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

Stefan Wermter, Mark Elshaw, Jim Austin, David Willshaw (auth.), Stefan Wermter, Jim Austin, David Willshaw (eds.)354042363X, 9783540423638

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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Table of contents :
Towards Novel Neuroscience-Inspired Computing….Pages 1-19
Images of the Mind: Brain Images and Neural Networks….Pages 20-38
Stimulus-Independent Data Analysis for fMRI….Pages 39-52
Emergence of Modularity within One Sheet of Neurons: A Model Comparison….Pages 53-67
Computational Investigation of Hemispheric Specialization and Interactions….Pages 68-82
Explorations of the Interaction between Split Processing and Stimulus Types….Pages 83-97
Modularity and Specialized Learning: Mapping between Agent Architectures and Brain Organization….Pages 98-113
Biased Competition Mechanisms for Visual Attention in a Multimodular Neurodynamical System….Pages 114-126
Recurrent Long-Range Interactions in Early Vision….Pages 127-138
Neural Mechanisms for Representing Surface and Contour Features….Pages 139-153
Representations of Neuronal Models Using Minimal and Bilinear Realisations….Pages 154-160
Collaborative Cell Assemblies: Building Blocks of Cortical Computation….Pages 161-173
On the Influence of Threshold Variability in a Mean-Field Model of the Visual Cortex….Pages 174-187
Towards Computational Neural Systems through Developmental Evolution….Pages 188-202
The Complexity of the Brain: Structural, Functional, and Dynamic Modules….Pages 203-211
Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission….Pages 212-226
Segmenting State into Entities and Its Implication for Learning….Pages 227-236
Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain….Pages 237-254
Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle….Pages 255-269
Locust Olfaction….Pages 270-284
Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays….Pages 285-295
The Role of Brain Chaos….Pages 296-310
Neural Network Classification of Word Evoked Neuromagnetic Brain Activity….Pages 311-319
Simulation Studies of the Speed of Recurrent Processing….Pages 320-332
The Dynamics of Learning and Memory: Lessons from Neuroscience….Pages 333-347
Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation….Pages 348-367
Plasticity and Nativism: Towards a Resolution of an Apparent Paradox….Pages 368-382
Cell Assemblies as an Intermediate Level Model of Cognition….Pages 383-397
Modelling Higher Cognitive Functions with Hebbian Cell Assemblies….Pages 398-406
Spiking Associative Memory and Scene Segmentation by Synchronization of Cortical Activity….Pages 407-427
A Familiarity Discrimination Algorithm Inspired by Computations of the Perirhinal Cortex….Pages 428-441
Linguistic Computation with State Space Trajectories….Pages 442-460
Robust Stimulus Encoding in Olfactory Processing: Hyperacuity and Efficient Signal Transmission….Pages 461-479
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?….Pages 480-493
An Investigation into the Role of Cortical Synaptic Depression in Auditory Processing….Pages 494-506
The Role of Memory, Anxiety, and Hebbian Learning in Hippocampal Function: Novel Explorations in Computational Neuroscience and Robotics….Pages 507-521
Using a Time-Delay Actor-Critic Neural Architecture with Dopamine-Like Reinforcement Signal for Learning in Autonomous Robots….Pages 522-533
Connectionist Propositional Logic A Simple Correlation Matrix Memory Based Reasoning System….Pages 534-546
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources….Pages 547-559
Connectionist Neuroimaging….Pages 560-576