Download or read book Recurrent Network Dynamics Modulate Orientation Tuning in Primary Visual Cortex written by Maria del Mar Quiroga and published by . This book was released on 2015 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Orientation Selectivity in Inhibition dominated Networks of Spiking Neurons Effect of Single Neuron Properties and Network Dynamics written by Sadra Sadeh and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity
Download or read book Recurrent Network Dynamics in Visual Cortex written by Jeroen Joukes and published by . This book was released on 2016 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a data-driven computational approach for studying neural systems. In this approach one starts with experimental stimuli (inputs) and measured neuronal responses (outputs). The relationship between the inputs and outputs is modeled with an artificial recurrent neural network (ARNN). A detailed investigation of the network weights and response properties of the connected elements, together with simulated experiments performed on the ARNN leads to significant new insights and new hypotheses about the underlying neural mechanisms. We first applied this approach to motion responses of neurons in the macaque middle temporal area (MT). This provided the novel insight that recurrent networks dynamics can explain complex motion tuned response dynamics found in MT neurons, without the need for feedforward temporal delay lines. In our second study we used this approach to model the early visual form processing pathway of the macaque brain. Neurons in the secondary visual cortex (V2) were stimulated with textured stimuli designed to probe the visual systems for complex visual shapes. The approach led to the novel hypothesis that selectivity for complex form depends on selectivity for motion. For the third study we extended the approach by taking advantage of chronically implanted microelectrode arrays (FMA) in primary visual cortex (V1) of the awake behaving macaque. With the FMA we collected V1 responses on day one, fitted an ARNN, explored the detailed properties of the ARNN the following days, and tested model predictions with a V1 validation experiment within the same week. We found that V1 selectivity for form is much more complex than commonly thought and includes spatiotemporal interactions between multiple hotspots in the receptive field. With this approach we found complex V1 tuning properties that are currently thought to primarily arise higher up in the visual processing stream. We conclude that ARNNs can offer a useful tool set for systems neuroscience; the powerful computational approach, together with carefully designed experiments, provides novel hypotheses and insights into the complexity of neural function.
Download or read book Models of Neural Networks IV written by J. Leo van Hemmen and published by Springer Science & Business Media. This book was released on 2012-11-09 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, with chapters by leading researchers in the field, is devoted to early vision and attention, that is, to the first stages of visual information processing. This state-of-the-art look at biological neural networks spans the many subfields, such as computational and experimental neuroscience; anatomy and physiology; visual information processing and scene segmentation; perception at illusory contours; control of visual attention; and paradigms for computing with spiking neurons.
Download or read book Propagation of Orientation Selectivity in a Spiking Network Model of Layered Primary Visual Cortex written by Benjamin Merkt and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Neurons in different layers of sensory cortex generally have different functional properties. But what determines firing rates and tuning properties of neurons in different layers? Orientation selectivity in primary visual cortex (V1) is an interesting case to study these questions. Thalamic projections essentially determine the preferred orientation of neurons that receive direct input. But how is this tuning propagated though layers, and how can selective responses emerge in layers that do not have direct access to the thalamus? Here we combine numerical simulations with mathematical analyses to address this problem. We find that a large-scale network, which just accounts for experimentally measured layer and cell-type specific connection probabilities, yields firing rates and orientation selectivities matching electrophysiological recordings in rodent V1 surprisingly well. Further analysis, however, is complicated by the fact that neuronal responses emerge in a dynamic fashion and cannot be directly inferred from static neuroanatomy, as some connections tend to have unintuitive effects due to recurrent interactions and strong feedback loops. These emergent phenomena can be understood by linearizing and coarse-graining. In fact, we were able to derive a low-dimensional linear dynamical system effectively describing stimulus-driven activity layer by layer. This low-dimensional system explains layer-specific firing rates and orientation tuning by accounting for the different gain factors of the aggregate system. Our theory can also be used to design novel optogenetic stimulation experiments, thus facilitating further exploration of the interplay between connectivity and function
Download or read book The Neural Basis of Early Vision written by A. Kaneko and published by Springer Science & Business Media. This book was released on 2013-03-13 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in recent decades have elucidated signal transduction in the retina and the function of the visual cortex. The highly flexible nature of neural circuits in the visual cortex especially during the critical period has been an interesting subject for studying neural plasticity and development. Recent advances in the visual neurosciences of the vertebrate retina and the visual cortex were discussed during the 12th Keio International Symposium for Life Science and Medicine, meeting jointly with Vision Forum 2002. Contributions to the symposium collected in this volume reflect the convergence of physiological, cell biological, molecular, mathematical, and clinical approaches. The book covers topics ranging from phototransduction to visual information processing in the primary visual cortex, and includes clinical studies on hereditary night blindness, creating a valuable source of information for researchers and clinicians in the visual neurosciences.
