Fundamentals of Computational Neuroscience
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A termék adatai:
- Kiadó Oxford University Press
- Megjelenés dátuma 2009. január 13.
- ISBN 9780198515838
- Kötéstípus Puhakötés
- Terjedelem360 oldal
- Méret 246x171x19 mm
- Súly 571 g
- Nyelv angol
- Illusztrációk numerous figures 0
Kategóriák
Rövid leírás:
Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system.
Fundamentals of computational neuroscience is the first introductory book to this topic. It introduces the theoretical foundations of neuroscience with a focus on understanding information processing in the brain.
The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards.
Hosszú leírás:
Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of
how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic.
Fundamentals of Computational Neuroscience is one of the first introductory books on this topic. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the
brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies.
Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to
explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards.
Tartalomjegyzék:
What is computational Neuroscience?
Domains in Computational Neuroscience
What is a model?
Emergence and adaptation
From exploration to a theory of the brain
Some notes on the book
Modelling biological neurons
Neurons are specialized cells
Basic synaptic mechanisms
The generation of action potentials: Hodgkin-Huxley equations
Dendritic trees, the propagation of action potentials, and compartmental models
Above and Beyond the Hodgkin-Huxley neuron: Fatigue, bursting and simplifications
Integrate-and-fire neurons
The spike-response model
Spike time variability
Noise Models for IF neurons
Organizations of neuronal networks
Information transmission in networks
Population Dynamics: modelling the average behaviour of neurons
The sigma node
Networks with non-classical synapses: the sigma-pi node
How Neurons talk
Information theory
Information in spike trains
Population coding and decoding
Distributed representation
Perception, function represntation, and look-up tables
The sigma node as perception
Multi-layer mapping networks
Learning, generalization and biological interpretations
Self-organizing network architectures and geentic algorighms
Mapping networks with context units
Probabilistic mapping networks
Associative memory and Hebbian learning
An example of learning association
The biochemical basis of synaptic plasticity
The temporal structure of Hebbian plasticity: LTP and LTD
Mathematical formulation of Hebian plasticity
Weight distributions
Neuronal response variability, gain control, and scaling
Features of associators and Hebbian learning
Short-term memory and reverberating network activity
Long-term memory and auto-associators
Point attractor networks: The Grossberg-Hopfield model
The phase diagram and the Grossberg-Hopfield model
Sparse attractor neural networks
Chaotic networks: a dynamical systems view
Biologically more realistic variation of attractor networks
Spatial representations and the sense of directions
Learning with continuous pattern representations
Asymptotic states and the dynamics of neural fields
Path-integration, Hebbian trace rule, and sequence learning
Competitive networks and self-organizing maps
Motor learning and control
The delta rule
Generalized delta rules
Reward learning
System level anatomy of the brain
Modular mapping networks
Coupled attractor networks
Working memory
Attentive vision
An interconnecting workspace hypothesis
Introduction to hte MATLAB programming environment
Spiking neurons and numerical integration in MATLAB
Associators and Hebbian learning
Recurrent networks and networks dynamics
Continuous attractor neural networks
Error-backpropagation network
Some Useful Mathematics
Basic Probability Theory
Numerical Integration
Index
