Fast intensity adaptation enhances the encoding of sound in Drosophila

Spike frequency adaptation in primate lateral prefrontal cortex neurons results from interplay between intrinsic properties and circuit dynamics

Cortical neurons in brain slices display intrinsic spike frequency adaptation (I-SFA) to constant current inputs, while extracellular recordings show extrinsic SFA (E-SFA) during sustained visual stimulation. Inferring how I-SFA contributes to E-SFA during behavior is challenging due to the isolated nature of slice recordings. To address this, we recorded macaque lateral prefrontal cortex (LPFC) neurons in vivo during a visually guided saccade task and in vitro in brain slices. Broad-spiking (BS) putative pyramidal cells and narrow-spiking (NS) putative inhibitory interneurons exhibit both E-SFA and I-SFA. Developing a data-driven hybrid circuit model comprising NS model neurons receiving BS input reveals that NS model neurons exhibit longer SFA than observed in vivo; however, adding feedforward inhibition corrects this in a manner dependent on I-SFA. Identification of this circuit motif shaping E-SFA in LPFC highlights the roles of both intrinsic and network mechanisms in neural activity underlying behavior.

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

The Secret of Nanoarrays toward Efficient Electrochemical Water Splitting: A Vision of Self-Dynamic Electrolyte

Nanoarray electrocatalysts with unique advantage of facilitating gas bubble detachment have garnered significant interest in gas evolution reactions (GERs). Existing research is largely based on a static hypothesis, assuming that buoyancy is the only driving force for the release of bubbles during GERs. However, this hypothesis overlooks the effect of the self-dynamic electrolyte flow, which is induced by the release of mature bubbles and helps destabilize and release the smaller, immature bubbles nearby. Herein, the enhancing effect of self-dynamic electrolyte flow on nanoarray structures is examined. Phase-field simulations demonstrate that the flow field of electrode with arrayed surface focuses shear force directly onto the gas bubble for efficient detachment, due to the flow could pass through voids and channels to bypass the shielding effect. The flow field therefore has a more substantial impact on the arrayed surface than the nanoscale smooth surface in terms of reducing the critical bubble size. To validate this, superaerophobic ferrous-nickel sulfide nanoarrays are fabricated and employed for water splitting, which display improved efficiency for GERs. This study contributes to understanding the influence of self-dynamic electrolyte on GERs and emphasizes that it should be considered when designing and evaluating nanoarray electrocatalysts.

Dynamics of cortical contrast adaptation predict perception of signals in noise

Neurons throughout the sensory pathway adapt their responses depending on the statistical structure of the sensory environment. Contrast gain control is a form of adaptation in the auditory cortex, but it is unclear whether the dynamics of gain control reflect efficient adaptation, and whether they shape behavioral perception. Here, we trained mice to detect a target presented in background noise shortly after a change in the contrast of the background. The observed changes in cortical gain and behavioral detection followed the dynamics of a normative model of efficient contrast gain control; specifically, target detection and sensitivity improved slowly in low contrast, but degraded rapidly in high contrast. Auditory cortex was required for this task, and cortical responses were not only similarly affected by contrast but predicted variability in behavioral performance. Combined, our results demonstrate that dynamic gain adaptation supports efficient coding in auditory cortex and predicts the perception of sounds in noise.

Multifaceted luminance gain control beyond photoreceptors in Drosophila

Animals navigating in natural environments must handle vast changes in their sensory input. Visual systems, for example, handle changes in luminance at many timescales, from slow changes across the day to rapid changes during active behavior. To maintain luminance-invariant perception, visual systems must adapt their sensitivity to changing luminance at different timescales. We demonstrate that luminance gain control in photoreceptors alone is insufficient to explain luminance invariance at both fast and slow timescales and reveal the algorithms that adjust gain past photoreceptors in the fly eye. We combined imaging and behavioral experiments with computational modeling to show that downstream of photoreceptors, circuitry taking input from the single luminance-sensitive neuron type L3 implements gain control at fast and slow timescales. This computation is bidirectional in that it prevents the underestimation of contrasts in low luminance and overestimation in high luminance. An algorithmic model disentangles these multifaceted contributions and shows that the bidirectional gain control occurs at both timescales. The model implements a nonlinear interaction of luminance and contrast to achieve gain correction at fast timescales and a dark-sensitive channel to improve the detection of dim stimuli at slow timescales. Together, our work demonstrates how a single neuronal channel performs diverse computations to implement gain control at multiple timescales that are together important for navigation in natural environments.