Download or read book Modeling of Orientation Mapping Based on Optical Recordings of the Feline Visual Cortex written by Danil M. Gorinevski and published by . This book was released on 1998 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Cat Primary Visual Cortex written by Bertram Payne and published by Academic Press. This book was released on 2001-11-17 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by experts on the forefront of investigations of brain function, vision, and perception, the material presented is of an unparalleled scientific quality, and shows that analyses of enormous breadth and sophistication are required to probe the structure and function of brain regions. The articles are highly persuasive in showing what can be achieved by carrying out careful and imaginative experiments. The Cat Primary Visual Cortex should emerge as essential reading for all those interested in cerebral cortical processing of visual signals or researching or working in any field of vision. - Comprehensive account of cat primary visual cortex - Generous use of illustrations including color - Covers research from structure to connections to functions - Chapters by leaders in the field - Topics presneted on multiple, compatible levels
Download or read book Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies written by Sadra Sadeh and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity
Download or read book Vision and Brain written by Stephen Grossberg and published by Elsevier Science & Technology. This book was released on 2004 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: An interdisciplinary book that surveys experimental and theoretical discoveries concerning how a brain sees and how insights about biological vision can be used to develop more effective algorithms for image processing in technology.
Download or read book Modern Techniques in Neuroscience Research written by Uwe Windhorst and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 1336 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the techniques used in modern neuroscience research with the emphasis on showing how different techniques can optimally be combined in the study of problems that arise at some levels of nervous system organization. This is essentially a working tool for the scientist in the laboratory and clinic, providing detailed step-by-step protocols with tips and recommendations. Most chapters and protocols are organized such that they can be used independently, while cross-references between the chapters, a glossary, a list of suppliers and appendices provide further help.
Download or read book Inhibitory Synaptic Plasticity written by Melanie A. Woodin and published by Springer Science & Business Media. This book was released on 2010-11-02 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume will explore the most recent findings on cellular mechanisms of inhibitory plasticity and its functional role in shaping neuronal circuits, their rewiring in response to experience, drug addiction and in neuropathology. Inhibitory Synaptic Plasticity will be of particular interest to neuroscientists and neurophysiologists.
Download or read book Coherent Behavior in Neuronal Networks written by Krešimir Josic and published by Springer Science & Business Media. This book was released on 2009-08-22 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent experimental research advances have led to increasingly detailed descriptions of how networks of interacting neurons process information. With these developments, it has become clear that dynamic network behaviors underlie information processing, and that the observed activity patterns cannot be fully explained by simple concepts such as synchrony and phase locking. These new insights raise significant challenges and offer exciting opportunities for experimental and theoretical neuroscientists. Coherent Behavior in Neuronal Networks features a review of recent research in this area from some of the world’s foremost experts on systems neuroscience. The book presents novel methodologies and interdisciplinary perspectives, and will serve as an invaluable resource to the research community. Highlights include the results of interdisciplinary collaborations and approaches as well as topics, such as the interplay of intrinsic and synaptic dynamics in producing coherent neuronal network activity and the roles of globally coherent rhythms and oscillations in the coordination of distributed processing, that are of significant research interest but have been underrepresented in the review literature. With its cutting-edge mathematical, statistical, and computational techniques, this volume will be of interest to all researchers and students in the field of systems neuroscience.
Download or read book An Introductory Course in Computational Neuroscience written by Paul Miller and published by MIT Press. This book was released on 2018-10-09 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.
Download or read book Orientation Tuning in Primary Visual Cortex written by Andrew Teich and published by . This book was released on 2005 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Emergent neural computation from the interaction of different forms of plasticity written by Cristina Savin and published by Frontiers Media SA. This book was released on 2016-03-22 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the propagation of neural activity through synapses, to the integration of signals in the dendritic arbor, and the processes determining action potential generation, virtually all aspects of neural processing are plastic. This plasticity underlies the remarkable versatility and robustness of cortical circuits: it enables the brain to learn regularities in its sensory inputs, to remember the past, and to recover function after injury. While much of the research into learning and memory has focused on forms of Hebbian plasticity at excitatory synapses (LTD/LTP, STDP), several other plasticity mechanisms have been characterized experimentally, including the plasticity of inhibitory circuits (Kullmann, 2012), synaptic scaling (Turrigiano, 2011) and intrinsic plasticity (Zhang and Linden, 2003). However, our current understanding of the computational roles of these plasticity mechanisms remains rudimentary at best. While traditionally they are assumed to serve a homeostatic purpose, counterbalancing the destabilizing effects of Hebbian learning, recent work suggests that they can have a profound impact on circuit function (Savin 2010, Vogels 2011, Keck 2012). Hence, theoretical investigation into the functional implications of these mechanisms may shed new light on the computational principles at work in neural circuits. This Research Topic of Frontiers in Computational Neuroscience aims to bring together recent advances in theoretical modeling of different plasticity mechanisms and of their contributions to circuit function. Topics of interest include the computational roles of plasticity of inhibitory circuitry, metaplasticity, synaptic scaling, intrinsic plasticity, plasticity within the dendritic arbor and in particular studies on the interplay between homeostatic and Hebbian plasticity, and their joint contribution to network function.
Download or read book Sensory Cue Integration written by Julia Trommershauser and published by Computational Neuroscience. This book was released on 2011 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The probabilistic approaches elaborated in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed
