Emergent Neural Computational Architectures Based on Neuroscience : Towards Neuroscience-Inspired Computing - Stefan Wermter

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Emergent Neural Computational Architectures Based on Neuroscience

Towards Neuroscience-Inspired Computing

By: Stefan Wermter (Editor), Jim Austin (Editor), David Willshaw (Editor)

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Paperback | 25 July 2001

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592 Pages

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24.13 x 15.88 x 1.91

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It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Computer Architecture & Logic Design Artificial Intelligence Neural Networks & Fuzzy Systems Medicine Clinical & Internal Medicine Neurology & Clinical Neurophysiology Science Biology, Life Sciences Life Sciences in General

Neurosciences Mathematics Calculus & Mathematical Analysis Numerical Analysis Computer Programming & Software Development Algorithms & Data Structures Computer Vision

Towards Novel, Neuroscience-Inspired Computing	p. 1
Images of the Mind: Brain Images and Neural Networks	p. 20
Stimulus-Independent Data Analysis for fMRI	p. 39
Emergence of Modularity within One Sheet of Neurons: A Model Comparison	p. 53
Computational Investigation of Hemispheric Specialization and Interactions	p. 68
Explorations of the Interaction between Split Processing and Stimulus Types	p. 83
Modularity and Specialized Learning: Mapping between Agent Architectures and Brain Organization	p. 98
Biased Competition Mechanisms for Visual Attention in a Multimodular Neurodynamical System	p. 114
Recurrent Long-Range Interactions in Early Vision	p. 127
Neural Mechanisms for Representing Surface and Contour Features	p. 139
Representations of Neuronal Models Using Minimal and Bilinear Realisations	p. 154
Collaborative Cell Assemblies: Building Blocks of Cortical Computation	p. 161
On the Influence of Threshold Variability in a Mean-Field Model of the Visual Cortex	p. 174
Towards Computational Neural Systems through Developmental Evolution	p. 188
The Complexity of the Brain: Structural, Functional, and Dynamic Modules	p. 203
Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission	p. 212
Segmenting State into Entities and Its Implication for Learning	p. 227
Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain	p. 237
Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle	p. 255
Locust Olfaction (Synchronous Oscillations in Excitatory and Inhibitory Groups of Spiking Neurons)	p. 270
Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays	p. 285
The Role of Brain Chaos	p. 296
Neural Network Classification of Word Evoked Neuromagnetic Brain Activity	p. 311
Simulation Studies of the Speed of Recurrent Processing	p. 320
The Dynamics of Learning and Memory: Lessons from Neuroscience	p. 333
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation	p. 348
Plasticity and Nativism: Towards a Resolution of an Apparent Paradox	p. 368
Cell Assemblies as an Intermediate Level Model of Cognition	p. 383
Modelling Higher Cognitive Functions with Hebbian Cell Assemblies	p. 398
Spiking Associative Memory and Scene Segmentation by Synchronization of Cortical Activity	p. 407
A Familiarity Discrimination Algorithm Inspired by Computations of the Perirhinal Cortex	p. 428
Linguistic Computation with State Space Trajectories	p. 442
Robust Stimulus Encoding in Olfactory Processing: Hyperacuity and Efficient Signal Transmission	p. 461
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?	p. 480
An Investigation into the Role of Cortical Synaptic Depression in Auditory Processing	p. 494
The Role of Memory, Anxiety, and Hebbian Learning in Hippocampal Function: Novel Explorations in Computational Neuroscience and Robotics	p. 507
Using a Time-Delay Actor-Critic Neural Architecture with Dopamine-Like Reinforcement Signal for Learning in Autonomous Robots	p. 522
Connectionist Propositional Logic (A Simple Correlation Matrix Memory Based Reasoning System)	p. 534
Analysis and Synthesis of Agents That Learn from Distributed Dynami Data Sources	p. 547
Connectionist Neuroimaging	p. 560
Author Index	p. 577
Table of Contents provided by Blackwell. All Rights Reserved.

Emergent Neural Computational Architectures Based on Neuroscience

Towards Neuroscience-Inspired Computing

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Paperback

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