Complex dynamics of hair bundle of auditory nervous system (II): forced oscillations related to two cases of steady state

The forced oscillations of hair bundle of inner hair cells of auditory nervous system evoked by external force from steady state are related to the fast adaption of hair cells, which are very important for auditory amplification. In the present paper, comprehensive and deep understandings to nonlinear dynamics of forced oscillations are acquired in four aspects. Firstly, the complex dynamics underlying the twitch (fast recoil of displacement X which is fast variable) induced from Case-1 and Case-2 steady states by external pulse force are obtained. With help of vector fields and nullclines, the phase trajectory of forced oscillations is identified to be an evolution process between two equilibrium points corresponding to zero force and pulse force, respectively, and then the twitch is obtained as the behavior running along the nonlinear part of X-nullcline. Especially, twitch observed in experiment are classified into 6 types, which are induced by negative change of force, negative and positive changes of force, and positive change of force, respectively, and further build relationships to three subcases of Case-2 steady state with N-shaped X-nullcline (equilibrium point locates on the left, middle, and right branches of X-nullcline, respectively). Secondly, the experimental observation of fatigue of twitch induced by continual two pulse forces, i.e. the reduced amplitude of the latter twitch when interval between two forces is short, is also explained as a nonlinear behavior beginning from an initial value different from that of the former one. Thirdly, the experimental observation of transition between sustained oscillations and steady state induced by pulse force can be simulated for Case-1 steady state with Z-shaped X-nullcline instead of Case-2, due to that there exists bifurcations with respect to external force for Case-1 while no bifurcations for Case-2. Last, the threshold phenomenon induced by simple pulse stimulation exists for Case-1 steady state rather than Case-2, due to that the upper and lower branches of Z-shaped X-nullcline close to the middle branch exhibit coexisting behaviors of variable X while N-shaped X-nullcline does not. The nonlinear dynamics of forced oscillations are helpful for explanations to the complex experimental observations, which presents potential measures to modulate the functions of twitch such as the fast adaption.

Neural network organization for courtship-song feature detection in Drosophila

Animals communicate using sounds in a wide range of contexts, and auditory systems must encode behaviorally relevant acoustic features to drive appropriate reactions. How feature detection emerges along auditory pathways has been difficult to solve due to challenges in mapping the underlying circuits and characterizing responses to behaviorally relevant features. Here, we study auditory activity in the Drosophila melanogaster brain and investigate feature selectivity for the two main modes of fly courtship song, sinusoids and pulse trains. We identify 24 new cell types of the intermediate layers of the auditory pathway, and using a new connectomic resource, FlyWire, we map all synaptic connections between these cell types, in addition to connections to known early and higher-order auditory neurons-this represents the first circuit-level map of the auditory pathway. We additionally determine the sign (excitatory or inhibitory) of most synapses in this auditory connectome. We find that auditory neurons display a continuum of preferences for courtship song modes and that neurons with different song-mode preferences and response timescales are highly interconnected in a network that lacks hierarchical structure. Nonetheless, we find that the response properties of individual cell types within the connectome are predictable from their inputs. Our study thus provides new insights into the organization of auditory coding within the Drosophila brain.

Flexible filtering by neural inputs supports motion computation across states and stimuli

Sensory systems flexibly adapt their processing properties across a wide range of environmental and behavioral conditions. Such variable processing complicates attempts to extract a mechanistic understanding of sensory computations. This is evident in the highly constrained, canonical Drosophila motion detection circuit, where the core computation underlying direction selectivity is still debated despite extensive studies. Here we measured the filtering properties of neural inputs to the OFF motion-detecting T5 cell in Drosophila. We report state- and stimulus-dependent changes in the shape of these signals, which become more biphasic under specific conditions. Summing these inputs within the framework of a connectomic-constrained model of the circuit demonstrates that these shapes are sufficient to explain T5 responses to various motion stimuli. Thus, our stimulus- and state-dependent measurements reconcile motion computation with the anatomy of the circuit. These findings provide a clear example of how a basic circuit supports flexible sensory computation.

Motion vision: Pinning down motion computation in an ever-changing circuit

A new electrophysiological study of the Drosophila visual system, recording from columnar inputs to motion-detecting neurons, has provided new insights into the computations that underlie motion vision.

