Coverart for item
The Resource Neuronal dynamics : from single neurons to networks and models of cognition, Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski

Neuronal dynamics : from single neurons to networks and models of cognition, Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski

Label
Neuronal dynamics : from single neurons to networks and models of cognition
Title
Neuronal dynamics
Title remainder
from single neurons to networks and models of cognition
Statement of responsibility
Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski
Creator
Contributor
Author
Subject
Language
eng
Cataloging source
CAMBR
http://library.link/vocab/creatorName
Gerstner, Wulfram
Dewey number
612.8
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1969-
http://library.link/vocab/relatedWorkOrContributorName
  • Kistler, Werner M.
  • Naud, Richard
  • Paninski, Liam
http://library.link/vocab/subjectName
  • Neurobiology
  • Neural networks (Neurobiology)
  • Cognitive neuroscience
Label
Neuronal dynamics : from single neurons to networks and models of cognition, Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Elements of neuronal dynamics
  • Noise input
  • 8.2.
  • Stochastic spike arrival
  • 8.3.
  • Subthreshold vs. superthreshold regime
  • 8.4.
  • Diffusion limit and Fokker--Planck equation (*)
  • 8.5.
  • Summary
  • 9.
  • 1.3.
  • Noisy output: escape rate and soft threshold
  • 9.1.
  • Escape noise
  • 9.2.
  • Likelihood of a spike train
  • 9.3.
  • Renewal approximation of the Spike Response Model
  • 9.4.
  • From noisy inputs to escape noise
  • 9.5.
  • Integrate-and-fire models
  • Summary
  • 10.
  • Estimating parameters of probabilistic neuron models
  • 10.1.
  • Parameter optimization in linear and nonlinear models
  • 10.2.
  • Statistical formulation of encoding models
  • 10.3.
  • Evaluating goodness-of-fit
  • 10.4.
  • 1.4.
  • Closed-loop stimulus design
  • 10.5.
  • Summary
  • 11.
  • Encoding and decoding with stochastic neuron models
  • 11.1.
  • Encoding models for intracellular recordings
  • 11.2.
  • Encoding models in systems neuroscience
  • 11.3.
  • Limitations of the leaky integrate-and-fire model
  • Decoding
  • 11.4.
  • Summary
  • pt. THREE
  • NETWORKS OF NEURONS AND POPULATION ACTIVITY
  • 12.
  • Neuronal populations
  • 12.1.
  • Columnar organization
  • 12.2.
  • 1.5.
  • Identical neurons: a mathematical abstraction
  • 12.3.
  • Connectivity schemes
  • 12.4.
  • From microscopic to macroscopic
  • 12.5.
  • Summary
  • 13.
  • Continuity equation and the Fokker--Planck approach
  • 13.1.
  • What can we expect from integrate-and-fire models?
  • Continuity equation
  • 13.2.
  • Stochastic spike arrival
  • 13.3.
  • Fokker--Planck equation
  • 13.4.
  • Networks of leaky integrate-and-fire neurons
  • 13.5.
  • Networks of nonlinear integrate-and-fire neurons
  • 13.6.
  • 1.6.
  • Neuronal adaptation and synaptic conductance
  • 13.7.
  • Summary
  • 14.
  • Quasi-renewal theory and the integral-equation approach
  • 14.1.
  • Population activity equations
  • 14.2.
  • Recurrent networks and interacting populations
  • 14.3.
  • Summary
  • Linear response to time-dependent input
  • 14.4.
  • Density equations vs. integral equations
  • 14.5.
  • Adaptation in population equations
  • 14.6.
  • Heterogeneity and finite size
  • 14.7.
  • Summary
  • 15.
  • 2.
  • Fast transients and rate models
  • 15.1.
  • How fast are population responses?
  • 15.2.
  • Fast transients vs. slow transients in models
  • 15.3.
  • Rate models
  • 15.4.
