A Multidisciplinary Study of Neural Coding Underlying Sensory-Motor Responses in the Leech

The subject of my Ph.D. project is the study of the neural coding of sensory-motor responses in the leech Hirudo Medicinalis. I have used a combination of multielectrode recordings, videomicroscopy and computer vision methods to quantify the leech behavior and simultaneously measure its underlying neuronal activity. Based on this experimental approach, I was able to characterize the motor pattern underlying reflexive behavior in the leech, addressing fundamental issues of systems neuroscience such as population coding and distributed organization of motor responses. In this context, the specific aims of my Ph.D. project can be summarized in three main goals: 1) to develop a new method to quantify the two-dimensional pattern of defmmation induced by muscle contraction on the leech body wall; 2) to investigate the distributed organization of leech reflexive behavior at the level of recruitment and activation of specific classes of motoneurons and muscle fibers; 3) to understand the neural basis of the reliability of leech motor responses, by quantifying their reproducibility at different levels of neural processing. In this scheme, the accomplishment of a specific aim was the starting point to address the next one. Quantitative analysis of defommtion occurring on leech body-wall was the necessary backdrop for characterizing leech sensory-motor responses at the behavioral level. This quantitative analysis of behavior, in tum, was fundamental for comparing reproducibility of leech motor responses with that of neural firing sustaining them. In the following paragraphs, I will summarize the major achievements of my Ph.D. project in relation to established main goals. The first aim of my research was to develop a reliable and innovative method to quantify the pattern of deformation that muscle contraction induces on the leech body-wall. Muscle contraction is usually measured and characterized with force and displacement transducers. The contraction of muscle fibers, however, evokes in the tissue a two and even three-dimensional displacement field, which is not properly quantified by these transducers because they provide just a single scalar quantity. I circumvented this problem by using videomicroscopy and standard tools of computer vision developed for the analysis of time varying image sequences. By computing the so called optical flow, i.e. the apparent motion of points in a time varying image sequence, it is possible to recover a two dimensional motion field, describing rather precisely the displacement caused by muscle contraction in a flattened piece of skin. The obtained two dimensional optical flow can be further analyzed by computing its migin (i.e. the singular point) and four elementary components that combine linearly: expansion, rotation, longitudinal shear and oblique shear. These elementary deformations provide a compact and accurate characterization of the contraction induced by different motoneurons. I demonstrated this technique by analyzing the displacement caused by muscle contraction on the leech body-wall. However, this method can be applied to monitor and characterize all contractions in almost flat tissues with enough visual texture. The second aim of my Ph.D. project was to apply the method described above in order to characterize the pattern of activation of different classes of motoneurons and muscles during leech reflexive behavior. Activation of motoneurons innervating leech muscles causes the appearance of a two dimensional vector field of deformations on the skin surface that can be fully characterized by computing the optical flow. I found that all motoneurons can be classified and recognized according to the elementary deformations of the contractions they elicit: longitudinal motoneurons give rise almost exclusively to longitudinal negative shear, whereas circular motoneurons give rise to both positive longitudinal shear and significant negative expansion. Oblique motoneurons induce strong oblique shear, in addition to longihidinal shear and negative expansion. These results clearly showed that contractions induced by different classes of motoneurons and muscle fibers form a set of basic behavioral units. I also investigated the way in which such behavioral units can combine to sustain motor responses and I found that optical flows induced by the contraction of longitudinal, circular and oblique fibers superimpose linearly. Complex patterns of skin deformation induced by mechanosensory stimulation can therefore be attributed rather reliably to the contraction of distinct longitudinal, circular and oblique muscle fibers. Based on this conclusion, I found that local bending, a motor response caused by local mechanical stimulation of the leech skin, is sustained by coactivation of two distinct classes of motoneurons: circular and longitudinal. I also compared the pattern of deformation produced by local bending with that produced by intracellular stimulation of mechanosensory pressure (P), touch (T) and nociceptive (N) cells: optical flows resulting from the activation of P cells were almost identical to those produced by mechanical stimulation. This confirmed that local bending is almost entirely mediated by excitation of P cells, with minor contributions from T and N cells. In conclusion, these results revealed the distributed nature of leech reflexive behavior at the level of muscle activation and motoneuron recruitment, showing that complex motor responses result from the linear sum of a small number of basic patterns of deformation. The final step in investigating distributed motor behavior in the leech was to characterize the reproducibility of local bending and that of neural firing sustaining it. I analyzed variability at different levels of processing: mechanosensory neurons, motoneurons, muscle activation and behavior. I found that spike trains in mechanosensory neurons were very reproducible, unlike those in motoneurons. However, the motor response was much more reproducible than the firing of individual motoneurons sustaining it. I showed that this reliability of the behavior is obtained by two distinct biophysical mechanisms: temporal and ensemble averaging. The former is guaranteed by the low pass filtering properties of the leech muscles that contract very slowly and therefore are poorly sensitive to the jitter of motoneuron spikes. The latter is provided by the coactivation of a population of motoneurons, firing in a statistically independent way. This statistical independence reduces the vm~ability of the population firing. These results have a general significance, because they show that reproducible spike trains are not required to sustain reproducible behaviors and illustrate how the nervous system can cope with umeliable components to produce reliable action