Encoding of cutaneous stimuli by lamina I projection neurons

ABSTRACT

Lamina I of the dorsal horn, together with its main output pathway, lamina I projection neurons, has long been implicated in the processing of nociceptive stimuli, as well as the development of chronic pain conditions. However, the study of lamina I projection neurons is hampered by technical challenges, including the low throughput and selection biases of traditional electrophysiological techniques. Here we report on a technique that uses anatomical labelling strategies and in vivo imaging to simultaneously study a network of lamina I projection neurons in response to electrical and natural stimuli. Although we were able to confirm the nociceptive involvement of this group of cells, we also describe an unexpected preference for innocuous cooling stimuli. We were able to characterize the thermal responsiveness of these cells in detail and found cooling responses decline when exposed to stable cold temperatures maintained for more than a few seconds, as well as to encode the intensity of the end temperature, while heating responses showed an unexpected reliance on adaptation temperatures.

Frequency tuning of swarming male mosquitoes (Aedes communis, Culicidae) and its neural mechanisms

The primary function of hearing in mosquitoes is believed to be intraspecific communication. This view dictated the principle of many behavioral studies, namely, the attraction of male mosquitoes to the sounds that mimicked a female tone. However, after the avoidance response to certain frequencies of sound was demonstrated, it became clear that attraction tests cannot fully account for all the capabilities of the mosquito auditory system. In addition, the tuning curves obtained by electrophysiological measurements differ from the behavioral ones. We designed a simple but robust field test based on responses of swarming mosquitoes to sound stimulation, but not limited to the attraction response. Here we report the auditory thresholds over a wide range of sound frequencies measured in the field from swarms of Aedes communis mosquitoes. In parallel, the auditory sensitivity of male mosquitoes taken from the same swarms was measured electrophysiologically. Surprisingly, we found high acoustic sensitivity; 26 dBSPL on average, in the frequency range 180-220 Hz (ambient temperature 12 °C). In addition, responses were found in the high-frequency range, 500-700 Hz (the so-called 'mirror channel'). Two types of auditory units were recorded: more sensitive broadband neurons and less sensitive units with distinct narrow (quality factor Q₆ = 7.4) frequency tunings in the range 180-350 Hz. We propose that the former provides the detection of signal while the latter are used for frequency identification in order to make a behavioral choice.

Heterogeneity across neural populations: Its significance for the dynamics and functions of neural circuits

Neural populations show patterns of synchronous activity, as they share common correlated inputs. Neurons in the cortex that are connected by strong synapses cause rapid firing explosions. In addition, areas that are connected by weaker synapses have a slower dynamics and they can contribute to asymmetries in the input distributions. The aim of this work is to develop a neural model to investigate how the heterogeneities in the synaptic input distributions affect different levels of organizational activity in the brain dynamics. We analytically show how small changes in the correlation inputs can cause large changes in the interactions of the outputs that lead to a phase transition, demonstrating that a simple variation in the direction of a biased skewed distribution in the neuronal inputs can generate a transition of states in the firing rate, passing from spontaneous silence ("down state") to an absolute spiking activity ("up state"). We present an exact quantification of the dynamics of the output variables, showing that when considering a biased skewed distribution in the inputs of neuronal population, the critical point is not in an asynchronous or synchronous state but rather at an intermediate value.

Neural adaptation

The term 'neural adaptation' refers to the common phenomenon of decaying neuronal activities in response to repeated or prolonged stimulation. Many different roles of adaptation in neural computations have been discussed. On a single-cell level adaptation introduces a high-pass filter operation as a basic element for predictive coding. Interactions of adaptation processes with nonlinearities are key to many more computations including generation of invariances, stimulus selectivity, denoising, and sparsening. Neural adaptation is observed all the way along neuronal pathways from the sensory periphery to the motor output and adaptation usually gets stronger at higher levels. Non-adapting neurons or neurons that increase their sensitivity are rare exceptions. What computations arise by repeated adaptation mechanisms along a processing pathway? After giving some background on neural adaptation, underlying mechanisms, dynamics, and resulting filter properties, I will discuss computational properties of four examples of serial and parallel adaptation processes, demonstrating that adaptation acts together with other mechanisms, in particular threshold nonlinearities, to eventually compute meaningful perceptions. Python code and further details of the simulations illustrating this primer are available at https://github.com/janscience/adaptationprimer.

