Collaborative Research: A Dynamic Atlas of the Cricket Cercal Sensory System

A fundamental question in neuroscience is how natural sensory stimuli are encoded for information handling by the brain. Invertebrate animals often offer systems that are in some ways simpler than those of mammals, and including such features as identifiable single cells in networks of relatively few numbers. This collaborative project exploits a sensory system called the cercal system of the cricket, in which small appendages on the rear of the body contain fine hairs that are used to detect, identify and localize behaviorally relevant air current movements, such as those produced by a predator. The input from roughly 2000 receptor cells converges on 30 local interneurons and only 20 output interneurons that lead to behavior such as escape. Three collaborators at two institutions use computational and mathematical analyses of a database of anatomical and physiological measurements on the \u27dynamic map\u27 that does the central processing in the brain of the peripheral signals. The goals are to characterize the representation of dynamic sensory stimulus parameters at two processing stages within the mapped sensory system, and to examine the mechanisms that transform the representation at the interface between these two processing stages. Results will be important for our understanding of information representation in nervous systems, particularly in dynamic processing. The project also will enhance the independent career of a woman faculty member in mathematics, and students will receive multi-disciplinary, highly quantitative training related to biology, in two states that do not currently have high profiles in federally funded research

The biomechanics of sensory organs

Studies of mechanosensory systems have largely focused on the filter characteristics of their neural components in relation to their ultimate function. Less attention has focused on the role of the physical structure of the sensory organ which also acts as a mechanical filter of the sensory input. This biomechanical filtering is readily apparent in the case of several mechanosensory systems that transduce information about the deformations of the sensory organs in response to external forces. Because these deformations critically depend on the geometry and material properties of the mechanosensory organs, it is necessary to conduct focused studies on the biomechanical characteristics of these organs when studying the encoding properties of the mechanosensory system. Modern experimental tools such as Laser Doppler Vibrometry and computational tools such as Computational Fluid Dynamics and Finite Element Analysis provide the means for determining the sensory pre-filtering properties of small-scale mechanosensory structures. In all the cases covered in this review, the physical properties of the sensory organs play a central role in determining the signals received by the nervous system

Information Transmission in Cercal Giant Interneurons Is Unaffected by Axonal Conduction Noise

What are the fundamental constraints on the precision and accuracy with which nervous systems can process information? One constraint must reflect the intrinsic “noisiness” of the mechanisms that transmit information between nerve cells. Most neurons transmit information through the probabilistic generation and propagation of spikes along axons, and recent modeling studies suggest that noise from spike propagation might pose a significant constraint on the rate at which information could be transmitted between neurons. However, the magnitude and functional significance of this noise source in actual cells remains poorly understood. We measured variability in conduction time along the axons of identified neurons in the cercal sensory system of the cricket Acheta domesticus, and used information theory to calculate the effects of this variability on sensory coding. We found that the variability in spike propagation speed is not large enough to constrain the accuracy of neural encoding in this system

Quantitative Characterization of the Filiform Mechanosensory Hair Array on the Cricket Cercus

Crickets and other orthopteran insects sense air currents with a pair of abdominal appendages resembling antennae, called cerci. Each cercus in the common house cricket Acheta domesticus is approximately 1 cm long, and is covered with 500 to 750 filiform mechanosensory hairs. The distribution of the hairs on the cerci, as well as the global patterns of their movement vectors, have been characterized semi-quantitatively in studies over the last 40 years, and have been shown to be very stereotypical across different animals in this species. Although the cercal sensory system has been the focus of many studies in the areas of neuroethology, development, biomechanics, sensory function and neural coding, there has not yet been a quantitative study of the functional morphology of the receptor array of this important model system.We present a quantitative characterization of the structural characteristics and functional morphology of the cercal filiform hair array. We demonstrate that the excitatory direction along each hair's movement plane can be identified by features of its socket that are visible at the light-microscopic level, and that the length of the hair associated with each socket can also be estimated accurately from a structural parameter of the socket. We characterize the length and directionality of all hairs on the basal half of a sample of three cerci, and present statistical analyses of the distributions.The inter-animal variation of several global organizational features is low, consistent with constraints imposed by functional effectiveness and/or developmental processes. Contrary to previous reports, however, we show that the filiform hairs are not re-identifiable in the strict sense

I, NEURON: the neuron as the collective

Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists. Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 mainstream papers were scrutinized. Findings – Anthropomorphization arose because single neurons were cast as “observers” who “identify”, “categorize”, “recognize”, “distinguish” or “discriminate” the stimuli, using math-based algorithms that reduce (“decode”) the stimulus-evoked spike trains to the particular stimuli inferred to elicit them. Without “decoding”, there is supposedly no perception. However, “decoding” is both unnecessary and unconfirmed. The neuronal “observer” in fact consists of the laboratory staff and the greater society that supports them. In anthropomorphization, the neuron becomes the collective. Research limitations/implications – Anthropomorphization underlies the widespread application to neurons Information Theory and Signal Detection Theory, making both approaches incorrect. Practical implications – A great deal of time, money and effort has been wasted on anthropomorphic Reductionist approaches to understanding perception and consciousness. Those resources should be diverted into more-fruitful approaches. Originality/value – A long-overdue scrutiny of sensory-neuroscience literature reveals that anthropomorphization, a form of Reductionism that involves the presumption of single-neuron consciousness, has run amok in neuroscience. Consciousness is more likely to be an emergent property of the brain

