent may form synapses with both type I and type II hair cells ( Eatock and Lysakowski 2006). However, the presence of a calyx constrains the outward diffusion of K + from the basolateral membrane of the type I cell, leading to accumulation of K + in the synaptic cleft. While this seems disadvantageous to the hair cell, which must eliminate on the basolateral side the K + that enters through the stereocilia, it also has the potential to directly depolarize the primary afferent membrane by the resulting charge accumulation and/or by Nernstian depolarization, producing thereby a direct, non-quantal excitation ( Songer and Eatock 2013). Consistent with this idea, the calyceal afferent membrane contains voltage-sensitive KCNQ-type channels, providing both a Nernstian permeability pathway and an exit channel from the synaptic cleft for accumulated K + ( Songer and Eatock 2013).

Diagram of stereotypical type I and type II hair cells from a mammalian vestibular organ. (A) Note that a single primary afferent may synapse with both a type I and a type II hair cell. A single efferent neuron may synapse onto a type I afferent ending (a postsynaptic synapse) and may synapse onto a type II hair cell (a presynaptic synapse) or onto the afferent axon (a postsynaptic synapse, not illustrated). But an efferent neuron cannot synapse onto a type I hair cell directly. (B) Ionic conductances that have been identified in vestibular hair cells. GDR, delayed rectifier K + conductance GK, L, K + leak conductance GCa, voltage-sensitive, noninactivating Ca 2+ conductance GNa, TTX-sensitive, voltage-sensitive Na + conductance GKI, K + - selective inward rectifier Gh, hyperpolarization-activated inward current GA, voltage-sensitive, rapidly inactivating K + conductance, or A-current GDRI, delayed rectifier K + conductance GDRII, delayed rectifier K + conductance GMET, mechanoelectrical transduction conductance. Reproduced with permission from Eatock et al. (1998).

Diagram of stereotypical type I and type II hair cells from a mammalian vestibular organ. (A) Note that a single primary afferent may synapse with both a type I and a type II hair cell. A single efferent neuron may synapse onto a type I afferent ending (a postsynaptic synapse) and may synapse onto a type II hair cell (a presynaptic synapse) or onto the afferent axon (a postsynaptic synapse, not illustrated). But an efferent neuron cannot synapse onto a type I hair cell directly. (B) Ionic conductances that have been identified in vestibular hair cells. GDR, delayed rectifier K + conductance GK, L, K + leak conductance GCa, voltage-sensitive, noninactivating Ca 2+ conductance GNa, TTX-sensitive, voltage-sensitive Na + conductance GKI, K + - selective inward rectifier Gh, hyperpolarization-activated inward current GA, voltage-sensitive, rapidly inactivating K + conductance, or A-current GDRI, delayed rectifier K + conductance GDRII, delayed rectifier K + conductance GMET, mechanoelectrical transduction conductance. Reproduced with permission from Eatock et al. (1998).

The mechanotransducer channel

Although the mechanotransducer channel has not yet been fully isolated and characterized, much is known about it. Early experiments used low extracellular Ca 2+ (less than 1 μM) to cause partial separation of the stereocilia tip links. Under fortunate conditions, this leaves enough intact tip links to allow electrical recording from one or a few channels at a time, and thus measurement of the channel conductance. When the extracellular Ca 2+ concentration was subsequently raised to about 3 mM, the channel conductance measured was on the order of 100 picosiemens (100 pS) ( Ohmori 1985 Crawford et al. 1991 Beurg et al. 2006). In contrast, most voltage-gated ion channels have conductances in the range of 10–30 pS ( Hille 2001). Even more surprising, the conductance of the mechano-sensitive channel nearly doubled when the extracellular Ca 2+ was lowered to its normal (sub-millimolar) endolymph concentration ( Crawford et al. 1991). The increased conductance in low Ca 2+ suggests that Ca 2+ has an inhibitory effect on the channel ( Beurg et al. 2010).

The mechanosensitive ion channel was conclusively localized to the tips of stereocilia by calcium imaging with fluorescent dyes ( Lumpkin and Hudspeth 1995 Beurg et al. 2009). This result is consistent with the presence of tip links between adjacent stereocilia, which provide a possible mechanism for the activation of tip-located mechanoreceptors: they could act as a gating spring. Several components of the tip links have been identified, and among them two essential proteins are cadherin23 (CDH23) and protocadherin 15 (PCDH15) ( Siemens et al. 2004 Narui and Sotomayor 2018). Both CDH23 and PCDH15 are calcium-binding proteins that form calcium-dependent links, so their presence is consistent with the earlier observation that low extracellular Ca 2+ disrupts tip links and blocks mechanotransduction ( Assad et al. 1991).

