Spiking neuron models : single neurons, populations, plasticity / Wulfram Gerstner, Werner M. Kistler.
Material type: TextPublisher: Cambridge, U.K. ; New York : Cambridge University Press, 2002Copyright date: ©2002Description: xiv, 480 pages : illustrations ; 24 cmContent type:- text
- unmediated
- volume
- 0521813840
- 9780521813846
- 0521890799
- 9780521890793
- 573.8536 21
- QP363 .G475 2002
Item type | Current library | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|
Book | City Campus City Campus Main Collection | 573.8536 GER (Browse shelf(Opens below)) | Available | A546975B |
Browsing City Campus shelves, Shelving location: City Campus Main Collection Close shelf browser (Hides shelf browser)
573.80113 BEN Computational neurogenetic modeling / | 573.80113 BEN Computational neurogenetic modeling / | 573.80285632 NOR Introduction to dynamic modeling of neuro-sensory systems / | 573.8536 GER Spiking neuron models : single neurons, populations, plasticity / | 575 HET Heterochrony in evolution : a multidisciplinary approach / | 575.10113 MIT An introduction to genetic algorithms / | 576 CON Conservation biology : evolution in action / |
Includes bibliographical references and index.
Single Neuron Models -- Detailed neuron models -- Two-dimensional neuron models -- Formal spiking neuron models -- Noise in spiking neuron models -- Population Models -- Population equations -- Signal transmission and neuronal coding -- Oscillations and synchrony -- Spatially structured networks -- Models of Synaptic Plasticity -- Hebbian models -- Learning equations -- Plasticity and coding.
"Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed."--Publisher description.
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