A New Perspective On Memorization In Recurrent Networks Of Spiking Neurons

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A New Perspective on Memorization in Recurrent Networks of Spiking Neurons

Author : Patrick Murer
Publisher : BoD – Books on Demand
ISBN 13 : 3866287585
Total Pages : 230 pages
Book Rating : 4.8/5 (662 download)

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Book Synopsis A New Perspective on Memorization in Recurrent Networks of Spiking Neurons by : Patrick Murer

Download or read book A New Perspective on Memorization in Recurrent Networks of Spiking Neurons written by Patrick Murer and published by BoD – Books on Demand. This book was released on 2022-05-13 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies the capability of spiking recurrent neural network models to memorize dynamical pulse patterns (or firing signals). In the first part, discrete-time firing signals (or firing sequences) are considered. A recurrent network model, consisting of neurons with bounded disturbance, is introduced to analyze (simple) local learning. Two modes of learning/memorization are considered: The first mode is strictly online, with a single pass through the data, while the second mode uses multiple passes through the data. In both modes, the learning is strictly local (quasi-Hebbian): At any given time step, only the weights between the neurons firing (or supposed to be firing) at the previous time step and those firing (or supposed to be firing) at the present time step are modified. The main result is an upper bound on the probability that the single-pass memorization is not perfect. It follows that the memorization capacity in this mode asymptotically scales like that of the classical Hopfield model (which, in contrast, memorizes static patterns). However, multiple-rounds memorization is shown to achieve a higher capacity with an asymptotically nonvanishing number of bits per connection/synapse. These mathematical findings may be helpful for understanding the functionality of short-term memory and long-term memory in neuroscience. In the second part, firing signals in continuous-time are studied. It is shown how firing signals, containing firings only on a regular time grid, can be (robustly) memorized with a recurrent network model. In principle, the corresponding weights are obtained by supervised (quasi-Hebbian) multi-pass learning. The asymptotic memorization capacity is a nonvanishing number measured in bits per connection/synapse as its discrete-time analogon. Furthermore, the timing robustness of the memorized firing signals is investigated for different disturbance models. The regime of disturbances, where the relative occurrence-time of the firings is preserved over a long time span, is elaborated for the various disturbance models. The proposed models have the potential for energy efficient self-timed neuromorphic hardware implementations.

Composite NUV Priors and Applications

Author : Raphael Urs Keusch
Publisher : BoD – Books on Demand
ISBN 13 : 3866287682
Total Pages : 275 pages
Book Rating : 4.8/5 (662 download)

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Book Synopsis Composite NUV Priors and Applications by : Raphael Urs Keusch

Download or read book Composite NUV Priors and Applications written by Raphael Urs Keusch and published by BoD – Books on Demand. This book was released on 2022-08-19 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Normal with unknown variance (NUV) priors are a central idea of sparse Bayesian learning and allow variational representations of non-Gaussian priors. More specifically, such variational representations can be seen as parameterized Gaussians, wherein the parameters are generally unknown. The advantage is apparent: for fixed parameters, NUV priors are Gaussian, and hence computationally compatible with Gaussian models. Moreover, working with (linear-)Gaussian models is particularly attractive since the Gaussian distribution is closed under affine transformations, marginalization, and conditioning. Interestingly, the variational representation proves to be rather universal than restrictive: many common sparsity-promoting priors (among them, in particular, the Laplace prior) can be represented in this manner. In estimation problems, parameters or variables of the underlying model are often subject to constraints (e.g., discrete-level constraints). Such constraints cannot adequately be represented by linear-Gaussian models and generally require special treatment. To handle such constraints within a linear-Gaussian setting, we extend the idea of NUV priors beyond its original use for sparsity. In particular, we study compositions of existing NUV priors, referred to as composite NUV priors, and show that many commonly used model constraints can be represented in this way.

Using Local State Space Model Approximation for Fundamental Signal Analysis Tasks

Author : Elizabeth Ren
Publisher : BoD – Books on Demand
ISBN 13 : 3866287925
Total Pages : 288 pages
Book Rating : 4.8/5 (662 download)

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Book Synopsis Using Local State Space Model Approximation for Fundamental Signal Analysis Tasks by : Elizabeth Ren

