000 06256nam a22006135i 4500
001 978-3-540-44597-5
003 DE-He213
005 20160624101950.0
007 cr nn 008mamaa
008 121227s2001 gw | s |||| 0|eng d
020 _a9783540445975
_9978-3-540-44597-5
024 7 _a10.1007/3-540-44597-8
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aEmergent Neural Computational Architectures Based on Neuroscience
_h[electronic resource] :
_bTowards Neuroscience-Inspired Computing /
_cedited by Stefan Wermter, Jim Austin, David Willshaw.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
300 _aX, 582 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2036
505 0 _aTowards 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.
520 _aIt 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.
650 0 _aComputer science.
650 0 _aNeurosciences.
650 0 _aNeurology.
650 0 _aComputer software.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aPattern Recognition.
650 2 4 _aNeurology.
650 2 4 _aNeurosciences.
700 1 _aWermter, Stefan.
_eeditor.
700 1 _aAustin, Jim.
_eeditor.
700 1 _aWillshaw, David.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540423638
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2036
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-44597-8
942 _2EBK4915
_cEBK
999 _c34209
_d34209