The brain, as an information processing machine, surpasses any man-made computational device, both in terms of its capabilities and its efficiency. Neuroscience research has made great strides since the foundational works of Cajal and Golgi. However, we still have very little understanding about the algorithmic underpinnings of the brain as an information processor. Identifying mechanistic models of the functional building blocks of the brain will have significant impact not just on neuroscience, but also on artificial computational systems. This provides the main motivation for the work presented in this thesis, summarily i) biologically-inspired algorithms that can be efficiently implemented in silico, ii) functional identification of the processing in certain types of neural circuits, and iii) a collaborative ecosystem for brain research in a model organism, towards the synergistic goal of understanding functional mechanisms employed by the brain. First, this thesis provides a highly parallelizable, biologically-inspired, motion detection algorithm that is based upon the temporal processing of the local (spatial) phase of a visual stimulus. The relation of the phase based motion detector to the widely studied Reichardt detector model, is discussed. Examples are provided comparing the performance of the proposed algorithm with the Reichardt detector as well as the optic flow algorithm, which is the workhorse for motion detection in computer vision. Further, it is shown through examples that the phase based motion detection model provides intuitive explanations for reverse-phi based illusory motion percepts. Then, tractable algorithms are presented for decoding with and identification of neural circuits, comprised of processing that can be described by a second-order Volterra kernel (quadratic filter). It is shown that the Reichardt detector, as well as models of cortical complex cells, can be described by this structure. Examples are provided for decoding of visual stimuli encoded by a population of Reichardt detector cells and complex cells, as well as their identification from observed spike times. Further, the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs. Subsequently, a general model that computes the ratio of two non-linear functionals, each comprising linear (first order Volterra kernel) and quadratic (second-order Volterra kernel) filters, is proposed. It is shown that, even under these highly non-linear operations, a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus. Tractable algorithms are devised to identify the divisive normalization model and examples of identification are provided for both simulated and biological data. Additionally, an extended framework, comprising parallel channels of divisively normalized cells each subjected to further divisive normalization from lateral feedback connections, is proposed. An algorithm is formulated for identifying all the components in this extended framework from controlled stimulus presentation and observed outputs samples. Finally, the thesis puts forward the Fruit Fly Brain Observatory (FFBO), an initiative to enable a collaborative ecosystem for fruit fly brain research. Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies.

The study of cerebellum-like circuits allows many points of entry. These circuits are often involved in very specific systems not found in all animals (for example electrolocation in weakly electric fish) and thus can be studied with a neuroethological approach in mind. There are many cerebellum-like circuits found across the animal kingdom, and so studies of these systems allow us to make interesting comparative observations. Cerebellum-like circuits are involved in computations that touch many domains of theoretical interest - the formation of internal predictions, adaptive filtering, cancellation of self-generated sensory inputs. This latter is linked both conceptually and historically to philosophical questions about the nature of perception and the distinction between the self and the outside world. The computation thought to be performed in cerebellum-like structures is further related, especially through studies of the cerebellum, to theories of motor control and cognition. The cerebellum itself is known to be involved in much more than motor learning, its traditionally assumed function, with particularly interesting links to schizophrenia and to autism. The particular advantage of studying cerbellum-like structures is that they sit at such a rich confluence of interests while being involved in well-defined computations and being accessible at the synaptic, cellular, and circuit levels. In this thesis we present work on two cerebellum-like structures: the electrosensory lobe (ELL) of mormyrid fish and the dorsal cochlear nucleus (DCN) of mice. Recent work in ELL has shown that a temporal basis of granule cells allows the formation of predictions of the sensory consequences of a simple motor act - the electric organ discharge (EOD). Here we demonstrate that such predictions generalize between electric organ discharge rates - an ability crucial to the ethological relevance of such predictions. We develop a model of how such generalization is made possible at the circuit level. In a second section we show that the DCN is able to adaptively cancel self-generated sounds. In the conclusion we discuss some differences between DCN and ELL and suggest future studies of both structures motivated by a reading of different aspects of the machine learning literature.

No description available.