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells

Unsupervised adaptation to the spatiotemporal statistics of visual experience is a key computational principle that has long been assumed to govern postnatal development of visual cortical tuning, including orientation selectivity of simple cells and position tolerance of complex cells in primary visual cortex (V1). Yet, causal empirical evidence supporting this hypothesis is scant. Here, we show that degrading the temporal continuity of visual experience during early postnatal life leads to a sizable reduction of the number of complex cells and to an impairment of their functional properties while fully sparing the development of simple cells. This causally implicates adaptation to the temporal structure of the visual input in the development of transformation tolerance but not of shape tuning, thus tightly constraining computational models of unsupervised cortical learning

A machine learning framework to optimize optic nerve electrical stimulation for vision restoration

Optic nerve electrical stimulation is a promising technique to restore vision in blind subjects. Machine learning methods can be used to select effective stimulation protocols, but they require a model of the stimulated system to generate enough training data. Here, we use a convolutional neural network (CNN) as a model of the ventral visual stream. A genetic algorithm drives the activation of the units in a layer of the CNN representing a cortical region toward a desired pattern, by refining the activation imposed at a layer representing the optic nerve. To simulate the pattern of activation elicited by the sites of an electrode array, a simple point-source model was introduced and its optimization process was investigated for static and dynamic scenes. Psychophysical data confirm that our stimulation evolution framework produces results compatible with natural vision. Machine learning approaches could become a very powerful tool to optimize and personalize neuroprosthetic systems

A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials

In recent years, the advent of the so-called silicon probes has made it possible to homogeneously sample spikes and local field potentials (LFPs) from a regular grid of cortical recording sites. In principle, this allows inferring the laminar location of the sites based on the spatiotemporal pattern of LFPs recorded along the probe, as in the well-known current source-density (CSD) analysis. This approach, however, has several limitations, since it relies on visual identification of landmark features (i.e., current sinks and sources) by human operators, features that can be absent from the CSD pattern if the probe does not span the whole cortical thickness, thus making manual labeling harder. Furthermore, as with any manual annotation procedure, the typical CSD-based workflow for laminar identification of recording sites is affected by subjective judgment undermining the consistency and reproducibility of results. To overcome these limitations, we developed an alternative approach, based on finding the optimal match between the LFPs recorded along a probe in a given experiment and a template LFP profile that was computed using 18 recording sessions, in which the depth of the recording sites had been recovered through histology. We show that this method can achieve an accuracy of 79 \u3bcm in recovering the cortical depth of recording sites and a 76% accuracy in inferring their laminar location. As such, our approach provides an alternative to CSD that, being fully automated, is less prone to the idiosyncrasies of subjective judgment and works reliably also for recordings spanning a limited cortical stretch. NEW & NOTEWORTHY Knowing the depth and laminar location of the microelectrodes used to record neuronal activity from the cerebral cortex is crucial to properly interpret the recorded patterns of neuronal responses. Here, we present an innovative approach that allows inferring such properties with high accuracy and in an automated way (i.e., without the need of visual inspection and manual annotation) from the evoked response potentials elicited by sensory (e.g., visual) stimuli