Drosophila Mechanosensory Transduction

Mechanosensation in Drosophila relies on sensory neurons transducing mechanical stimuli into ionic currents. The molecular mechanisms of this transduction are in the process of being revealed. Transduction relies on mechanogated ion channels that are activated by membrane stretch or the tension of force-conveying tethers. NOMPC (no-mechanoreceptor potential C) and DmPiezo were put forward as bona fide mechanoelectrical transduction (MET) channels, providing insights into MET channel architecture and the structural basis of mechanogating. Various additional channels were implicated in Drosophila mechanosensory neuron functions, and parallels between fly and vertebrate mechanotransduction were delineated. Collectively, these advances put forward Drosophila mechanosensory neurons as cellular paradigms for mechanotransduction and mechanogated ion channel function in the context of proprio- and nociception as well as the detection of substrate vibrations, touch, gravity, and sound.

Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

Shared Song Detector Neurons in Drosophila Male and Female Brains Drive Sex-Specific Behaviors

Males and females often produce distinct responses to the same sensory stimuli. How such differences arise-at the level of sensory processing or in the circuits that generate behavior-remains largely unresolved across sensory modalities. We address this issue in the acoustic communication system of Drosophila. During courtship, males generate time-varying songs, and each sex responds with specific behaviors. We characterize male and female behavioral tuning for all aspects of song and show that feature tuning is similar between sexes, suggesting sex-shared song detectors drive divergent behaviors. We then identify higher-order neurons in the Drosophila brain, called pC2, that are tuned for multiple temporal aspects of one mode of the male's song and drive sex-specific behaviors. We thus uncover neurons that are specifically tuned to an acoustic communication signal and that reside at the sensory-motor interface, flexibly linking auditory perception with sex-specific behavioral responses.

Online Detection of Multiple Stimulus Changes Based on Single Neuron Interspike Intervals

Nervous systems need to detect stimulus changes based on their neuronal responses without using any additional information on the number, times, and types of stimulus changes. Here, two relatively simple, biologically realistic change point detection methods are compared with two common analysis methods. The four methods are applied to intra- and extracellularly recorded responses of a single cricket interneuron (AN2) to acoustic simulation. Solely based on these recorded responses, the methods should detect an unknown number of different types of sound intensity in- and decreases shortly after their occurrences. For this task, the methods rely on calculating an adjusting interspike interval (ISI). Both simple methods try to separate responses to intensity in- or decreases from activity during constant stimulation. The Pure-ISI method performs this task based on the distribution of the ISI, while the ISI-Ratio method uses the ratio of actual and previous ISI. These methods are compared to the frequently used Moving-Average method, which calculates mean and standard deviation of the instantaneous spike rate in a moving interval. Additionally, a classification method provides the upper limit of the change point detection performance that can be expected for the cricket interneuron responses. The classification learns the statistical properties of the actual and previous ISI during stimulus changes and constant stimulation from a training data set. The main results are: (1) The Moving-Average method requires a stable activity in a long interval to estimate the previous activity, which was not always given in our data set. (2) The Pure-ISI method can reliably detect stimulus intensity increases when the neuron bursts, but it fails to identify intensity decreases. (3) The ISI-Ratio method detects stimulus in- and decreases well, if the spike train is not too noisy. (4) The classification method shows good performance for the detection of stimulus in- and decreases. But due to the statistical learning, this method tends to confuse responses to constant stimulation with responses triggered by a stimulus change. Our results suggest that stimulus change detection does not require computationally costly mechanisms. Simple nervous systems like the cricket's could effectively apply ISI-Ratios to solve this fundamental task.

To court or not to court - a multimodal sensory decision in Drosophila males

When Drosophila males encounter another fly, they have to make a rapid assessment to ensure the appropriate response: should they court, fight or pursue a different action entirely? Previous work has focused on the significance of sensory cues detected by the male during these encounters; however, recent evidence highlights the importance of the male's own internal state in shaping his responses. Additionally, once triggered, courtship is not a rigid sequence of motor actions, but rather a finely tuned behavioural display that must continually update in response to sensory feedback. Here, we review recent findings highlighting how sensory information and internal states are integrated ensuring appropriate action selection, and how they sustain and fine-tune motor output. We further discuss recent advances in our understanding of species differences in sensory processing that may contribute to reproductive isolation.