It is now well-established that CDH23 forms the upper region of the tip link and PCDH15 forms the lower ( Zhao and Müller 2015). Subsequent searches, based on genes responsible for a severe deafness syndrome (Usher Syndrome Type 1, or USH1), have led to the description of proteins that engage with CDH23 or PCDH15 and could possibly form the mechanotransducer channel. These searches were productive and yielded important components of the stereocilia tip-link complex, including harmonin, sans, and myosin VIIa, which form a complex at the upper end of the tip-link and interact with CDH23. Also identified were CIB2, TMIE, and LHFPL5 (also known as TMHS), which form a complex at the lower end of the tip link and interact with PCDH15 ( Zhao and Müller 2015). Both TMIE and LHFPL5 are integral membrane proteins and could possibly contribute to a transmembrane ion channel. However, experiments with a mouse knock-out of LHFPL5, while showing reduction of the mechanotransduction conductance, indicated not that LHFPL5 was a component of the mechanotransducer channel, but rather that disruption of LHFPL5 acts by downregulating expression of another potential channel protein, TMC1 ( Beurg et al. 2015).

Multiple lines of evidence suggest that TMC1 is part of the mechanotransducer channel ( Holt et al. 2014). Like TMIE and LHFPL5, TMC1 is localized to the stereocilia tips ( Kurima et al. 2015). Also, TMC1 interacts with CIB2, which itself is necessary for normal mechanotransduction ( Giese et al. 2017). Experiments with mice expressing TMC1, the related protein TMC2, or a mutated TMC1 showed that the votage-clamp recording from inner hair cells or vestibular hair cells yielded normal-appearing currents with either TMC1, TMC2, or a mixture of the two, while the mice expressing mutated TMC1 only showed reduced currents ( Pan et al. 2013). These data do not prove that TMC1 or TMC2 can form a complete channel, but they strongly suggest that TMC proteins contribute to the channel pore ( Pan and Holt 2015). However, this does not necessarily imply that they are the only components of the channel pore. Many questions remain and the issue is unlikely to be fully resolved until a functional mechanotransducer channel can be reconstituted in a heterologous expression system ( Fettiplace 2016 Corey and Holt 2016 Wu and Müller 2016).

Reverse-polarity transduction

In hair cells lacking both TMC1 and TMC2, or in hair cells in which the tip links have been thoroughly disrupted, it is possible to observe a reverse-polarity current when the hair bundle is displaced in the negative direction (away from the tallest stereocilia). This outward current results from deformation of the apical plasma membrane of the cell ( Beurg et al. 2016). Subsequent investigation determined that the mechanosensitive channel responsible is piezo2, which has previously been shown to underlie mechanotransduction in mammalian touch sensation ( Ranade et al. 2014 Beurg and Fettiplace 2017). However, the normal role of this channel in the apical membrane of hair cells is not known.

Adaptation

All sensory systems exhibit adaptation, which is a decline in sensory response to a steady or unchanging stimulus. Adaptation may occur at any level of the system, from peripheral receptor to central integrating neural circuit, and it often occurs at multiple levels. From the organism’s point of view, adaptation allows the sensory system to remain sensitive to changes or differences in the environment, as those are the locus of information that is useful for survival and reproduction. Like many other mechanosensory cells, hair cells show rapid adaptation to tonic displacement of the stereocilia bundle. This is functionally important in the vestibular system because it allows hair cells to sense changes of acceleration against a background of constant velocity. Additionally, in auditory hair cells of amphibians, reptiles, and birds, adaptation contributes to frequency sensitivity. Two general types of hair cell adaptation have been described: fast and slow. Both were originally described in amphibians (frog saccule) or reptiles (turtle auditory papilla), and both types depend on Ca 2+ influx through the mechanosensitive channel ( Assad et al. 1989 Wu et al. 1999 Eatock 2000 Colclasure and Holt 2003 Corns et al. 2014)

Fast adaptation occurs very rapidly, with a time constant of <2 ms, and it includes a rapic, active movement of the stereocilia against the direction of the stimulus (i.e., toward the shorter stereocilia) ( Crawford et al. 1989). The recoil has the same time course as the decay of inward current following stereocilia displacement, and the most likely mechanism is that entering Ca 2+ binds to the mechanotransducer channel complex and causes rapid channel closing ( Crawford et al. 1989 Ricci et al. 2000a Fettiplace and Ricci 2003 Stepanyan and Frolenkov 2009). Consistent with this idea, turtle auditory hair cells contain a high concentration of endogenous Ca +2 buffer, equivalent to 0.1–0.4 mM BAPTA, and the concentration of endogenous buffer is higher in high-frequency responding hair cells than in low-frequency cells. In parallel, the time constant of fast adaptation is longer in high-frequency cells than in low-frequency cells ( Ricci et al. 1998).