Dynamically Orthogonal Approximation for Stochastic Differential Equations

In this paper, we set the mathematical foundations of the Dynamical Low Rank Approximation (DLRA) method for high-dimensional stochastic differential equations. DLRA aims at approximating the solution as a linear combination of a small number of basis vectors with random coefficients (low rank format) with the peculiarity that both the basis vectors and the random coefficients vary in time. While the formulation and properties of DLRA are now well understood for random/parametric equations, the same cannot be said for SDEs and this work aims to fill this gap. We start by rigorously formulating a Dynamically Orthogonal (DO) approximation (an instance of DLRA successfully used in applications) for SDEs, which we then generalize to define a parametrization independent DLRA for SDEs. We show local well-posedness of the DO equations and their equivalence with the DLRA formulation. We also characterize the explosion time of the DO solution by a loss of linear independence of the random coefficients defining the solution expansion and give sufficient conditions for global existence.Comment: 32 page

Transformation-tolerant object recognition in rats revealed by visual priming

Successful use of rodents as models for studying object vision crucially depends on the ability of their visual system to construct representations of visual objects that tolerate (i.e., remain relatively unchanged with respect to) the tremendous changes in object appearance produced, for instance, by size and viewpoint variation. Whether this is the case is still controversial, despite some recent demonstration of transformation-tolerant object recognition in rats. In fact, it remains unknown to what extent such a tolerant recognition has a spontaneous, perceptual basis, or, alternatively, mainly reflects learning of arbitrary associative relations among trained object appearances. In this study, we addressed this question by training rats to categorize a continuum of morph objects resulting from blending two object prototypes. The resulting psychometric curve (reporting the proportion of responses to one prototype along the morph line) served as a reference when, in a second phase of the experiment, either prototype was briefly presented as a prime, immediately before a test morph object. The resulting shift of the psychometric curve showed that recognition became biased toward the identity of the prime. Critically, this bias was observed also when the primes were transformed along a variety of dimensions (i.e., size, position, viewpoint, and their combination) that the animals had never experienced before. These results indicate that rats spontaneously perceive different views/appearances of an object as similar (i.e., as instances of the same object) and argue for the existence of neuronal substrates underlying formation of transformation-tolerant object representations in rats

Supralinear and Supramodal Integration of Visual and Tactile Signals in Rats: Psychophysics and Neuronal Mechanisms

To better understand how object recognition can be triggered independently of the sensory channel through which information is acquired, we devised a task in which rats judged the orientation of a raised, black and white grating. They learned to recognize two categories of orientation: 0° ± 45° ("horizontal") and 90° ± 45° ("vertical"). Each trial required a visual (V), a tactile (T), or a visual-tactile (VT) discrimination; VT performance was better than that predicted by optimal linear combination of V and T signals, indicating synergy between sensory channels. We examined posterior parietal cortex (PPC) and uncovered key neuronal correlates of the behavioral findings: PPC carried both graded information about object orientation and categorical information about the rat's upcoming choice; single neurons exhibited identical responses under the three modality conditions. Finally, a linear classifier of neuronal population firing replicated the behavioral findings. Taken together, these findings suggest that PPC is involved in the supramodal processing of shape. Knowledge about objects can be accessed through multiple sensory pathways. Nikbakht et al. find that rats judge object orientation by synergistically combining signals from vision and touch; posterior parietal cortex seems to be involved in the supramodal knowledge of orientation

Multifeatural shape processing in rats engaged in invariant visual object recognition