Download or read book Using Local State Space Model Approximation for Fundamental Signal Analysis Tasks written by Elizabeth Ren and published by BoD – Books on Demand. This book was released on 2023-05-26 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: With increasing availability of computation power, digital signal analysis algorithms have the potential of evolving from the common framewise operational method to samplewise operations which offer more precision in time. This thesis discusses a set of methods with samplewise operations: local signal approximation via Recursive Least Squares (RLS) where a mathematical model is fit to the signal within a sliding window at each sample. Thereby both the signal models and cost windows are generated by Autonomous Linear State Space Models (ALSSMs). The modeling capability of ALSSMs is vast, as they can model exponentials, polynomials and sinusoidal functions as well as any linear and multiplicative combination thereof. The fitting method offers efficient recursions, subsample precision by way of the signal model and additional goodness of fit measures based on the recursively computed fitting cost. Classical methods such as standard Savitzky-Golay (SG) smoothing filters and the Short-Time Fourier Transform (STFT) are united under a common framework. First, we complete the existing framework. The ALSSM parameterization and RLS recursions are provided for a general function. The solution of the fit parameters for different constraint problems are reviewed. Moreover, feature extraction from both the fit parameters and the cost is detailed as well as examples of their use. In particular, we introduce terminology to analyze the fitting problem from the perspective of projection to a local Hilbert space and as a linear filter. Analytical rules are given for computation of the equivalent filter response and the steady-state precision matrix of the cost. After establishing the local approximation framework, we further discuss two classes of signal models in particular, namely polynomial and sinusoidal functions. The signal models are complementary, as by nature, polynomials are suited for time-domain description of signals while sinusoids are suited for the frequency-domain. For local approximation of polynomials, we derive analytical expressions for the steady-state covariance matrix and the linear filter of the coefficients based on the theory of orthogonal polynomial bases. We then discuss the fundamental application of smoothing filters based on local polynomial approximation. We generalize standard SG filters to any ALSSM window and introduce a novel class of smoothing filters based on polynomial fitting to running sums.

The Role of Synaptic Tagging and Capture for Memory Dynamics in Spiking Neural Networks

Author : Jannik Luboeinski
Publisher :
ISBN 13 :
Total Pages : 201 pages
Book Rating : 4./5 ( download)

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Book Synopsis The Role of Synaptic Tagging and Capture for Memory Dynamics in Spiking Neural Networks by : Jannik Luboeinski

Download or read book The Role of Synaptic Tagging and Capture for Memory Dynamics in Spiking Neural Networks written by Jannik Luboeinski and published by . This book was released on 2021-09-02 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Memory serves to process and store information about experiences such that this information can be used in future situations. The transfer from transient storage into long-term memory, which retains information for hours, days, and even years, is called consolidation. In brains, information is primarily stored via alteration of synapses, so-called synaptic plasticity. While these changes are at first in a transient early phase, they can be transferred to a late phase, meaning that they become stabilized over the course of several hours. This stabilization has been explained by so-called synaptic tagging and capture (STC) mechanisms. To store and recall memory representations, emergent dynamics arise from the synaptic structure of recurrent networks of neurons. This happens through so-called cell assemblies, which feature particularly strong synapses. It has been proposed that the stabilization of such cell assemblies by STC corresponds to so-called synaptic consolidation, which is observed in humans and other animals in the first hours after acquiring a new memory. The exact connection between the physiological mechanisms of STC and memory consolidation remains, however, unclear. It is equally unknown which influence STC mechanisms exert on further cognitive functions that guide behavior. On timescales of minutes to hours (that means, the timescales of STC) such functions include memory improvement, modification of memories, interference and enhancement of similar memories, and transient priming of certain memories. Thus, diverse memory dynamics may be linked to STC, which can be investigated by employing theoretical methods based on experimental data from the neuronal and the behavioral level. In this thesis, we present a theoretical model of STC-based memory consolidation in recurrent networks of spiking neurons, which are particularly suited to reproduce biologically realistic dynamics. Furthermore, we combine the STC mechanisms with calcium dynamics, which have been found to guide the major processes of early-phase synaptic plasticity in vivo. In three included research articles as well as additional sections, we develop this model and investigate how it can account for a variety of behavioral effects. We find that the model enables the robust implementation of the cognitive memory functions mentioned above. The main steps to this are: 1. demonstrating the formation, consolidation, and improvement of memories represented by cell assemblies, 2. showing that neuromodulator-dependent STC can retroactively control whether information is stored in a temporal or rate-based neural code, and 3. examining interaction of multiple cell assemblies with transient and attractor dynamics in different organizational paradigms. In summary, we demonstrate several ways by which STC controls the late-phase synaptic structure of cell assemblies. Linking these structures to functional dynamics, we show that our STC-based model implements functionality that can be related to long-term memory. Thereby, we provide a basis for the mechanistic explanation of various neuropsychological effects. Keywords: synaptic plasticity; synaptic tagging and capture; spiking recurrent neural networks; memory consolidation; long-term memory

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.