Stimulus Encoding and Feature Extraction by Multiple Sensory Neurons

Neighboring cells in topographical sensory maps may transmit similar information to the next higher level of processing. How information transmission by groups of nearby neurons compares with the performance of single cells is a very important question for understanding the functioning of the nervous system. To tackle this problem, we quantified stimulus-encoding and feature extraction performance by pairs of simultaneously recorded electrosensory pyramidal cells in the hindbrain of weakly electric fish. These cells constitute the output neurons of the first central nervous stage of electrosensory processing. Using random amplitude modulations (RAMs) of a mimic of the fish’s own electric field within behaviorally relevant frequency bands, we found that pyramidal cells with overlapping receptive fields exhibit strong stimulus-induced correlations. To quantify the encoding of the RAM time course, we estimated the stimuli from simultaneously recorded spike trains and found significant improvements over single spike trains. The quality of stimulus reconstruction, however, was still inferior to the one measured for single primary sensory afferents. In an analysis of feature extraction, we found that spikes of pyramidal cell pairs coinciding within a time window of a few milliseconds performed significantly better at detecting upstrokes and downstrokes of the stimulus compared with isolated spikes and even spike bursts of single cells. Coincident spikes can thus be considered “distributed bursts.” Our results suggest that stimulus encoding by primary sensory afferents is transformed into feature extraction at the next processing stage. There, stimulus-induced coincident activity can improve the extraction of behaviorally relevant features from the stimulus

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

3D motion : encoding and perception

The visual system supports perception and inferences about events in a dynamic, three-dimensional (3D) world. While remarkable progress has been made in the study of visual information processing, the existing paradigms for examining visual perception and its relation to neural activity often fail to generalize to perception in the real world which has complex dynamics and 3D spatial structure. This thesis focuses on the case of 3D motion, developing dynamic tasks for studying visual perception and constructing a neural coding framework to relate neural activity to perception in a 3D environment. First, I introduce target-tracking as a psychophysical method and develop an analysis framework based on state space models and the Kalman filter. I demonstrate that target-tracking in conjunction with a Kalman filter analysis framework produce estimates of visual sensitivity that are comparable to those obtained with a traditional forced-choice task and a signal detection theory analysis. Next, I use the target-tracking paradigm in a series of experiments examining 3D motion perception, specifically comparing the perception of frontoparallel motion with the perception of motion-through-depth. I find that continuous tracking of motion-through-depth is selectively impaired due to the relatively small retinal projections resulting from motion-through-depth and the slower processing of binocular disparities. The thesis then turns the neural representation of 3D motion and how that underlies perception. First I introduce a theoretical framework that extends the standard neural coding approach, incorporating the environment-to-retina transformation. Neural coding typically treats the visuals stimulus as a direct proxy for the pattern of stimulation that falls on the retina. Incorporating the environment-to-retina transformation results in a neural representation fundamentally shaped by the projective geometry of the world onto the retina. This model explains substantial anomalies in existing neurophysiological recordings in primate visual cortical neurons during presentations of 3D motion and in psychophysical studies of human perception. In a series of psychophysical experiments, I systematically examine the predictions of the model for human perception by observing how perceptual performance changes as a function of viewing distance and eccentricity. Performance in these experiments suggests a reliance on a neural representation similar to the one described by the model. Taken together, the experimental and theoretical findings reported here advance the understanding of the neural representation and perception of the dynamic 3D world, and adds to the behavioral tools available to vision scientists.Neuroscienc

An auditory feature detection circuit for sound pattern recognition.

From human language to birdsong and the chirps of insects, acoustic communication is based on amplitude and frequency modulation of sound signals. Whereas frequency processing starts at the level of the hearing organs, temporal features of the sound amplitude such as rhythms or pulse rates require processing by central auditory neurons. Besides several theoretical concepts, brain circuits that detect temporal features of a sound signal are poorly understood. We focused on acoustically communicating field crickets and show how five neurons in the brain of females form an auditory feature detector circuit for the pulse pattern of the male calling song. The processing is based on a coincidence detector mechanism that selectively responds when a direct neural response and an intrinsically delayed response to the sound pulses coincide. This circuit provides the basis for auditory mate recognition in field crickets and reveals a principal mechanism of sensory processing underlying the perception of temporal patterns.Financial support for the study was provided by the Biotechnology and Biological Sciences Research Council (BB/J01835X/1) and the Isaac Newton Trust (Trinity College, Cambridge).This is the final version of the article. It first appeared from AAAS via http://dx.doi.org/10.1126/sciadv.150032