Learning In Spiking Neural Networks

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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Author : Nikola K. Kasabov
Publisher : Springer
ISBN 13 : 3662577151
Total Pages : 742 pages
Book Rating : 4.6/5 (625 download)

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Book Synopsis Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence by : Nikola K. Kasabov

Download or read book Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence written by Nikola K. Kasabov and published by Springer. This book was released on 2018-08-29 with total page 742 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.

Learning in Spiking Neural Networks

Author : Răzvan V. Florian (Doctorat în informatică.)
Publisher :
ISBN 13 :
Total Pages : 204 pages
Book Rating : 4.:/5 (895 download)

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Book Synopsis Learning in Spiking Neural Networks by : Răzvan V. Florian (Doctorat în informatică.)

Download or read book Learning in Spiking Neural Networks written by Răzvan V. Florian (Doctorat în informatică.) and published by . This book was released on 2009 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Spiking Neural Network Learning, Benchmarking, Programming and Executing

Author : Guoqi Li
Publisher : Frontiers Media SA
ISBN 13 : 2889637670
Total Pages : 234 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Spiking Neural Network Learning, Benchmarking, Programming and Executing by : Guoqi Li

Download or read book Spiking Neural Network Learning, Benchmarking, Programming and Executing written by Guoqi Li and published by Frontiers Media SA. This book was released on 2020-06-05 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Spiking Neuron Models

Author : Wulfram Gerstner
Publisher : Cambridge University Press
ISBN 13 : 9780521890793
Total Pages : 498 pages
Book Rating : 4.8/5 (97 download)

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Book Synopsis Spiking Neuron Models by : Wulfram Gerstner

Download or read book Spiking Neuron Models written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2002-08-15 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

Learning in Spiking Neural Networks

Author : Sergio Davies
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Learning in Spiking Neural Networks by : Sergio Davies

Download or read book Learning in Spiking Neural Networks written by Sergio Davies and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

How to Build a Brain

Author : Chris Eliasmith
Publisher : Oxford University Press
ISBN 13 : 0199794693
Total Pages : 475 pages
Book Rating : 4.1/5 (997 download)

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Book Synopsis How to Build a Brain by : Chris Eliasmith

Download or read book How to Build a Brain written by Chris Eliasmith and published by Oxford University Press. This book was released on 2013-04-16 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to Build a Brain provides a detailed exploration of a new cognitive architecture - the Semantic Pointer Architecture - that takes biological detail seriously, while addressing cognitive phenomena. Topics ranging from semantics and syntax, to neural coding and spike-timing-dependent plasticity are integrated to develop the world's largest functional brain model.

Principles of Computational Modelling in Neuroscience

Author : David Sterratt
Publisher : Cambridge University Press
ISBN 13 : 1108483143
Total Pages : 553 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Principles of Computational Modelling in Neuroscience by : David Sterratt

Download or read book Principles of Computational Modelling in Neuroscience written by David Sterratt and published by Cambridge University Press. This book was released on 2023-10-05 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.

Neuronal Dynamics

Author : Wulfram Gerstner
Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Neuronal Dynamics by : Wulfram Gerstner

Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

SpiNNaker - A Spiking Neural Network Architecture

Author : Steve Furber
Publisher : NowOpen
ISBN 13 : 9781680836523
Total Pages : 352 pages
Book Rating : 4.8/5 (365 download)

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Book Synopsis SpiNNaker - A Spiking Neural Network Architecture by : Steve Furber

Download or read book SpiNNaker - A Spiking Neural Network Architecture written by Steve Furber and published by NowOpen. This book was released on 2020-03-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This books tells the story of the origins of the world's largest neuromorphic computing platform, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over

Learning Deep Architectures for AI

Author : Yoshua Bengio
Publisher : Now Publishers Inc
ISBN 13 : 1601982941
Total Pages : 145 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Learning Deep Architectures for AI by : Yoshua Bengio

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Tutorial on Neural Systems Modeling

Author : Thomas J. Anastasio
Publisher : Sinauer
ISBN 13 : 9780878933396
Total Pages : 0 pages
Book Rating : 4.9/5 (333 download)

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Book Synopsis Tutorial on Neural Systems Modeling by : Thomas J. Anastasio

Download or read book Tutorial on Neural Systems Modeling written by Thomas J. Anastasio and published by Sinauer. This book was released on 2010-03-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For students of neuroscience and cognitive science who wish to explore the functioning of the brain further, but lack an extensive background in computer programming or maths, this new book makes neural systems modelling truly accessible. Short, simple MATLAB computer programs give readers all the experience necessary to run their own simulations.

Spike-timing dependent plasticity

Author : Henry Markram
Publisher : Frontiers E-books
ISBN 13 : 2889190439
Total Pages : 575 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Spike-timing dependent plasticity by : Henry Markram

Download or read book Spike-timing dependent plasticity written by Henry Markram and published by Frontiers E-books. This book was released on with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hebb's postulate provided a crucial framework to understand synaptic alterations underlying learning and memory. Hebb's theory proposed that neurons that fire together, also wire together, which provided the logical framework for the strengthening of synapses. Weakening of synapses was however addressed by "not being strengthened", and it was only later that the active decrease of synaptic strength was introduced through the discovery of long-term depression caused by low frequency stimulation of the presynaptic neuron. In 1994, it was found that the precise relative timing of pre and postynaptic spikes determined not only the magnitude, but also the direction of synaptic alterations when two neurons are active together. Neurons that fire together may therefore not necessarily wire together if the precise timing of the spikes involved are not tighly correlated. In the subsequent 15 years, Spike Timing Dependent Plasticity (STDP) has been found in multiple brain brain regions and in many different species. The size and shape of the time windows in which positive and negative changes can be made vary for different brain regions, but the core principle of spike timing dependent changes remain. A large number of theoretical studies have also been conducted during this period that explore the computational function of this driving principle and STDP algorithms have become the main learning algorithm when modeling neural networks. This Research Topic will bring together all the key experimental and theoretical research on STDP.