During nervous system development, thousands of distinct neuronal cell types are generated and assembled into highly precise circuits. The proper wiring of these circuits requires that developing neurons recognize their appropriate synaptic partners. Analysis of a vertebrate spinal circuit that controls motor behavior reveals distinct synaptic connections of two types of inhibitory interneurons, a ventral V1 class that synapses with motor neurons and a dorsal dI4 class that selectively synapses with proprioceptive sensory neuron terminals that are located on or in close proximity to motor neurons. What are the molecular and cellular programs that instruct this remarkable synaptic specificity? Are only subsets of these interneurons capable of integrating into this circuit, or do all neurons within the same class behave similarly? The ability to answer such questions, however, is hampered both by the complexity of the spinal cord, where many different neuronal cell types can be found synapsing in the same area; as well as by the challenge of obtaining enough neurons of a particular subtype for analysis. Meanwhile, pluripotent stem cells have emerged as powerful tools for studying neural development, particularly because they can be differentiated to produce large amounts of diverse neuronal populations. Mouse embryonic stem cell-derived neurons can thus be used in a simplified in vitro system to study the development of specific neuronal cell types as well the interactions between defined cell types in a controlled environment. Using stem cell-derived neurons, I investigated how the V1 and dI4 cardinal spinal classes differentiate into molecularly distinct subtypes and acquire cell type-specific functional properties, including synaptic connectivity. In Chapter Two, I describe the production of lineage-based reporter stem cell lines and optimized differentiation protocols for generating V1 and dI4 INs from mouse embryonic stem cells, including confirming that they have molecular and functional characteristics of their in vivo counterparts. In Chapter Three, I show that a well-known V1 interneuron subtype, the Renshaw cell, which mediates recurrent inhibition of motor neurons, can be efficiently generated from stem cell differentiation. Importantly, manipulation of the Notch signaling pathway in V1 progenitors impinges on V1 subtype differentiation and greatly enhances the generation of Renshaw cells. I further show that sustained retinoic acid signaling is critical for the specific development of the Renshaw cell subtype, suggesting that interneuron progenitor domain diversification may also be regulated by spatially-restricted cues during embryonic development. In Chapter Four, using a series of transplantation, rabies virus-based transsynaptic tracing, and optogenetics combined with whole-cell patch-clamp recording approaches, I demonstrate that stem cell-derived Renshaw cells exhibit significant differences in physiology and connectivity compared to other V1 subpopulations, suggesting that synaptic specificity of the Renshaw cell-motor neuron circuit can be modeled and studied in a simplified in vitro co-culture preparation. Finally, in Chapter Five, I describe ongoing investigations into molecular mechanisms of dI4 interneuron subtype diversification, as well as approaches to studying their synaptic specificity with proprioceptive sensory neurons. Overall, my results suggest that our stem cell-cell based system is well-positioned to probe the functional diversity of molecularly-defined cell types. This work represents a novel use of embryonic stem cell-derived neurons for studying inhibitory spinal circuit assembly and will contribute to further understanding of neural circuit formation and function during normal development and potentially in diseased states.

Rapid and efficient escape behaviors in response to noxious sensory stimuli are essential for protection and survival. In Drosophila larvae, the class III (cIII) and class IV (cIV) dendritic arborization (da) neurons detect low-threshold mechanosensory and noxious stimuli, respectively. Their axons project to modality-specific locations in the neuropil, reminiscent of vertebrate dorsal horn organization. Despite extensive characterization of nociceptors across organisms, how noxious stimuli are transformed to the coordinated behaviors that protect animals from harm remains poorly understood. In larvae, noxious mechanical and thermal stimuli trigger an escape behavior consisting of sequential C-shape body bending followed by corkscrew-like rolling, and finally an increase in forward locomotion (escape crawl). The downstream circuitry controlling the sequential coordination of escape responses is largely unknown. This work identifies a population of interneurons in the nerve cord, Down-and-Back (DnB) neurons, that are activated by noxious heat, promote nociceptive behavior, and are required for robust escape responses to noxious stimuli. Activation of DnB neurons can trigger both rolling, and the initial C-shape body bend independent of rolling, revealing modularity in the initial nociceptive responses. Electron microscopic circuit reconstruction shows that DnBs receive direct input from nociceptive and mechanosensory neurons, are presynaptic to pre-motor circuits, and link indirectly to a population of command-like neurons (Goro) that control rolling. DnB activation promotes activity in Goro neurons, and coincident inactivation of Goro neurons prevents the rolling sequence but leaves intact body bending motor responses. Thus, activity from nociceptors to DnB interneurons coordinates modular elements of nociceptive escape behavior. The impact of DnB neurons may not be restricted to synaptic partners, as DnB presynaptic sites accumulate dense-core vesicles, suggesting aminergic or peptidergic signaling. Anatomical analyses show that DnB neurons receive spatially segregated input from cIII mechanosensory and cIV nociceptive neurons. However, DnB neurons do not seem to promote or be required for gentle-touch responses, suggesting a modulatory role for cIII input. Behavioral experiments suggest that cIII input presented prior to cIV input can enhance nociceptive behavior. Moreover, weak co-activation of DnB and cIII neurons can also enhance nociceptive responses, particularly C-shape bending. These results indicate that timing and level of cIII activation might determine its modulatory role. Taken together, these studies describe a novel nociceptive circuit, which integrates nociceptive and mechanosensory inputs, and controls modular motor pathways to promote robust escape behavior. Future work on this circuit could reveal neural mechanisms for sequence transitions, peptidergic modulation of nociception, and developmental mechanisms that control convergence of sensory afferents onto common synaptic partners.