Fast adaptation has also been observed in outer hair cells of the mammalian cochlea. The kinetics of fast adaptation were more rapid than in turtle hair cells, even when both were at room temperature, with the average time constant for fast adaptation in mouse cells around 154 μs when the extracellular Ca 2+ concentration was 1.5 mM. When extracellular Ca 2 was reduced to 50 μM, a physiological concentration, the time constant was slower (620 μs) and the total inward current was greater ( Kennedy et al. 2003). These differences reflect the fact that the mechanotransducer channel is blocked by millimolar Ca 2+ , which was also observed in turtle hair cells ( Crawford et al. 1991). Similar results were obtained by Corns and colleagues when using millimolar Ca 2+ , but when Ca 2+ was lowered to a physiological concentration, both fast and slow adaptation were abolished ( Corns et al. 2014). It has also been reported that fast adaptation can occur independent of Ca 2+ entry in mammalian cochlear hair cells ( Peng et al. 2013).

Slow adaptation occurs with a longer time constant (5–50 ms). This adaptation involves a passive adjustment of the hair bundle in the direction of the excitatory displacement (i.e., toward the longer stereocilia). In the process, the channels become reset to respond to further displacement, and this is thought to involve adjustment of the resting tension on the channels. One model for this is that Ca 2+ binds to myosin and causes its detachment from an actin filament in the stereocilium. The myosin is hypothesized to be attached to an elastic element, or “gating spring,” which is also attached to the channel, so that detachment of the myosin from actin would allow the complex to slip in the direction that reduces tension on the gating spring. Following channel closure, intracellular Ca 2+ returns to normal and the myosin could again bind to actin and migrate in the opposite (tension-increasing) direction. Once tension reaches the point for channel opening, Ca 2 influx would again act to reduce the tension. In this way, a negative feedback loop would keep the channels optimally positioned for sensitivity ( Hudspeth et al. 2000 Farris et al. 2006). Recent evidence in support of this model is that directed mutation of the myosin1c gene, combined with an ADP analog that interferes with the mutated myosin, selectively blocks slow adaptation in the transgenic mice carrying the mutation ( Holt et al. 2002). It has also been reported that myosin VIIa is necessary for slow adaptation in mouse cochlear hair cells ( Kros et al. 2002). Myosin VI has also been implicated in adaptation ( Marcotti et al. 2016).

How do auditory hair cells distinguish tones?

Through hair cells, the auditory system encodes sound intensity (loudness) and sound frequency (tone). In contrast to the lateral line and vestibular systems, in which water or endolymph translates in the plane of the epithelium, auditory transduction involves vertical displacements of the hair cell (i.e., along the basolateral-to-apical axis) caused by sound-induced oscillation of the epithelium. It is straightforward to see how larger amplitudes of oscillation might be induced by louder sounds, and how this could create greater depolarization of the hair cell. But how does the ear distinguish tones? Two fundamentally different mechanisms have evolved for tone discrimination one which employs electrical resonance in the basolateral membrane of the hair cell, while the other makes use of tonotopic differences in the mechanical resonance of the basilar membrane. There are also tonotopic differences in the stiffness of hair bundles.

In amphibians, reptiles, and birds, tone discrimination derives primarily from resonant electrical properties of the hair cells some resonate at lower frequencies and some at higher frequencies ( Art et al. 1995 Art and Fettiplace 1987 Fuchs et al. 1988 Smotherman and Narins 1999a, 1999b). The physiological mechanism for this resonance involves an intriguing interplay of voltage-activated Ca 2+ channels and Ca 2+ -activated K + channels. This was initially described by Fettiplace and colleagues ( Crawford and Fettiplace 1981 Art and Fettiplace 1987, 2006 Goodman and Art 1996). The electrical resonance manifests as a damped oscillation of membrane potential in responses to an applied current pulse, or a sustained oscillation of membrane potential in response to pure tone stimulation of the intact cochlea. The resonant response to pure tones is not an all-or-none phenomenon each cell displays a tuning curve with maximal resonance at a characteristic frequency and a graded decline with frequencies on either side ( Art et al. 1985). What is remarkable is that the same characteristic frequency can be seen in the isolated hair cell: it is an intrinsic property of the cell membrane.