Temporal processing properties of auditory DUM neurons in a bush-cricket

Insects with ears process sounds and respond to conspecific signals or predator cues. Axons of auditory sensory cells terminate in mechanosensory neuropils from which auditory interneurons project into (brain-) areas to prepare response behaviors. In the prothoracic ganglion of a bush-cricket, a cluster of local DUM (dorsal unpaired median) neurons has recently been described and constitutes a filter bank for carrier frequency. Here, we demonstrate that these neurons also constitute a filter bank for temporal patterns. The majority of DUM neurons showed pronounced phasic-tonic responses. The transitions from phasic to tonic activation had different time constants in different DUM neurons. Time constants of the membrane potential were shorter in most DUM neurons than in auditory sensory neurons. Patterned stimuli with known behavioral relevance evoked a broad range of responses in DUM neurons: low-pass, band-pass, and high-pass characteristics were encountered. Temporal and carrier frequency processing were not correlated. Those DUM neurons producing action potentials showed divergent processing of temporal patterns when the graded potential or the spiking was analyzed separately. The extent of membrane potential fluctuations mimicking the patterned stimuli was different between otherwise similarly responding neurons. Different kinds of inhibition were apparent and their relevance for temporal processing is discussed.

Coding Principles in Adaptation

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

Robust and Intensity-Dependent Synaptic Inhibition Underlies the Generation of Non-monotonic Neurons in the Mouse Inferior Colliculus

Intensity and frequency are the two main properties of sound. The non-monotonic neurons in the auditory system are thought to represent sound intensity. The central nucleus of the inferior colliculus (ICC), as an important information integration nucleus of the auditory system, is also involved in the processing of intensity encoding. Although previous researchers have hinted at the importance of inhibitory effects on the formation of non-monotonic neurons, the specific underlying synaptic mechanisms in the ICC are still unclear. Therefore, we applied the in vivo whole-cell voltage-clamp technique to record the excitatory and inhibitory postsynaptic currents (EPSCs and IPSCs) in the ICC neurons, and compared the effects of excitation and inhibition on the membrane potential outputs. We found that non-monotonic neuron responses could not only be inherited from the lower nucleus but also be created in the ICC. By integrating with a relatively weak IPSC, approximately 35% of the monotonic excitatory inputs remained in the ICC. In the remaining cases, monotonic excitatory inputs were reshaped into non-monotonic outputs by the dominating inhibition at high intensity, which also enhanced the non-monotonic nature of the non-monotonic excitatory inputs.

Variance adaptation in navigational decision making

Sensory systems relay information about the world to the brain, which enacts behaviors through motor outputs. To maximize information transmission, sensory systems discard redundant information through adaptation to the mean and variance of the environment. The behavioral consequences of sensory adaptation to environmental variance have been largely unexplored. Here, we study how larval fruit flies adapt sensory-motor computations underlying navigation to changes in the variance of visual and olfactory inputs. We show that variance adaptation can be characterized by rescaling of the sensory input and that for both visual and olfactory inputs, the temporal dynamics of adaptation are consistent with optimal variance estimation. In multisensory contexts, larvae adapt independently to variance in each sense, and portions of the navigational pathway encoding mixed odor and light signals are also capable of variance adaptation. Our results suggest multiplication as a mechanism for odor-light integration.

Functional Maps of Mechanosensory Features in the Drosophila Brain

Johnston's organ is the largest mechanosensory organ in Drosophila. It contributes to hearing, touch, vestibular sensing, proprioception, and wind sensing. In this study, we used in vivo 2-photon calcium imaging and unsupervised image segmentation to map the tuning properties of Johnston's organ neurons (JONs) at the site where their axons enter the brain. We then applied the same methodology to study two key brain regions that process signals from JONs: the antennal mechanosensory and motor center (AMMC) and the wedge, which is downstream of the AMMC. First, we identified a diversity of JON response types that tile frequency space and form a rough tonotopic map. Some JON response types are direction selective; others are specialized to encode amplitude modulations over a specific range (dynamic range fractionation). Next, we discovered that both the AMMC and the wedge contain a tonotopic map, with a significant increase in tonotopy-and a narrowing of frequency tuning-at the level of the wedge. Whereas the AMMC tonotopic map is unilateral, the wedge tonotopic map is bilateral. Finally, we identified a subregion of the AMMC/wedge that responds preferentially to the coherent rotation of the two mechanical organs in the same angular direction, indicative of oriented steady air flow (directional wind). Together, these maps reveal the broad organization of the primary and secondary mechanosensory regions of the brain. They provide a framework for future efforts to identify the specific cell types and mechanisms that underlie the hierarchical re-mapping of mechanosensory information in this system.