Neural Network Dynamics of Temporal Processing PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Neural Network Dynamics of Temporal Processing PDF full book. Access full book title Neural Network Dynamics of Temporal Processing by Nicholas Hardy. Download full books in PDF and EPUB format.
Author: Nicholas Hardy
Publisher:
ISBN:
Category :
Languages : en
Pages : 153
Get Book Here
Book Description
Time is centrally involved in most tasks the brain performs. However, the neurobiological mechanisms of timing remain a mystery. Signatures of temporal processing related to sensory and motor behavior have been observed in several brain regions and behavioral contexts. This activity is often complex, representing time in the activity of large populations of neurons. A major question is whether this observed activity is generated by a specialized clock in the brain or whether it is arises locally via the emergent dynamics of neural networks. State dependent theories of timing argue for the latter: neural activity evolving over time produces a trajectory of network states that can encode temporal information. In this work, I examine the role of network dynamics in encoding temporal information. Combining mathematical models, in vitro neural recordings, and human psychophysics, the studies presented here describe potential network level mechanisms for timing in the brain. Chapter 2 presents research examining sequential activity observed in brain regions characterized by recurrent connectivity. This study describes a theoretical mechanism that recurrent neural networks may use to autonomously produce sequential activity and encode temporal information. Next, Chapter 3 examines the mechanisms of producing the same complex movement at a variety of speeds, a fundamental feature of motor timing. This study combines theoretical and psychophysical studies to predict and test a novel feature of motor timing: temporal accuracy improves with speed, termed the Weber-speed effect. Finally, Chapter 4 examines how cortical neural networks encode temporal information. Using organotypic slice cultures, this study demonstrates that the cortex processes temporal input patterns in a state dependent manner, supporting theoretical predictions. Taken together, the results of this work strongly support state dependent theories of timing, providing insight into the neural basis temporal processing.
Author: Nicholas Hardy
Publisher:
ISBN:
Category :
Languages : en
Pages : 153
Get Book Here
Book Description
Time is centrally involved in most tasks the brain performs. However, the neurobiological mechanisms of timing remain a mystery. Signatures of temporal processing related to sensory and motor behavior have been observed in several brain regions and behavioral contexts. This activity is often complex, representing time in the activity of large populations of neurons. A major question is whether this observed activity is generated by a specialized clock in the brain or whether it is arises locally via the emergent dynamics of neural networks. State dependent theories of timing argue for the latter: neural activity evolving over time produces a trajectory of network states that can encode temporal information. In this work, I examine the role of network dynamics in encoding temporal information. Combining mathematical models, in vitro neural recordings, and human psychophysics, the studies presented here describe potential network level mechanisms for timing in the brain. Chapter 2 presents research examining sequential activity observed in brain regions characterized by recurrent connectivity. This study describes a theoretical mechanism that recurrent neural networks may use to autonomously produce sequential activity and encode temporal information. Next, Chapter 3 examines the mechanisms of producing the same complex movement at a variety of speeds, a fundamental feature of motor timing. This study combines theoretical and psychophysical studies to predict and test a novel feature of motor timing: temporal accuracy improves with speed, termed the Weber-speed effect. Finally, Chapter 4 examines how cortical neural networks encode temporal information. Using organotypic slice cultures, this study demonstrates that the cortex processes temporal input patterns in a state dependent manner, supporting theoretical predictions. Taken together, the results of this work strongly support state dependent theories of timing, providing insight into the neural basis temporal processing.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 66
Get Book Here
Book Description
Dynamic neural networks & their applications to temporal processing tasks are reviewed in this report. Of special interest is a simple dynamic feed-forward network known as the finite impulse response neural network (FIRNN) and the classification of temporal patterns such as acoustic transients. Basic principles of the FIRNN are presented along with an explanation of how the FIRNN processes temporal information. Other dynamic networks discussed include additional dynamic feed-forward neural networks, locally-recurrent globally feed-forward neural networks, partially-recurrent neural networks, and fully-recurrent neural networks. Application areas to which the various neural network paradigms have been applied include the classification of acoustic transients, prediction of chaotic time series, prediction & classification of speech signals, and modelling of non-linear autoregressive (NAR) and NAR moving-average processes.
Author: J.G. Taylor
Publisher: Springer Science & Business Media
ISBN: 1447120019
Category : Computers
Languages : en
Pages : 378
Get Book Here
Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
The research carried out under this contract focussed on four efforts, all involving the processing of temporal sequences by neural networks (1-3) or the effect of imposing a spatio-temporal gradient on network learning (4): (1) Assessing alternative neural network techniques for problems involving temporal coding. (2) Development of tools for analysing recurrent networks, so that the solutions of successfully trained networks can be better understood; (3) Development of a dynamical systems theory approach to computation in recurrent networks. (4) Development of biologically and cognitively plausible techniques for enhancing training.
Author: Bora Bakal
Publisher:
ISBN:
Category :
Languages : en
Pages : 148
Get Book Here
Book Description
Author: Eytan Domany
Publisher: Springer Science & Business Media
ISBN: 1461243203
Category : Science
Languages : en
Pages : 354
Get Book Here
Book Description
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).
Author: A. Ravishankar Rao
Publisher: Springer Science & Business Media
ISBN: 1461407249
Category : Medical
Languages : en
Pages : 234
Get Book Here
Book Description
A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs. The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function. The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks. This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks
Author: E. Covey
Publisher: Springer Science & Business Media
ISBN: 1461519195
Category : Medical
Languages : en
Pages : 264
Get Book Here
Book Description
Author: Marcel van Gerven
Publisher: Frontiers Media SA
ISBN: 2889454010
Category :
Languages : en
Pages : 220
Get Book Here
Book Description
Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.
Author: Karl H. Pribram
Publisher: Psychology Press
ISBN: 1317780957
Category : Psychology
Languages : en
Pages : 564
Get Book Here
Book Description
The result of the first Appalachian Conference on neurodynamics, this volume focuses on processing in biological neural networks. How do brain processes become organized during decision making? That is, what are the neural antecedents that determine which course of action is to be pursued? Half of the contributions deal with modelling synapto-dendritic and neural ultrastructural processes; the remainder, with laboratory research findings, often cast in terms of the models. The interchanges at the conference and the ensuing publication also provide a foundation for further meetings. These will address how processes in different brain systems, coactive with the neural residues of experience and with sensory input, determine decisions.