While differences in the density and kinetics of channels bearing inward or outward current could influence a resonant feedback loop, the experimental evidence indicates that most of the differences lie in the density and kinetics of BK potassium channels, which are sensitive to both voltage and Ca 2+ ( Art and Fettiplace 1987 Art et al. 1995). Cells with a lower resonant frequency have a lower density of BK channels and those channels are composed of subunits that result in slower activation of the channel. Thus, the membrane response to both depolarization and Ca 2+ influx are slowed, resulting in a lower resonant frequency. Cells which have a higher density of BK channels, and in which the channel subunits yield faster kinetics, repolarize more rapidly after an initial depolarization and Ca 2+ influx. Buffering of the cytosolic Ca 2+ then allows the BK channels to close, causing a rebound depolarization and Ca 2+ channel opening. In the presence of tones that match the resonant frequency range of the cell, this would yield enhanced receptor potentials compared to a cell lacking resonance ( Wu et al. 1995).

In addition to membrane electrical resonance of individual cells, there is an overall tonotopic organization in the basilar (auditory) papilla of reptiles such as turtles (from which many of these studies are drawn). Hair cells with high-frequency resonance reside at the basal end and cells with progressively lower-frequency resonance lie toward the apical end of the epithelium. This is similar to the the organization of the mammalian cochlea, and raises the question of whether the basilar papilla membrane itself has mechanical resonance properties like the basilar membrane in the mammalian organ of Corti ( Ricci et al. 2000). If that were the case, the electrical resonance of the hair cells could be viewed as increasing the ear’s existing sensitivity to specific tones, effectively acting as a gain amplifier. The question was explored in the turtle ear by laser interferometry, the result that the turtle basilar membrane appears to be broadly tuned and does not display intrinsic tonotopic resonance differences ( O’Neill and Bearden 1995). Similar results have been reported from the chick cochlea ( Xia et al. 2016). These results are consistent with earlier experiments in alligator lizards which used the Mossbauer source method ( Peake and Ling 1980).

While the basilar membrane is not tonotopically tuned in non-mammalian species, there are differences in the morphology of stereocilia along the length of the basilar papilla, one of which is stereocilia length ( Howard and Ashmore 1986 Fettiplace and Fuchs 1999 Fettiplace 2017). These differences affect the passive compliance of the stereocilia, to which the opening and closing of the mechanosensitivie channels contribute an active component (i.e., greater compliance when the channels open and reduced compliance when the channels close). The features contribute mechanical tuning to hair cells that adds to their electrical tuning ( Fettiplace and Ricci 2006).

Besides enhancing frequency discrimination, membrane electrical resonance can also amplify hair cell responses by inducing oscillatory movements of the hair bundle ( Martin and Hudspeth 1999 Ricci et al. 2000). Consistent with this, short hair cells of the bird auditory papilla display electrical tuning although they have no afferent function ( Tan et al. 2013). The time constant of fast adaptation is related to the resonant frequency, and the active movement of the hair bundle caused by fast adaptation could generate enough force to enhance displacement of hair bundles in long hair cells (the bird analog of inner hair cells) ( LeMasurier and Gillespie 2005 Tan et al. 2013).

It is likely that electrical resonance first evolved in vestibular hair cells and was further refined in the tetrapod evolution of auditory papillae. In that light it is worth noting that electrical resonance properties are also found in the vestibular neurons of mammals, where they contribute importantly to rapid perception of head movements (rotational or translational) at physiological frequencies ( Vollrath and Eatock 2003 Eatock and Lysakowski 2006 Fisher et al. 2011 Songer and Eatock 2013 Venturino et al. 2015).

Auditory hair cells also have structural features that vary tonotopically along the length of the basilar membrane in the mammalian cochlea. Chief among these is the length of the stereocilia. Hair cells toward the apex, which are most sensitive to low frequencies, have longer stereocilia, and hair cells toward the basal end, sensitive to high frequencies, have shorter stereocilia. In rat cochlea, inner hair cells toward to apex with an expected best frequency of 4 kHz had stereocilia that were twice as long as those in hair cells with an expected best frequency of 30 kHz ( Furness et al. 2008). These differences are likely to enhance frequency sensitivity, as the stiffness of stereocilia is inversely proportional to the square of their length in frog sacculus and turtle cochlear hair cells ( Crawford and Fettiplace 1985 Howard and Ashmore 1986).

In contrast to vestibular hair cells and non-mammalian auditory hair cells, cochlear hair cells of mammals do not display electrical resonance. In the absence of electrical resonance, mammalian inner ears have evolved two complementary mechanical features to enhance tone discrimination and sound sensitivity. The first is that the basilar membrane changes in stiffness along its length, being more stiff toward the basal end and less stiff toward the apical end. This arises from differences in the width of the basilar membrane and also differences in its structure, which is thicker at the base and thinner toward the apex. As initially observed by von Bekesy on the inner ears of human cadavers, sound entering the cochlea generates traveling waves along the length of the basilar membrane, and the amplitude of displacement is tonotopically organized the apical end resonates with lower frequencies and the basal end at higher frequencies ( von Bekesy 1960). Traveling waves of this sort have not been observed in fish, amphibians, reptiles, or birds.