The ability to recognize objects despite substantial variation in their appearance (e.g., because of position or size changes) represents such a formidable computational feat that it is widely assumed to be unique to primates. Such an assumption has restricted the investigation of its neuronal underpinnings to primate studies, which allow only a limited range of experimental approaches. In recent years, the increasingly powerful array of optical and molecular tools that has become available in rodents has spurred a renewed interest for rodent models of visual functions. However, evidence of primate-like visual object processing in rodents is still very limited and controversial. Here we show that rats are capable of an advanced recognition strategy, which relies on extracting the most informative object features across the variety of viewing conditions the animals may face. Rat visual strategy was uncovered by applying an image masking method that revealed the features used by the animals to discriminate two objects across a range of sizes, positions, in-depth, and in-plane rotations. Noticeably, rat recognition relied on a combination of multiple features that were mostly preserved across the transformations the objects underwent, and largely overlapped with the features that a simulated ideal observer deemed optimal to accomplish the discrimination task. These results indicate that rats are able to process and efficiently use shape information, in a way that is largely tolerant to variation in object appearance. This suggests that their visual system may serve as a powerful model to study the neuronal substrates of object recognition

Editorial: What can simple brains teach us about how vision works

Vision is the process of extracting behaviorally-relevant information from patterns of light that fall on retina as the eyes sample the outside world. Traditionally, non-human primates have been viewed by many as the animal model-of-choice for investigating the neuronal substrates of visual processing, not only because their visual systems closely mirror our own (e.g., Orban, 2008; Nassi and Callaway, 2009 for a review), but also because it is often assumed that \u201csimpler\u201d brains lack advanced visual processing machinery. However, this narrow view of visual neuroscience ignores the fact that vision is widely distributed throughout the animal kingdom, enabling a wide repertoire of complex behaviors in species from insects to birds, fish, and mammals. Recent years have seen a resurgence of interest in alternative animal models for vision research, such as rodents (see Huberman and Niell, 2011; Zoccolan, 2015 for a review). This resurgence is partly due to the availability of increasingly powerful experimental approaches (e.g., optogenetics and two-photon imaging) that are challenging to apply to their full potential in primates. Meanwhile, even more phylogenetically distant species such as birds, fish, and insects have long been workhorse animal models for gaining insight into the core computations underlying visual processing (see Baier, 2000; Bilotta and Saszik, 2001; Borst et al., 2010; Aptekar and Frye, 2013 for a review ). In many cases, these animal models are valuable precisely because their visual systems are simpler than the primate visual system. Simpler systems are often easier to understand, and studying a diversity of neuronal systems that achieve similar functions can focus attention on those computational principles that are universal and essential. This Research Topic provides a survey of the state of the art in the use of non-primate models of visual functions. It includes original research, methods articles, reviews, and opinions that exploit a variety of animal models (including rodents, birds, fishes and insects) to investigate visual function. The experimental approaches covered by these studies range from psychophysics and electrophysiology to histology and genetics, testifying to the richness and depth of visual neuroscience in non-primate species. Below, we briefly summarize the contributions to this Research Topic

Rats spontaneously perceive global motion direction of drifting plaids

Computing global motion direction of extended visual objects is a hallmark of primate high-level vision. Although neurons selective for global motion have also been found in mouse visual cortex, it remains unknown whether rodents can combine multiple motion signals into global, integrated percepts. To address this question, we trained two groups of rats to discriminate either gratings (G group) or plaids (i.e., superpositions of gratings with different orientations; P group) drifting horizontally along opposite directions. After the animals learned the task, we applied a visual priming paradigm, where presentation of the target stimulus was preceded by the brief presentation of either a grating or a plaid. The extent to which rat responses to the targets were biased by such prime stimuli provided a measure of the spontaneous, perceived similarity between primes and targets. We found that gratings and plaids, when uses as primes, were equally effective at biasing the perception of plaid direction for the rats of the P group. Conversely, for G group, only the gratings acted as effective prime stimuli, while the plaids failed to alter the perception of grating direction. To interpret these observations, we simulated a decision neuron reading out the representations of gratings and plaids, as conveyed by populations of either component or pattern cells (i.e., local or global motion detectors). We concluded that the findings for the P group are highly consistent with the existence of a population of pattern cells, playing a functional role similar to that demonstrated in primates. We also explored different scenarios that could explain the failure of the plaid stimuli to elicit a sizable priming magnitude for the G group. These simulations yielded testable predictions about the properties of motion representations in rodent visual cortex at the single-cell and circuitry level, thus paving the way to future neurophysiology experiments