The physical architecture of neural circuits is thought to underlie the computations that give rise to higher order feature sensitivity in the neocortex. Recent technological breakthroughs have allowed the structural and functional investigation of the basic computational units of neural circuits; individual synaptic connections. However, it remains unclear how cortical neurons sample and integrate the thousands of synaptic inputs, supplied by different brain structures, to achieve feature selectivity. Here, I first describe how visual cortical circuits transform the elementary inputs supplied by the periphery into highly diverse, but well-organized, feature representations. By combining and optimizing newly developed techniques to map the functional synaptic connections with defined sources of inputs, I show that the intersection between columnar architecture and dendritic sampling strategies can lead to the selectivity properties of individual neurons: First, in the canonical feedforward circuit, the basal dendrites of a pyramidal neuron utilize unique strategies to sample ON (light increment) and OFF (light decrement) inputs in orientation columns to create the distinctive receptive field structure that is responsible for basic sensitivity to visual spatial location, orientation, spatial frequency, and phase. Second, for long-range horizontal connections, apical dendrites unbiasedly integrate functionally specialized and spatially targeted inputs in different orientation columns, which generates specific axial surround modulation of the receptive field. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Artificial neural networks are computational paradigms which are inspired by biological neural networks (the human brain). Recurrent neural networks (RNNs) are characterized by neuron connections which include feedback paths. This dissertation uses the dynamics of RNN architectures for solving linear and certain nonlinear equations. Neural network with linear dynamics (variants of the well-known Hopfield network) are used to solve systems of linear equations, where the network structure is adapted to match properties of the linear system in question. Nonlinear equations inturn are solved using the dynamics of nonlinear RNNs, which are based on feedforward multilayer perceptrons. Neural networks are well-suited for implementation on special parallel hardware, due to their intrinsic parallelism. The RNNs developed here are implemented on a neural network processor (NNP) designed specifically for fast neural type processing, and are applied to the inverse kinematics problem in robotics, demonstrating their superior performance over alternative approaches.

Thesis (Ph. D.) -- California Institute of Technology, 2010. / Title from home page (viewed 03/03/2010). Advisor and committee chair names found in the thesis' metadata record in the digital repository. Includes bibliographical references.

Many recent studies on the segmental motor control system have employed spike-triggered-averaging (STA) and other forms of cross-correlation to either attribute CNS, reflex, or direct motor effects to the impulses of a single (reference) neuronal spike train or to explore conditions under which pairs of neural units show temporal correlations in their discharge. Our experience with these techniques suggested the need for a control procedure that tests for synchrony between the reference and other spike trains such as to: (1) either preclude that the observed effects are due to spike trains other than or in addition to the reference train; or (2) give insight into the conditions leading to correlated discharge between two units. A motor unit synchronization test based on analysis of EMG waveforms has already been described. We have modified this test for the detection of synchrony between either afferent or efferent signals by analysis of averaged muscle nerve signals rather than EMG waveforms. Our procedure involves use of a multi-unit muscle nerve recording that serves as the input to a signal averager triggered by a spike train from either: (1) a motor unit's EMG; (2) a dorsal root filament or ganglion cell; or (3) a ramdom trigger source. With appropriate delay of the muscle nerve signal input, the non-rectified average of the trigger signal's waveform is compared to the rectified average which contains this waveform together with contributions of all other active unitary events. Additionally, the rectified average is compared to a "randomly" triggered average of the same input signal. On the basis of these recordings, it can be determined, within certain boundary conditions, whether or not any other unitary events are in synchrony with the reference event. Such synchronization is expressed quantitatively in the form of a synchronization index (SI). We evaluated the efficacy of the SI by electronic simulation procedures and by comparing its use to that of a cross-correlation procedure that tests for synchrony on the basis of crosscorrelograms computed between two simultaneously recorded spindle afferent spike trains during brief stretch of a passive muscle at progressively increasing amplitudes (5 - 100um). These experiments revealed that the SI is a sensitive test of afferent synchrony in the passive muscle provided the spike trains of interest have a signal-to-noise (S/N) ratio > 0.2 in the muscle nerve recording and that it is recognized that the detectable degree of synchronization of a non-reference event is a function of its S/N ratio. For tests on the active muscle, the force levels must remain low. Otherwise increased neuronal activity in the muscle nerve recording decreases the S/N ratio of individual spike trains. Thus, despite restrictive (but predictable) boundary conditions, the SI test can contribute importantly to select conclusions drawn from cross-correlation studies.

No description available.