Correlated Neuronal Activity And Its Relationship To Coding Dynamics And Network Architecture
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Correlated Neuronal Activity And Its Relationship To Coding Dynamics And Network Architecture
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Author : Tatjana Tchumatchenko
language : en
Publisher: Frontiers E-books
Release Date : 2014-12-03
Correlated Neuronal Activity And Its Relationship To Coding Dynamics And Network Architecture written by Tatjana Tchumatchenko and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-03 with Brain function categories.
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.
Neural Network Dynamics
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Author : J.G. Taylor
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Neural Network Dynamics written by J.G. Taylor and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
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.
The Interplay Of Architecture And Correlated Variability In Neuronal Networks
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Author : James Trousdale
language : en
Publisher:
Release Date : 2013
The Interplay Of Architecture And Correlated Variability In Neuronal Networks written by James Trousdale and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Mathematics categories.
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.
Models Of Neural Networks
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Author : Eytan Domany
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Models Of Neural Networks written by Eytan Domany and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Science categories.
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).
Metastable Dynamics Of Neural Ensembles
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Author : Emili Balaguer-Ballester
language : en
Publisher: Frontiers Media SA
Release Date : 2018-03-19
Metastable Dynamics Of Neural Ensembles written by Emili Balaguer-Ballester and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-19 with categories.
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.
Collective Activity In Neural Networks
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Author : Yu Hu
language : en
Publisher:
Release Date : 2014
Collective Activity In Neural Networks written by Yu Hu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Correlated, or synchronized, spiking activity among pairs of neurons is widely observed across the nervous system. How do these correlations arise from the dynamics of neural networks? The interconnectivity of neurons is one likely contributor. Moreover, recent experiments found that certain connectivity patterns, or motifs, in biological neural networks occur at markedly different frequencies than what would be expected if the neurons were randomly connected. Connecting these ideas, we find that the average correlation in a network is determined by the statistical prevalence of two families of motifs. We derive an expression for correlation that reveals the contributions of each motif in order. The prevalence of a motif is quantified by a new graphical quantity we call the motif cumulant. Importantly, in practical examples motif cumulants decay quickly with the motif size. Therefore frequencies of motifs involving only a few cells are often enough to predict the average correlation. We find that this link between local connectivity structures and global correlation is strongly affected by heterogeneity in connectivity, but can be recovered by a network-partitioning method. Next, we study the impact of correlated activity on the accuracy of information encoded by neural populations --- that is, on the accuracy of the neural code. First, we generalize an existing result to prove a rule that describes accuracy-improving correlations via their sign. For large populations, however, we find that this rule is only useful for weak correlations; many diversely structured correlations can improve coding accuracy. However, there is organization within this diversity: we prove that the optimal correlations must lie on boundaries of the allowed set of correlations. Finally, we study how motifs can change the signal-filtering property of a network. We find that the transfer function for the whole network is determined by chain motif cumulants. Moreover, the way that the effects of these motifs combine has an intriguing structure. This can be illustrated with a diagram where each cumulant is a feedback link, a process that reveals how chain motifs shape the transfer function to produce varied spectral and temporal features. We provide examples where these features are employed to sustain functions such as extending the response time constant and signal de-noising.
Advances In Neural Computation Machine Learning And Cognitive Research Vii
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Author : Boris Kryzhanovsky
language : en
Publisher: Springer Nature
Release Date : 2023-11-12
Advances In Neural Computation Machine Learning And Cognitive Research Vii written by Boris Kryzhanovsky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-12 with Technology & Engineering categories.
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.
Neural Networks From Biology To High Energy Physics Proceedings Of The 2nd Workshop
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Author : Omar Benhar
language : en
Publisher: World Scientific
Release Date : 1993-10-16
Neural Networks From Biology To High Energy Physics Proceedings Of The 2nd Workshop written by Omar Benhar and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-10-16 with categories.
Neural network models, in addition to being of intrinsic theoretical interest, have also proved to be a useful framework in which issues in theoretical biology can be put into perspective. These issues include, amongst others, modelling the activity of the cortex and the study of protein folding. More recently, neural network models have been extensively investigated as tools for data analysis in high energy physics experiments. These workshop proceedings reflect the strongly interdisciplinary character of the field and provide an updated overview of recent developments.
Unifying Causality And Psychology
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Author : Gerald Young
language : en
Publisher: Springer
Release Date : 2016-05-17
Unifying Causality And Psychology written by Gerald Young and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-17 with Psychology categories.
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.
Advancing Our Understanding Of Structure And Function In The Brain Developing Novel Approaches For Network Inference And Emergent Phenomena
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Author : Chris G. Antonopoulos
language : en
Publisher: Frontiers Media SA
Release Date : 2021-02-09
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 has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Science categories.