The second mammalian innovation is the introduction of a second type of hair cell to amplify the basilar membrane’s oscillation at specific sound frequencies. These are the outer hair cells. The cochleas of eutherian mammals comprise one row of primary sensory hair cells (inner hair cells, IHCs) and three rows of modulatory hair cells with little or no afferent function (outer hair cells, OHCs). Each inner hair cell receives afferent synapses from 10 to 15 primary afferent nerve fibers, which amounts to 90–95% of the primary afferent fibers. The outer hair cells share synaptic connection to the remaining 5–10% of afferent axons. The upshot is that the CNS auditory system oversamples the activity of IHCs by a many-to-one mapping, while averaging the activity of OHCs by a one-to-many mapping. This arrangement indicates that IHCs are the primary mediators of auditory information.

Structurally, the OHCs have more stereocilia and the W-shaped arrangement of the stereocilia is more pronounced ( Furness and Hackney 2006). In addition, the tips of OHC stereocilia are physically embedded in the overlying tectorial membrane, while those of IHCs are not. The functional significance of this arrangement became apparent when William Brownell and colleagues discovered that OHCs could shorten along their base-to-apical axis when depolarized ( Brownell et al. 1985). This shortening is extremely rapid and can be observed in response to imposed depolarizations at frequencies up to 100 kHz, near the top end of frequency sensitivity in bats and cetaceans (whales and dolphins). Impressively, the response amplitude and response phase of OHCs display high fidelity out to 50 kHz in vitro ( Frank et al. 1999). By shortening, the OHCs increase the shearing motion of the tectorial membrane over the surface of the IHCs, thereby amplifying the displacement of the IHC stereocilia bundle.

Shortening of the OHC is accompanied by proportional increase of cell radius the cell volume remains constant. Uniquely among vertebrate cells, OHCs have a positive turgor pressure this is made possible by structural specializations of the cuticular plate at the cell’s apical pole and a highly regular, thick cytoskeletal layer lining the lateral and basolateral plasma membrane ( Brownell 2006). Shortening of the OHC under natural conditions begins with displacement of the stereocilia bundle and opening of mechanosensitive channels at their tips. Depolarization-induced shortening results from activation of a piezoelectric effect, meaning that electrical depolarization and physical shortening are directly coupled. The protein responsible for this effect has been cloned and identified and named prestin ( Dallos and Fakler 2002 Dallos et al. 2006 Dallos 2008). Prestin belongs to a family of sulfate transporters called Slc26A ( Vincourt et al. 2003). Like its family members, prestin has a large number of putative transmembrane domains predicted to form alpha helices with mostly hydrophobic amino acid residues. But unlike its cousins, prestin preferentially binds Cl − , not SO 4 2 − ⁠ , and it does not function as a transporter. Our current knowledge of prestin structure and function has been recently reviewed ( He et al. 2014). Intracellular Cl − is required for normal OHC function, consistent with a model in which Cl - ions bind to prestin and act as the voltage sensor. Chloride ions would be predicted to move away from the cell interior during hyperpolarization and toward the cell interior during depolarization. The close coupling between charge movement and cell shortening (actuation) remains unresolved. Prestin has also been reported to contribute to amplification by short hair cells in the bird auditory papilla ( Beurg et al. 2013).

One constraint of the prestin-based cochlear amplifier is that the ability of an OHC to keep pace with high frequency tones is limited by the time constant of the OHC membrane, because OHC shortening must be preceded by OHC depolarization. Since the membrane time constant is the product or membrane resistance and membrane capacitance (τ = Rm × Cm), one approach to solve this problem is to reduce Rm. This is accomplished in OHCs by increasing the concentration of endogenous intracellular Ca 2+ so that about 50% of mechanotransducer channels are open at rest, which in turn depolarizes the OHC membrane potential to about −40 mV ( Johnson et al, 2011). An additional effect reducing Rm is that, at −40 mV, voltage-dependent KCNQ4 channels on the OHC basolateral membrane are fully activated. This creates a “silent current” of K + through the OHC, which may be energetically expensive but which yields high sensitivity across the frequency spectrum ( Johnson et al. 2011 Nam and Fettiplace 2012 Fettiplace 2017).

Efferent modulation of the cochlear amplifier

Hair cells generally receive both afferent and efferent innervation. In mammals, the OHCs receive about 90% of the efferent innervation. Whether OHCs even contribute an afferent signal is an open question, as attempts to record afferent activity in response to OHC activation have yielded negative results. In bird cochlea, the short hair cells, which appear analogous to OHCs, receive only efferent innervation. So, what is the function of efferent synapses on hair cells?