Parallel Problem Solving from Nature – PPSN XVI

Author : Thomas Bäck
Publisher : Springer Nature
ISBN 13 : 3030581128
Total Pages : 753 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Parallel Problem Solving from Nature – PPSN XVI by : Thomas Bäck

Download or read book Parallel Problem Solving from Nature – PPSN XVI written by Thomas Bäck and published by Springer Nature. This book was released on 2020-09-02 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.

Pulsed Neural Networks

Author : Wolfgang Maass
Publisher : MIT Press
ISBN 13 : 9780262632218
Total Pages : 414 pages
Book Rating : 4.6/5 (322 download)

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Book Synopsis Pulsed Neural Networks by : Wolfgang Maass

Download or read book Pulsed Neural Networks written by Wolfgang Maass and published by MIT Press. This book was released on 2001-01-26 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador

Learning in Spiking Neural Networks

Author : Sergio Davies
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Learning in Spiking Neural Networks by : Sergio Davies

Download or read book Learning in Spiking Neural Networks written by Sergio Davies and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives are the understanding of the mechanisms underlying the human brain, how to reproduce them in an artificial environment, and how drugs interact with them. Multiple neural models have been proposed, each with their peculiarities, from the very complex and biologically realistic Hodgkin-Huxley neuron model to the very simple 'leaky integrate-and-fire' neuron. However, despite numerous attempts to understand the learning behaviour of the synapses, few models have been proposed. Spike-Timing-Dependent Plasticity (STDP) is one of the most relevant and biologically plausible models, and some variants (such as the triplet-based STDP rule) have been proposed to accommodate all biological observations. The research presented in this thesis focuses on a novel learning rule, based on the spike-pair STDP algorithm, which provides a statistical approach with the advantage of being less computationally expensive than the standard STDP rule, and is therefore suitable for its implementation on stand-alone computational units. The environment in which this research work has been carried out is the SpiNNaker project, which aims to provide a massively parallel computational substrate for neural simulation. To support such research, two other topics have been addressed: the first is a way to inject spikes into the SpiNNaker system through a non-real-time channel such as the Ethernet link, synchronising with the timing of the SpiNNaker system. The second research topic is focused on a way to route spikes in the SpiNNaker system based on populations of neurons. The three topics are presented in sequence after a brief introduction to the SpiNNaker project. Future work could include structural plasticity (also known as synaptic rewiring); here, during the simulation of neural networks on the SpiNNaker system, axons, dendrites and synapses may be grown or pruned according to biological observations.

Fuzzy Spiking Neural Networks

Author : Haider Raza
Publisher : GRIN Verlag
ISBN 13 : 3656097259
Total Pages : 113 pages
Book Rating : 4.6/5 (56 download)

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Book Synopsis Fuzzy Spiking Neural Networks by : Haider Raza

Download or read book Fuzzy Spiking Neural Networks written by Haider Raza and published by GRIN Verlag. This book was released on 2012-01-11 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2011 in the subject Engineering - Computer Engineering, grade: 8.84, Manav Rachna International University, course: Master of Technology (M.Tech), language: English, abstract: This dissertation presents an introductory knowledge to computational neuroscience and major emphasize on the branch of computational neuroscience called Spiking Neural Networks (SNNs). SNNs are also called the third generation neural networks. It has become now a major field of Soft Computing. In this we talk about the temporal characteristics' of neuron and studied the dynamics of it. We have presented SNNs architecture with fuzzy reasoning capability. Neuron selectivity is facilitated using receptive fields that enable individual neurons to be responsive to certain spike train frequencies and behave in a similar manner as fuzzy membership functions. The network of SNNs consists of three layers that is input, hidden and output layer. The topology of this network is based on Radial basis Network, which can be regarded as universal approximators. The input layer receives the input in the form of frequency which produces the spikes through linear encoding. There is another method of encoding called Poisson encoding; this encoding is used where the data is large. The hidden layer use Receptive Field (RF) to process the input and thus it is frequency selective. The output layer is only responsible for learning. The learning is based on local learning. The XOR classification problem is used to test the capabilities of the network. There is a problem of continuous updating of weight arises. This issue of weight is resolved by using STDP window and fuzzy reasoning. The dissertation demonstrates how it is possible to obtain fuzzy reasoning capability from biological models of spiking neurons. The fuzzy spiking neural network implements fuzzy rules by configuration of receptive fields, antecedent conjunction with excitatory and inhibitory connections, and inferenc

The NEURON Book

Author : Nicholas T. Carnevale
Publisher : Cambridge University Press
ISBN 13 : 1139447831
Total Pages : 399 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis The NEURON Book by : Nicholas T. Carnevale

Download or read book The NEURON Book written by Nicholas T. Carnevale and published by Cambridge University Press. This book was released on 2006-01-12 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.