Neuroscience and Connectionist Theory
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Adaptive Signal Processing
ALC
CaM Kinase II
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cell
Cell Excitability
cerebellar learning mechanisms
Climbing Fiber
computational neuroscience models for researchers
Cortical Cells
CS
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eq_computing
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field
flow
Geniculate Cells
granule
Hidden Units
Interpositus Nucleus
LMS Algorithm
long-term
LTP
Mexican Hat
modification
Monocular Deprivation
Mossy Fibers
neural development theory
Ocular Dominance
optical
Optical Flow
optical flow computation
Optical Flow Field
potentiation
Purkinje Cells
receptive
Receptive Fields
Rescorla Wagner Model
Retinal Receptive Fields
spatial memory modeling
synaptic
Synaptic Efficacy
Synaptic Modification
synaptic plasticity
Synaptic Strength
vertebrate brain circuits
Product details
- ISBN 9780805806199
- Weight: 780g
- Dimensions: 152 x 229mm
- Publication Date: 01 Feb 1990
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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Written for cognitive scientists, psychologists, computer scientists, engineers, and neuroscientists, this book provides an accessible overview of how computational network models are being used to model neurobiological phenomena. Each chapter presents a representative example of how biological data and network models interact with the authors' research. The biological phenomena cover network- or circuit-level phenomena in humans and other higher-order vertebrates.
Mark A. Gluck, David E. Rumelhart