The transmitter released at the efferent synapse has long been known to be acetylcholine (ACh), and the postsynaptic receptor for ACh in hair cells is of the nicotinic type, which has a ligand-gated channel. In most cells, such as skeletal muscle, the ACh-gated channel is a non-selective cation channel, permeable to Na + , K + , and Ca 2+ . The efferent synapses are on the basolateral membrane of hair cells, where the predominant extracellular cations are Na + and Ca 2+ , both of which would produce inward, depolarizing currents ( Glowatzki and Fuchs 2000 Oliver et al. 2000). Yet, the effect of efferent signaling is a hyperpolarizing, inhibitory synaptic potential ( Brown and Nuttall 1984). How does this come about?

Like other nicotinic receptors, the receptor in hair cells is a pentamer. It has five subunits but they need not be identical. Two nicotinic subunit types, alpha-9 and alpha-10, have been identified in hair cells, and when expressed together in Xenopus oocytes they form an ACh receptor that preferentially allows Ca 2+ to permeate. In fact, it is about 10 times more permeable to Ca 2+ than to Na + ( Weisstaub et al., 2002). The key to the puzzle of hyperpolarization produced by inward current is the presence of Ca 2+ -activated K + channels in close proximity to the ACh receptors. These channels belong to the SK2 family of potassium channels, which are sensitive to micromolar concentrations of Ca 2+ and insensitive to voltage. That they lie in very close proximity to the ACh channels was demonstrated by the observation that intracellular injection of BAPTA, a calcium chelator with fast kinetics, could prevent activation of the K + channels, while injection of EGTA, a calcium chelator with similar affinity but slow kinetics, could not block their activation. Once activated, these Ca 2+ -activated K + channels remain open for a long time, due to the time required to bring intracellular Ca 2+ down below the concentration that activates them ( Fuchs and Murrow 1992 Rohmann et al. 2015).

The net effect of efferent synaptic activity on OHCs is to counteract the depolarizing effect of mechanical activation, thereby inhibiting shortening and reducing their amplifying effect on IHCs. Direct experiments to demonstrate this sequence of events have remained elusive, however, due to the technical difficulty of making electrical recordings from OHCs in an intact cochlea preparation. In the future, this may be an area amenable to the use of voltage-sensitive dyes rather than microelectrode recording.

The effect of efferent modulation on IHCs is more clear-cut hyperpolarization reduces transmitter release at the afferent synapse, allowing the central auditory system to attenuate or filter out those sound frequencies. This can improve detection of specific sound frequencies against background noise (the “cocktail party” effect) and also protects the cochlea from acoustic trauma ( May and McQuone 1995 Lauer and May 2011 Fuente 2015). Developmentally, cholinergic efferents synapse onto both IHCs and OHCs prior to the onset of hearing ( Simmons 2002). Efferent synapses develop first on IHCs, where they are initially excitatory due to an absence of Ca 2+ -activated K + channels (SK channels) efferent synapses appear several days later (P6–P8) on OHCs ( Roux et al. 2011). There are also differences in the distribution of Ca 2 -activated K + channels on OHCs OHCs in the high frequency region have a higher density of large-conductance BK channels, which have faster kinetics ( Rohmann et al. 2015).

What, then, does efferent modulation contribute to hearing in non-mammalian vertebrates? The answer seems to be a combination of increased overall sensitivity to sound volume and decreased sensitivity to tone. This was initially demonstrated by stimulating efferent axons while recording the electrical responses to pure tone acoustic stimuli in hair cells of the turtle cochlea ( Art et al. 1985). This experimental paradigm allowed identification of the characteristic resonant frequency of the hair cell. Stimulation of the efferent axons strongly inhibited the hair cell response to tones near its characteristic frequency, as would be expected if the increased conductance to K + acted to lower the impedance of the resonant components (voltage-gated Ca 2+ channels, Ca 2+ - and voltage-gated K + channels). At the same time, however, the hair cell response to lower frequencies was enhanced, but in a flat, frequency-independent manner. This can be explained by an overall increase in the cell input resistance, as the hyperpolarization induced by the efferent synapse caused closure of voltage-gated K + and voltage-gated Ca 2+ channels that were open in the resting cell ( Fuchs and Parsons 2006).

