Download or read book Correlated neuronal activity and its relationship to coding dynamics and network architecture written by Tatjana Tchumatchenko and published by Frontiers E-books. This book was released on 2014-12-03 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlated activity in populations of neurons has been observed in many brain regions and plays a central role in cortical coding, attention, and network dynamics. Accurately quantifying neuronal correlations presents several difficulties. For example, despite recent advances in multicellular recording techniques, the number of neurons from which spiking activity can be simultaneously recorded remains orders magnitude smaller than the size of local networks. In addition, there is a lack of consensus on the distribution of pairwise spike cross correlations obtained in extracellular multi-unit recordings. These challenges highlight the need for theoretical and computational approaches to understand how correlations emerge and to decipher their functional role in the brain.
Download or read book Neural Network Dynamics written by J.G. Taylor and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Dynamics is the latest volume in the Perspectives in Neural Computing series. It contains papers presented at the 1991 Workshop on Complex Dynamics in Neural Networks, held at IIASS in Vietri, Italy. The workshop encompassed a wide range of topics in which neural networks play a fundamental role, and aimed to bridge the gap between neural computation and computational neuroscience. The papers - which have been updated where necessary to include new results - are divided into four sections, covering the foundations of neural network dynamics, oscillatory neural networks, as well as scientific and biological applications of neural networks. Among the topics discussed are: A general analysis of neural network activity; Descriptions of various network architectures and nodes; Correlated neuronal firing; A theoretical framework for analyzing the behaviour of real and simulated neuronal networks; The structural properties of proteins; Nuclear phenomenology; Resonance searches in high energy physics; The investigation of information storage; Visual cortical architecture; Visual processing. Neural Network Dynamics is the first volume to cover neural networks and computational neuroscience in such detail. Although it is primarily aimed at researchers and postgraduate students in the above disciplines, it will also be of interest to researchers in electrical engineering, medicine, psychology and philosophy.
Download or read book The Interplay of Architecture and Correlated Variability in Neuronal Networks written by James Trousdale and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This much is certain: neurons are coupled, and they exhibit covariations in their output. The extent of each does not have a single answer. Moreover, the strength of neuronal correlations, in particular, has been a subject of hot debate within the neuroscience community over the past decade, as advancing recording techniques have made available a lot of new, sometimes seemingly conflicting, datasets. The impact of connectivity and the resulting correlations on the ability of animals to perform necessary tasks is even less well understood. In order to answer relevant questions in these categories, novel approaches must be developed. This work focuses on three somewhat distinct, but inseparably coupled, crucial avenues of research within the broader field of computational neuroscience. First, there is a need for tools which can be applied, both by experimentalists and theorists, to understand how networks transform their inputs. In turn, these tools will allow neuroscientists to tease apart the structure which underlies network activity. The Generalized Thinning and Shift framework, presented in Chapter 4, addresses this need. Next, taking for granted a general understanding of network architecture as well as some grasp of the behavior of its individual units, we must be able to reverse the activity to structure relationship, and understand instead how network structure determines dynamics. We achieve this in Chapters 5 through 7 where we present an application of linear response theory yielding an explicit approximation of correlations in integrate--and--fire neuronal networks. This approximation reveals the explicit relationship between correlations, structure, and marginal dynamics. Finally, we must strive to understand the functional impact of network dynamics and architecture on the tasks that a neural network performs. This need motivates our analysis of a biophysically detailed model of the blow fly visual system in Chapter 8. Our hope is that the work presented here represents significant advances in multiple directions within the field of computational neuroscience.
Download or read book Metastable Dynamics of Neural Ensembles written by Emili Balaguer-Ballester and published by Frontiers Media SA. This book was released on 2018-03-19 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: A classical view of neural computation is that it can be characterized in terms of convergence to attractor states or sequential transitions among states in a noisy background. After over three decades, is this still a valid model of how brain dynamics implements cognition? This book provides a comprehensive collection of recent theoretical and experimental contributions addressing the question of stable versus transient neural population dynamics from complementary angles. These studies showcase recent efforts for designing a framework that encompasses the multiple facets of metastability in neural responses, one of the most exciting topics currently in systems and computational neuroscience.
Download or read book Advances in Neural Computation Machine Learning and Cognitive Research VII written by Boris Kryzhanovsky and published by Springer Nature. This book was released on 2023-11-12 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXV International Conference on Neuroinformatics, held on October 23-27, 2023, in Moscow, Russia.