Artificial Neural Networks and Machine Learning -- ICANN 2013

Author : Valeri Mladenov
Publisher : Springer
ISBN 13 : 3642407285
Total Pages : 660 pages
Book Rating : 4.6/5 (424 download)

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Book Synopsis Artificial Neural Networks and Machine Learning -- ICANN 2013 by : Valeri Mladenov

Download or read book Artificial Neural Networks and Machine Learning -- ICANN 2013 written by Valeri Mladenov and published by Springer. This book was released on 2013-09-04 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes the proceedings of the 23rd International Conference on Artificial Neural Networks, ICANN 2013, held in Sofia, Bulgaria, in September 2013. The 78 papers included in the proceedings were carefully reviewed and selected from 128 submissions. The focus of the papers is on following topics: neurofinance graphical network models, brain machine interfaces, evolutionary neural networks, neurodynamics, complex systems, neuroinformatics, neuroengineering, hybrid systems, computational biology, neural hardware, bioinspired embedded systems, and collective intelligence.

Neuromorphic Cognitive Systems

Author : Qiang Yu
Publisher : Springer
ISBN 13 : 3319553100
Total Pages : 180 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Neuromorphic Cognitive Systems by : Qiang Yu

Download or read book Neuromorphic Cognitive Systems written by Qiang Yu and published by Springer. This book was released on 2017-05-03 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail.

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.

Improving Associative Memory in a Network of Spiking Neurons

Author : Russell I. Hunter
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (16 download)

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Book Synopsis Improving Associative Memory in a Network of Spiking Neurons by : Russell I. Hunter

Download or read book Improving Associative Memory in a Network of Spiking Neurons written by Russell I. Hunter and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we use computational neural network models to examine the dynamics and functionality of the CA3 region of the mammalian hippocampus. The emphasis of the project is to investigate how the dynamic control structures provided by inhibitory circuitry and cellular modification may effect the CA3 region during the recall of previously stored information. The CA3 region is commonly thought to work as a recurrent auto-associative neural network due to the neurophysiological characteristics found, such as, recurrent collaterals, strong and sparse synapses from external inputs and plasticity between coactive cells. Associative memory models have been developed using various configurations of mathematical artificial neural networks which were first developed over 40 years ago. Within these models we can store information via changes in the strength of connections between simplified model neurons (two-state). These memories can be recalled when a cue (noisy or partial) is instantiated upon the net. The type of information they can store is quite limited due to restrictions caused by the simplicity of the hard-limiting nodes which are commonly associated with a binary activation threshold. We build a much more biologically plausible model with complex spiking cell models and with realistic synaptic properties between cells. This model is based upon some of the many details we now know of the neuronal circuitry of the CA3 region. We implemented the model in computer software using Neuron and Matlab and tested it by running simulations of storage and recall in the network. By building this model we gain new insights into how different types of neurons, and the complex circuits they form, actually work. The mammalian brain consists of complex resistive-capacative electrical circuitry which is formed by the interconnection of large numbers of neurons. A principal cell type is the pyramidal cell within the cortex, which is the main information processor in our neural networks. Pyramidal cells are surrounded by diverse populations of interneurons which have proportionally smaller numbers compared to the pyramidal cells and these form connections with pyramidal cells and other inhibitory cells. By building detailed computational models of recurrent neural circuitry we explore how these microcircuits of interneurons control the flow of information through pyramidal cells and regulate the efficacy of the network. We also explore the effect of cellular modification due to neuronal activity and the effect of incorporating spatially dependent connectivity on the network during recall of previously stored information. In particular we implement a spiking neural network proposed by Sommer and Wennekers (2001). We consider methods for improving associative memory recall using methods inspired by the work by Graham and Willshaw (1995) where they apply mathematical transforms to an artificial neural network to improve the recall quality within the network. The networks tested contain either 100 or 1000 pyramidal cells with 10% connectivity applied and a partial cue instantiated, and with a global pseudo-inhibition. We investigate three methods. Firstly, applying localised disynaptic inhibition which will proportionalise the excitatory post synaptic potentials and provide a fast acting reversal potential which should help to reduce the variability in signal propagation between cells and provide further inhibition to help synchronise the network activity. Secondly, implementing a persistent sodium channel to the cell body which will act to non-linearise the activation threshold where after a given membrane potential the amplitude of the excitatory postsynaptic potential (EPSP) is boosted to push cells which receive slightly more excitation (most likely high units) over the firing threshold. Finally, implementing spatial characteristics of the dendritic tree will allow a greater probability of a modified synapse existing after 10% random connectivity has been applied throughout the network. We apply spatial characteristics by scaling the conductance weights of excitatory synapses which simulate the loss in potential in synapses found in the outer dendritic regions due to increased resistance. To further increase the biological plausibility of the network we remove the pseudo-inhibition and apply realistic basket cell models with differing configurations for a global inhibitory circuit. The networks are configured with; 1 single basket cell providing feedback inhibition, 10% basket cells providing feedback inhibition where 10 pyramidal cells connect to each basket cell and finally, 100% basket cells providing feedback inhibition. These networks are compared and contrasted for efficacy on recall quality and the effect on the network behaviour. We have found promising results from applying biologically plausible recall strategies and network configurations which suggests the role of inhibition and cellular dynamics are pivotal in learning and memory.

