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Emergent neural computational architectures based on neuroscience : towards neuroscience-inspired computing

Author: Stefan Wermter
Publisher: Berlin [u.a.] : Springer, 2001.
Series: Lecture notes in computer science, Lecture notes in artificial intelligence., 2036.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems.

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Genre/Form: Aufsatzsammlung
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Stefan Wermter
ISBN: 354042363X 9783540423638
OCLC Number: 247900933
Description: x, 576 S : Ill., graph. Darst ; 24 cm.
Contents: Towards Novel Neuroscience-Inspired Computing.- Towards Novel Neuroscience-Inspired Computing.- Modular Organisation and Robustness.- Images of the Mind: Brain Images and Neural Networks.- Stimulus-Independent Data Analysis for fMRI.- Emergence of Modularity within One Sheet of Neurons: A Model Comparison.- Computational Investigation of Hemispheric Specialization and Interactions.- Explorations of the Interaction between Split Processing and Stimulus Types.- Modularity and Specialized Learning: Mapping between Agent Architectures and Brain Organization.- Biased Competition Mechanisms for Visual Attention in a Multimodular Neurodynamical System.- Recurrent Long-Range Interactions in Early Vision.- Neural Mechanisms for Representing Surface and Contour Features.- Representations of Neuronal Models Using Minimal and Bilinear Realisations.- Collaborative Cell Assemblies: Building Blocks of Cortical Computation.- On the Influence of Threshold Variability in a Mean-Field Model of the Visual Cortex.- Towards Computational Neural Systems through Developmental Evolution.- The Complexity of the Brain: Structural, Functional, and Dynamic Modules.- Timing and Synchronisation.- Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission.- Segmenting State into Entities and Its Implication for Learning.- Temporal Structure of Neural Activity and Modelling of Information Processing in the Brain.- Role of the Cerebellum in Time-Critical Goal-Oriented Behaviour: Anatomical Basis and Control Principle.- Locust Olfaction.- Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays.- The Role of Brain Chaos.- Neural Network Classification of Word Evoked Neuromagnetic Brain Activity.- Simulation Studies of the Speed of Recurrent Processing.- Learning and Memory Storage.- The Dynamics of Learning and Memory: Lessons from Neuroscience.- Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation.- Plasticity and Nativism: Towards a Resolution of an Apparent Paradox.- Cell Assemblies as an Intermediate Level Model of Cognition.- Modelling Higher Cognitive Functions with Hebbian Cell Assemblies.- Spiking Associative Memory and Scene Segmentation by Synchronization of Cortical Activity.- A Familiarity Discrimination Algorithm Inspired by Computations of the Perirhinal Cortex.- Linguistic Computation with State Space Trajectories.- Robust Stimulus Encoding in Olfactory Processing: Hyperacuity and Efficient Signal Transmission.- Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?.- An Investigation into the Role of Cortical Synaptic Depression in Auditory Processing.- The Role of Memory, Anxiety, and Hebbian Learning in Hippocampal Function: Novel Explorations in Computational Neuroscience and Robotics.- Using a Time-Delay Actor-Critic Neural Architecture with Dopamine-Like Reinforcement Signal for Learning in Autonomous Robots.- Connectionist Propositional Logic A Simple Correlation Matrix Memory Based Reasoning System.- Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources.- Connectionist Neuroimaging.
Series Title: Lecture notes in computer science, Lecture notes in artificial intelligence., 2036.
Responsibility: Stefan Wermter ... (eds.).
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