In contrast to the inhibitory effects of efferent modulation in the auditory system, efferent modulation of vestibular hair cells has both excitatory and inhibitory effects ( Jordan et al. 2013). Calyx-bearing afferents (similar to type I cells) are excited by efferent fibers, which synapse postsynpatically onto the afferent nerve ending ( Holt et al. 2015). Bouton afferents (similar to type II cells) receive both presynaptic efferent innervation (directly onto the hair cell) and postsynaptic innervation onto the primary afferent. Efferent stimulation of bouton afferent cells is initially inhibitory, followed by an extended postinhibitory excitation ( Holt et al. 2006). As in the cochlea, acetylcholine is the neurotransmitter and fast synaptic effects are mediated by alpha-9 containing ACh receptors, with inhibitory effects mediated by SK Ca 2+ -activated K + channels ( Parks et al. 2017). In addition, muscarinic acetylcholine receptors mediate a slower excitation in calyx-bearing afferents, probably by inhibition of an M-current ( Holt et al. 2017).

Evolution of hair cells

Are hair cells a vertebrate innovation? Or do homologous cells exist among invertebrates? This has been an active topic of research and discussion, and has been reviewed in depth elsewhere ( Coffin et al. 2004 Burighel et al. 2011). While many invertebrates have mechanosensitive cells bearing cilia and/or microvilli, it has been difficult to identify candidate homologs to vertebrate hair cells. Mechanosensory cells on the tentacles of the sea anemone Nematostella vectensis have many interesting properties, including bundles of actin-filled stereocilia bound together by Ca + -dependent links ( Tang and Watson 2014 Menard and Watson 2017). Closer to home in Chordata, Manni and colleagues have described mechanosensitive coronal cells on the oral (incurrent) siphon of tunicates that satisfy multiple criteria for homology with vertebrate hair cells ( Caicci et al. 2007 Rigon et al. 2013). These characters include embryological development from placodes, differentiation as secondary sensory cells which synapse onto primary afferents and also receive efferent innervation, and an arrangement of microvilli or stereovilli with graded length, and one or two true cilia, usually located eccentrically ( Caicci et al. 2007).

Summary and future directions

Great progress has been made in our understanding of sensory hair cell structure and function. We have a functional understanding of cochlear amplification in mammals and electrical tuning of hair cells in non-mammals, both of which contribute strongly to frequency sensitivity. The motor protein for outer hair cell shortening has been identified, and at least part of the mechanosensitive transduction channel has also been identified. Still, many questions remain about outer hair cell function and there are many gaps in our basic knowledge of hair cell structure. Hair cells should continue to attract basic researchers for many decades to come, and will no doubt repay that curiosity with many experimental insights.


Contents

One such mechanism is the opening of ion channels in the hair cells of the cochlea in the inner ear.

Air pressure changes in the ear canal cause the vibrations of the tympanic membrane and middle ear ossicles. At the end of the ossicular chain, movement of the stapes footplate within the oval window of the cochlea, in turn, generates a pressure field within the cochlear fluids, imparting a pressure differential across the basilar membrane. A sinusoidal pressure wave results in localized vibrations of the organ of Corti: near the base for high frequencies, near the apex for low frequencies. The cochlea thus acts as an 'acoustic prism', distributing the energy of each Fourier component (which represent particular frequencies) of a complex sound at different locations along its longitudinal axis. Hair cells in the cochlea are stimulated when the basilar membrane is driven up and down by differences in the fluid pressure between the scala vestibuli and scala tympani. Because this motion is accompanied by a shearing motion between the tectorial membrane and the reticular lamina of the organ of Corti, the hair bundles that link the two are deflected, which initiates mechano-electrical transduction. When the basilar membrane is driven upward, shear between the hair cells and the tectorial membrane deflects hair bundles in the excitatory direction, toward their tall edge. At the midpoint of an oscillation the hair bundles resume their resting position. When the basilar membrane moves downward, the hair bundles are driven in the inhibitory direction.

Basilar Membrane motion causes a shearing motion between the reticular lamina and the tectorial membrane, thereby activating the mechano-sensory apparatus of the hair bundle, which in turn generates a receptor potential in the hair cells. [ citation needed ]

Thus the sound pressure wave is transduced to an electrical signal which can be processed as sound in higher parts of the auditory system. [ citation needed ]

When a deformation is imposed on a muscle, changes in cellular and molecular conformations link the mechanical forces with biochemical signals, and the close integration of mechanical signals with electrical, metabolic, and hormonal signaling may disguise the aspect of the response that is specific to the mechanical forces. [17]

One of the main mechanical functions of articular cartilage is to act as a low-friction, load-bearing surface. Due to its unique location at joint surfaces, articular cartilage experiences a range of static and dynamic forces that include shear, compression and tension. These mechanical loads are absorbed by the cartilage extracellular matrix (ECM), where they are subsequently dissipated and transmitted to chondrocytes (cartilage cells).