Brain-Inspired Computing: From Neuroscience to Neuromorphic Electronics driving new forms of Artificial Intelligence

Author : Jonathan Mapelli
Publisher : Frontiers Media SA
ISBN 13 : 2889746089
Total Pages : 139 pages
Book Rating : 4.8/5 (897 download)

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Book Synopsis Brain-Inspired Computing: From Neuroscience to Neuromorphic Electronics driving new forms of Artificial Intelligence by : Jonathan Mapelli

Download or read book Brain-Inspired Computing: From Neuroscience to Neuromorphic Electronics driving new forms of Artificial Intelligence written by Jonathan Mapelli and published by Frontiers Media SA. This book was released on 2022-03-08 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms

Author : Chenwei Deng
Publisher : Frontiers Media SA
ISBN 13 : 2832521169
Total Pages : 223 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms by : Chenwei Deng

Download or read book Brain-inspired Cognition and Understanding for Next-generation AI: Computational Models, Architectures and Learning Algorithms written by Chenwei Deng and published by Frontiers Media SA. This book was released on 2023-04-19 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Models of Brain and Behavior

Author : Ahmed A. Moustafa
Publisher : John Wiley & Sons
ISBN 13 : 1119159067
Total Pages : 586 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Computational Models of Brain and Behavior by : Ahmed A. Moustafa

Download or read book Computational Models of Brain and Behavior written by Ahmed A. Moustafa and published by John Wiley & Sons. This book was released on 2017-11-13 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Neural Networks and Deep Learning

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 497 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Neural Networks and Deep Learning by : Charu C. Aggarwal

Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Artificial Neural Networks and Machine Learning – ICANN 2021

Author : Igor Farkaš
Publisher : Springer Nature
ISBN 13 : 3030863832
Total Pages : 705 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2021 by : Igor Farkaš

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2021 written by Igor Farkaš and published by Springer Nature. This book was released on 2021-09-10 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as representation learning, reservoir computing, semi- and unsupervised learning, spiking neural networks, text understanding, transfers and meta learning, and video processing. *The conference was held online 2021 due to the COVID-19 pandemic.

Artificial Neural Networks as Models of Neural Information Processing

Author : Marcel van Gerven
Publisher : Frontiers Media SA
ISBN 13 : 2889454010
Total Pages : 220 pages
Book Rating : 4.8/5 (894 download)

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Book Synopsis Artificial Neural Networks as Models of Neural Information Processing by : Marcel van Gerven

Download or read book Artificial Neural Networks as Models of Neural Information Processing written by Marcel van Gerven and published by Frontiers Media SA. This book was released on 2018-02-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advanced Data Analysis in Neuroscience

Author : Daniel Durstewitz
Publisher : Springer
ISBN 13 : 3319599763
Total Pages : 308 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Advanced Data Analysis in Neuroscience by : Daniel Durstewitz

Download or read book Advanced Data Analysis in Neuroscience written by Daniel Durstewitz and published by Springer. This book was released on 2017-09-15 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanatory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck

Object Recognition, Attention, and Action

Author : Naoyuki Osaka
Publisher : Springer Science & Business Media
ISBN 13 : 4431730192
Total Pages : 252 pages
Book Rating : 4.4/5 (317 download)

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Book Synopsis Object Recognition, Attention, and Action by : Naoyuki Osaka

Download or read book Object Recognition, Attention, and Action written by Naoyuki Osaka and published by Springer Science & Business Media. This book was released on 2009-03-12 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human object recognition is a classical topic both for philosophy and for the natural sciences. Ultimately, understanding of object recognition will be promoted by the cooperation of behavioral research, neurophysiology, and computation. This original book provides an excellent introduction to the issues that are involved. It contains chapters that address the ways in which humans and machines attend to, recognize, and act toward objects in the visual environment.