Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta.

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

Nonuniform structural properties of wings confer sensing advantages.

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations during each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients and are subject to both aerodynamic and structural damping. Here we examine how both of these properties impact sensory performance, using finite element analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

Wing structure and neural encoding jointly determine sensing strategies in insect flight.

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing are largely unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively on a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, enable accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of understanding each in the context of their joint evolution.

Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions.

Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.

Spatial distribution of campaniform sensilla mechanosensors on wings: form, function, and phylogeny.

Insect wings serve two crucial functions in flight: propulsion and sensing. During flapping flight, complex spatiotemporal patterns of strain on the wing reflect mechanics, kinematics, and external perturbations; sensing wing deformation provides feedback necessary for flight control. Campaniform sensilla distributed across the wing transduce local strain fluctuations into neural signals, so their placement on the wing determines sensory information available to the insect. Thus, understanding the significance of these sensor locations will also reveal how sensing and wing movement are coupled. Here, we identify trends in wing campaniform sensilla placement across flying insects from the literature. We then discuss how these patterns can influence sensory encoding by wing mechanosensors. Finally, we propose combining a comparative approach on model insect clades with computational modeling, leveraging the spectacular natural diversity in wings to uncover biological principles of mechanosensory feedback in flight control.

The role of adaptation in neural coding.

The concept of 'neural coding' supposes that neural firing patterns in some sense represent some external correlate, whether sensory, motor, or structural knowledge about the world. While the implied existence of a one-to-one mapping between external referents and neural firing has been useful, the prevalence of adaptation challenges this. Adaptation provides neural responses with dynamics on timescales that range from milliseconds up to many seconds. These timescales are highly relevant for sensory experience in the natural world, in which local statistical properties of inputs change continuously, and are additionally altered by active sensing. Adaptation has a number of consequences for coding: it creates short-term history dependence; it engenders complex feature selectivity that is time-varying; and it can serve to enhance information representation in dynamic environments. Considering how to best incorporate adaptation into neural models exposes a fundamental dichotomy in approaches to the description of neural systems: ones that take an explicitly 'coding' perspective versus ones that describe the system's dynamics. Here we discuss the pros and cons of different approaches to the modeling of adaptive dynamics.

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.

The Effect of Contact Force on the Responses of Tactile Nerve Fibers to Scanned Textures.

The perception of fine textures relies on highly precise and repeatable spiking patterns evoked in tactile afferents. These patterns have been shown to depend not only on the surface microstructure and material but also on the speed at which it moves across the skin. Interestingly, the perception of texture is independent of scanning speed, implying the existence of downstream neural mechanisms that correct for scanning speed in interpreting texture signals from the periphery. What force is applied during texture exploration also has negligible effects on how the surface is perceived, but the consequences of changes in contact force on the neural responses to texture have not been described. In the present study, we measure the signals evoked in tactile afferents of macaques to a diverse set of textures scanned across the skin at two different contact forces and find that responses are largely independent of contact force over the range tested. We conclude that the force invariance of texture perception reflects the force independence of texture representations in the nerve.

The neural code for tactile roughness in the somatosensory nerves.

Roughness is the most salient perceptual dimension of surface texture but has no well-defined physical basis. We seek to determine the neural determinants of tactile roughness in the somatosensory nerves. Specifically, we record the patterns of activation evoked in tactile nerve fibers of anesthetized Rhesus macaques to a large and diverse set of natural textures and assess what aspect of these patterns of activation can account for psychophysical judgments of roughness, obtained from human observers. We show that perceived roughness is determined by the variation in the population response, weighted by fiber type. That is, a surface will feel rough to the extent that the activity varies across nerve fibers and varies across time within nerve fibers. We show that this variation-based neural code can account not only for magnitude estimates of roughness but also for roughness discrimination performance.NEW & NOTEWORTHY Our sense of touch endows us with an exquisite sensitivity to the microstructure of surfaces, the most salient aspect of which is roughness. We analyze the responses evoked in tactile fibers of monkeys by natural textures and compare them to judgments of roughness obtained for the same textures from human observers. We then describe how texture signals from three populations of nerve fibers are integrated to culminate in a percept of roughness.

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.

A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.

How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

Spatial and temporal codes mediate the tactile perception of natural textures.

When we run our fingers over the surface of an object, we acquire information about its microgeometry and material properties. Texture information is widely believed to be conveyed in spatial patterns of activation evoked across one of three populations of cutaneous mechanoreceptive afferents that innervate the fingertips. Here, we record the responses evoked in individual cutaneous afferents in Rhesus macaques as we scan a diverse set of natural textures across their fingertips using a custom-made rotating drum stimulator. We show that a spatial mechanism can only account for the processing of coarse textures. Information about most natural textures, however, is conveyed through precise temporal spiking patterns in afferent responses, driven by high-frequency skin vibrations elicited during scanning. Furthermore, these texture-specific spiking patterns predictably dilate or contract in time with changes in scanning speed; the systematic effect of speed on neuronal activity suggests that it can be reversed to achieve perceptual constancy across speeds. The proposed temporal coding mechanism involves converting the fine spatial structure of the surface into a temporal spiking pattern, shaped in part by the mechanical properties of the skin, and ascribes an additional function to vibration-sensitive mechanoreceptive afferents. This temporal mechanism complements the spatial one and greatly extends the range of tangible textures. We show that a combination of spatial and temporal mechanisms, mediated by all three populations of afferents, accounts for perceptual judgments of texture.

Comparing the effects of isoflurane and pentobarbital on the responses of cutaneous mechanoreceptive afferents.

BACKGROUND: While pentobarbital has been used extensively in neurophysiological experiments investigating activity in peripheral nerves, it has fallen out of favor as an anesthetic because of safety concerns and is often replaced with isoflurane. However, the effects of isoflurane on the excitability of mechanoreceptive afferents have yet to be conclusively elucidated. METHODS: To fill this gap, we collected extracellular single-unit recordings of cutaneous mechanoreceptive afferents from the sciatic nerve of 21 rats during vibratory stimulation of the hindpaw. We then compared the strength and temporal structure of the afferent response measured under pentobarbital and isoflurane anesthesia. RESULTS: We found that the strength and temporal structure of afferent responses were statistically equivalent whether these were evoked under isoflurane or pentobarbital. CONCLUSIONS: We conclude that, if these two anesthetics have any effect on the responses of mechanoreceptive afferents, their effects are indistinguishable.

Separate mechanisms for audio-tactile pitch and loudness interactions.

A major goal in perceptual neuroscience is to understand how signals from different sensory modalities are combined to produce stable and coherent representations. We previously investigated interactions between audition and touch, motivated by the fact that both modalities are sensitive to environmental oscillations. In our earlier study, we characterized the effect of auditory distractors on tactile frequency and intensity perception. Here, we describe the converse experiments examining the effect of tactile distractors on auditory processing. Because the two studies employ the same psychophysical paradigm, we combined their results for a comprehensive view of how auditory and tactile signals interact and how these interactions depend on the perceptual task. Together, our results show that temporal frequency representations are perceptually linked regardless of the attended modality. In contrast, audio-tactile loudness interactions depend on the attended modality: Tactile distractors influence judgments of auditory intensity, but judgments of tactile intensity are impervious to auditory distraction. Lastly, we show that audio-tactile loudness interactions depend critically on stimulus timing, while pitch interactions do not. These results reveal that auditory and tactile inputs are combined differently depending on the perceptual task. That distinct rules govern the integration of auditory and tactile signals in pitch and loudness perception implies that the two are mediated by separate neural mechanisms. These findings underscore the complexity and specificity of multisensory interactions.