Chondrocytes sense and convert the mechanical signals they receive into biochemical signals, which subsequently direct and mediate both anabolic (matrix building) and catabolic (matrix degrading) processes. These processes include the synthesis of matrix proteins (type II collagen and proteoglycans), proteases, protease inhibitors, transcription factors, cytokines and growth factors. [18] [19]

The balance that is struck between anabolic and catabolic processes is strongly influenced by the type of loading that cartilage experiences. High strain rates (such as which occurs during impact loading) cause tissue damage, degradation, decreased matrix production and apoptosis. [20] [21] Decreased mechanical loading over long periods, such as during extended bed-rest, causes a loss of matrix production. [22] Static loads have been shown to be detrimental to biosynthesis [23] while oscillatory loads at low frequencies (similar that of a normal walking gait) have been shown to be beneficial in maintaining health and increasing matrix synthesis. [24] Due to the complexity of in-vivo loading conditions and the interplay of other mechanical and biochemical factors, the question of what an optimal loading regimen may be or whether one exists remain unanswered.

Although studies have shown that, like most biological tissues, cartilage is capable of mechanotransduction, the precise mechanisms by which this is done remain unknown. However, there exist a few hypotheses which begin with the identification of mechanoreceptors. [ citation needed ]

In order for mechanical signals to be sensed, there need to be mechanoreceptors on the surface of chondrocytes. Candidates for chondrocyte mechanoreceptors include stretch-activated ion channels (SAC), [25] the hyaluronan receptor CD44, annexin V (a collagen type II receptor), [26] and integrin receptors (of which there exist several types on chondrocytes).

Using the integrin-linked mechanotransduction pathway as an example (being one of the better studied pathways), it has been shown to mediate chondrocyte adhesion to cartilage surfaces, [27] mediate survival signaling [28] and regulate matrix production and degradation. [29]

Integrin receptors have an extracellular domain that binds to the ECM proteins (collagen, fibronectin, laminin, vitronectin and osteopontin), and a cytoplasmic domain that interacts with intracellular signaling molecules. When an integrin receptor binds to its ECM ligand and is activated, additional integrins cluster around the activated site. In addition, kinases (e.g., focal adhesion kinase, FAK) and adapter proteins (e.g., paxillin, Pax, talin, Tal and Shc) are recruited to this cluster, which is called the focal adhesion complex (FAC). The activation of these FAC molecules in turn, triggers downstream events that up-regulate and /or down-regulate intracellular processes such as transcription factor activation and gene regulation resulting in apoptosis or differentiation. [ citation needed ]

In addition to binding to ECM ligands, integrins are also receptive to autocrine and paracrine signals such as growth factors in the TGF-beta family. Chondrocytes have been shown to secrete TGF-b, and upregulate TGF-b receptors in response to mechanical stimulation this secretion may be a mechanism for autocrine signal amplification within the tissue. [30]

Integrin signaling is just one example of multiple pathways that are activated when cartilage is loaded. Some intracellular processes that have been observed to occur within these pathways include phosphorylation of ERK1/2, p38 MAPK, and SAPK/ERK kinase-1 (SEK-1) of the JNK pathway [31] as well as changes in cAMP levels, actin re-organization and changes in the expression of genes which regulate cartilage ECM content. [32]

More recent studies have hypothesized that chondrocyte primary cilium act as a mechanoreceptor for the cell, transducing forces from the extracellular matrix into the cell. Each chondrocyte has one cilium and it is hypothesized to transmit mechanical signals by way of bending in response to ECM loading. Integrins have been identified on the upper shaft of the cilium, acting as anchors to the collagen matrix around it. [33] Recent studies published by Wann et al. in FASEB Journal have demonstrated for the first time that primary cilia are required for chondrocyte mechanotransduction. Chondrocytes derived from IFT88 mutant mice did not express primary cilia and did not show the characteristic mechanosensitive up regulation of proteoglycan synthesis seen in wild type cells [34]

It is important to examine the mechanotransduction pathways in chondrocytes since mechanical loading conditions which represent an excessive or injurious response upregulates synthetic activity and increases catabolic signalling cascades involving mediators such as NO and MMPs. In addition, studies by Chowdhury TT and Agarwal S have shown that mechanical loading which represents physiological loading conditions will block the production of catabolic mediators (iNOS, COX-2, NO, PGE2) induced by inflammatory cytokines (IL-1) and restore anabolic activities. Thus an improved understanding of the interplay of biomechanics and cell signalling will help to develop therapeutic methods for blocking catabolic components of the mechanotransduction pathway. A better understanding of the optimal levels of in vivo mechanical forces are therefore necessary for maintaining the health and viability of cartilage, preventative techniques may be devised for the prevention of cartilage degradation and disease. [ citation needed ]


Watch the video: Sensory Adaptation (January 2023).