Download or read book Local Cortical Circuits written by Moshe Abeles and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurophysiologists are often accused by colleagues in the physical sci ences of designing experiments without any underlying hypothesis. This impression is attributable to the ease of getting lost in the ever-increasing sea of professional publications which do not state explicitly the ultimate goal of the research. On the other hand, many of the explicit models for brain function in the past were so far removed from experimental reality that they had very little impact on further research. It seems that one needs much intimate experience with the real nerv-. ous system before a reasonable model can be suggested. It would have been impossible for Copernicus to suggest his model of the solar system without the detailed observations and tabulations of star and planet motion accu mulated by the preceeding generations. This need for intimate experience with the nervous system before daring to put forward some hypothesis about its mechanism of action is especially apparent when theorizing about cerebral cortex function. There is widespread agreement that processing of information in the cor tex is associated with complex spatio-temporal patterns of activity. Yet the vast majority of experimental work is based on single neuron recordings or on recordings made with gross electrodes to which tens of thousands of neurons contribute in an unknown fashion. Although these experiments have taught us a great deal about the organization and function of the cor tex, they have not enabled us to examine the spatio-temporal organization of neuronal activity in any detail.
Download or read book Statistical analysis of multi cell recordings linking population coding models to experimental data written by Matthias Bethge and published by Frontiers E-books. This book was released on 2012-01-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern recording techniques such as multi-electrode arrays and 2-photon imaging are capable of simultaneously monitoring the activity of large neuronal ensembles at single cell resolution. This makes it possible to study the dynamics of neural populations of considerable size, and to gain insights into their computations and functional organization. The key challenge with multi-electrode recordings is their high-dimensional nature. Understanding this kind of data requires powerful statistical techniques for capturing the structure of the neural population responses and their relation with external stimuli or behavioral observations. Contributions to this Research Topic should advance statistical modeling of neural populations. Questions of particular interest include: 1. What classes of statistical methods are most useful for modeling population activity? 2. What are the main limitations of current approaches, and what can be done to overcome them? 3. How can statistical methods be used to empirically test existing models of (probabilistic) population coding? 4. What role can statistical methods play in formulating novel hypotheses about the principles of information processing in neural populations? This Research Topic is connected to a one day workshop at the Computational Neuroscience Meeting 2009 in Berlin (http://www.cnsorg.org/2009/workshops.shtml and http://www.kyb.tuebingen.mpg.de/bethge/workshops/cns2009/)
Download or read book Unifying Causality and Psychology written by Gerald Young and published by Springer. This book was released on 2016-05-17 with total page 962 pages. Available in PDF, EPUB and Kindle. Book excerpt: This magistral treatise approaches the integration of psychology through the study of the multiple causes of normal and dysfunctional behavior. Causality is the focal point reviewed across disciplines. Using diverse models, the book approaches unifying psychology as an ongoing project that integrates genetics, experience, evolution, brain, development, change mechanisms, and so on. The book includes in its integration free will, epitomized as freedom in being. It pinpoints the role of the self in causality and the freedom we have in determining our own behavior. The book deals with disturbed behavior, as well, and tackles the DSM-5 approach to mental disorder and the etiology of psychopathology. Young examines all these topics with a critical eye, and gives many innovative ideas and models that will stimulate thinking on the topic of psychology and causality for decades to come. It is truly integrative and original. Among the topics covered: Models and systems of causality of behavior. Nature and nurture: evolution and complexities. Early adversity, fetal programming, and getting under the skin. Free will in psychotherapy: helping people believe. Causality in psychological injury and law: basics and critics. A Neo-Piagetian/Neo-Eriksonian 25-step (sub)stage model. Unifying Causality and Psychology appeals to the disciplines of psychology, psychiatry, epidemiology, philosophy, neuroscience, genetics, law, the social sciences and humanistic fields, in general, and other mental health fields. Its level of writing makes it appropriate for graduate courses, as well as researchers and practitioners.
Download or read book From Animals to Animats 7 written by Bridget Hallam and published by MIT Press. This book was released on 2002 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior
Download or read book Reproducibility and Rigour in Computational Neuroscience written by Sharon Crook and published by Frontiers Media SA. This book was released on 2020-07-09 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advancing Our Understanding of Structure and Function in the Brain Developing Novel Approaches for Network Inference and Emergent Phenomena written by Chris G. Antonopoulos and published by Frontiers Media SA. This book was released on 2021-02-09 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Models of Neural Networks written by Eytan Domany and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop field (1982).
Download or read book Cognitive Science written by Harald Maurer and published by CRC Press. This book was released on 2021-07-08 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Mind and Brain are usually considered as one and the same nonlinear, complex dynamical system, in which information processing can be described with vector and tensor transformations and with attractors in multidimensional state spaces. Thus, an internal neurocognitive representation concept consists of a dynamical process which filters out statistical prototypes from the sensorial information in terms of coherent and adaptive n-dimensional vector fields. These prototypes serve as a basis for dynamic, probabilistic predictions or probabilistic hypotheses on prospective new data (see the recently introduced approach of "predictive coding" in neurophilosophy). Furthermore, the phenomenon of sensory and language cognition would thus be based on a multitude of self-regulatory complex dynamics of synchronous self-organization mechanisms, in other words, an emergent "flux equilibrium process" ("steady state") of the total collective and coherent neural activity resulting from the oscillatory actions of neuronal assemblies. In perception it is shown how sensory object informations, like the object color or the object form, can be dynamically related together or can be integrated to a neurally based representation of this perceptual object by means of a synchronization mechanism ("feature binding"). In language processing it is shown how semantic concepts and syntactic roles can be dynamically related together or can be integrated to neurally based systematic and compositional connectionist representations by means of a synchronization mechanism ("variable binding") solving the Fodor-Pylyshyn-Challenge. Since the systemtheoretical connectionism has succeeded in modeling the sensory objects in perception as well as systematic and compositional representations in language processing with this vector- and oscillation-based representation format, a new, convincing theory of neurocognition has been developed, which bridges the neuronal and the cognitive analysis level. The book describes how elementary neuronal information is combined in perception and language, so it becomes clear how the brain processes this information to enable basic cognitive performance of the humans.
Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press. This book was released on 2003 with total page 1328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Download or read book Coordinated Neural Activity written by N. Alex Cayco Gajic and published by . This book was released on 2015 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does the activity of populations of neurons encode the signals they receive? Since neurons in vivo are inherently variable, each fixed input to a population will elicit not a deterministic response, but rather a probability distribution of states of the individual neurons. Traditional theories of neural coding rely on single-cell tuning curves that describe the average response of each neuron to stimulus features. Adding complexity to this neuron- by-neuron view is the fact that neural activity is not independent: it is often correlated, reflecting shared input and connectivity. Such "coordinated" activity can have diverse and potentially strong impacts on how neural circuits encode stimuli. In this dissertation, we combine dynamical and statistical tools to examine how single-cell and network properties dynamically generate coordinated neural spiking, and how this affects stimulus coding in populations of cells. First, we show how feedforward connectivity leads to the emergence of a neutrally stable subspace that allows information about input rates to be transmitted through layers. At this critical parameter regime, neural activity is characterized by higher-order interactions, meaning that the activity cannot be described by minimal models including only the lower-order moments (mean and pairwise interactions). Interestingly, recent experiments have also demonstrated the existence of higher-order correlations in the neural activity patterns in retina and cortex. Using maximum entropy techniques, we show that in general populations, higher-order correlations can facilitate the encoding of stimulus information in neural activity patterns. We propose a statistical model for fitting neurophysiological data that incorporates only the most significant higher-order interactions. We apply this model to analyze the statistics of population firing patterns in the lateral geniculate nucleus of awake mice. Finally, we analyze dendritic nonlinearities as a novel mechanism by which intrinsic cell properties can generate higher-order correlations. Together, these results work towards determining the origins of coordinated spiking, understanding its impact on neural coding, and building better tools for quantification in electrophysiological data.
Download or read book Neural Codes and Distributed Representations written by L. F. Abbott and published by MIT Press. This book was released on 1999 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.
Download or read book The Dynamic Brain written by Mingzhou Ding, PhD and published by Oxford University Press. This book was released on 2011-01-18 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is a well-known fact of neurophysiology that neuronal responses to identically presented stimuli are extremely variable. This variability has in the past often been regarded as "noise." At the single neuron level, interspike interval (ISI) histograms constructed during either spontaneous or stimulus evoked activity reveal a Poisson type distribution. These observations have been taken as evidence that neurons are intrinsically "noisy" in their firing properties. In fact, the use of averaging techniques, like post-stimulus time histograms (PSTH) or event-related potentials (ERPs) have largely been justified based on the presence of what was believed to be noise in the neuronal responses. More recent attempts to measure the information content of single neuron spike trains have revealed that a surprising amount of information can be coded in spike trains even in the presence of trial-to-trial variability. Multiple single unit recording experiments have suggested that variability formerly attributed to noise in single cell recordings may instead simply reflect system-wide changes in cellular response properties. These observations raise the possibility that, at least at the level of neuronal coding, the variability seen in single neuron responses may not simply reflect an underlying noisy process. They further raise the very distinct possibility that noise may in fact contain real, meaningful information which is available for the nervous system in information processing. To understand how neurons work in concert to bring about coherent behavior and its breakdown in disease, neuroscientists now routinely record simultaneously from hundreds of different neurons and from different brain areas, and then attempt to evaluate the network activities by computing various interdependence measures, including cross correlation, phase synchronization and spectral coherence. This book examines neuronal variability from theoretical, experimental and clinical perspectives.