  • Summary
  • pt. FOUR
  • Machine generated contents note:
  • Ion channels and the Hodgkin--Huxley model
  • DYNAMICS OF COGNITION
  • 16.
  • Competing populations and decision making
  • 16.1.
  • Perceptual decision making
  • 16.2.
  • Competition through common inhibition
  • 16.3.
  • Dynamics of decision making
  • 16.4.
  • 2.1.
  • Alternative decision models
  • 16.5.
  • Human decisions, determinism, and free will
  • 16.6.
  • Summary
  • 17.
  • Memory and attractor dynamics
  • 17.1.
  • Associations and memory
  • 17.2.
  • Equilibrium potential
  • Hopfield model
  • 17.3.
  • Memory networks with spiking neurons
  • 17.4.
  • Summary
  • 18.
  • Cortical field models for perception
  • 18.1.
  • Spatial continuum model
  • 18.2.
  • 2.2.
  • Input-driven regime and sensory cortex models
  • 18.3.
  • Bump attractors and spontaneous pattern formation
  • 18.4.
  • Summary
  • 19.
  • Synaptic plasticity and learning
  • 19.1.
  • Hebb rule and experiments
  • 19.2.
  • Hodgkin--Huxley model
  • Models of Hebbian learning
  • 19.3.
  • Unsupervised learning
  • 19.4.
  • Reward-based learning
  • 19.5.
  • Summary
  • 20.
  • Outlook: dynamics in plastic networks
  • 20.1.
  • 2.3.
  • Reservoir computing
  • 20.2.
  • Oscillations: good or bad?
  • 20.3.
  • Helping patients
  • 20.4.
  • Summary
  • The zoo of ion channels
  • 2.4.
  • Summary
  • 3.
  • pt. ONE
  • Dendrites and synapses
  • 3.1.
  • Synapses
  • 3.2.
  • Spatial structure: the dendritic tree
  • 3.3.
  • Spatial structure: axons
  • 3.4.
  • Compartmental models
  • 3.5.
  • FOUNDATIONS OF NEURONAL DYNAMICS
  • Summary
  • 4.
  • Dimensionality reduction and phase plane analysis
  • 4.1.
  • Threshold effects
  • 4.2.
  • Reduction to two dimensions
  • 4.3.
  • Phase plane analysis
  • 4.4.
  • 1.
  • Type I and type II neuron models
  • 4.5.
  • Threshold and excitability
  • 4.6.
  • Separation of time scales and reduction to one dimension
  • 4.7.
  • Summary
  • pt. TWO
  • GENERALIZED INTEGRATE-AND-FIRE NEURONS
  • 5.
  • Introduction: neurons and mathematics
  • Nonlinear integrate-and-fire models
  • 5.1.
  • Thresholds in a nonlinear integrate-and-fire model
  • 5.2.
  • Exponential integrate-and-fire model
  • 5.3.
  • Quadratic integrate and fire
  • 5.4.
  • Summary
  • 6.
  • 1.1.
  • Adaptation and firing patterns
  • 6.1.
  • Adaptive exponential integrate-and-fire
  • 6.2.
  • Firing patterns
  • 6.3.
  • Biophysical origin of adaptation
  • 6.4.
  • Spike Response Model (SRM)
  • 6.5.
  • Elements of neuronal systems
  • Summary
  • 7.
  • Variability of spike trains and neural codes
  • 7.1.
  • Spike-train variability
  • 7.2.
  • Mean firing rate
  • 7.3.
  • Interval distribution and coefficient of variation
  • 7.4.
  • 1.2.
  • Autocorrelation function and noise spectrum
  • 7.5.
  • Renewal statistics
  • 7.6.
  • The problem of neural coding
  • 7.7.
  • Summary
  • 8.
  • Noisy input models: barrage of spike arrivals
  • 8.1.
Control code
ocn885338083
Dimensions
unknown
Extent
1 online resource (xi, 577 pages)
File format
unknown
Form of item
online
Isbn
9781107447615
Isbn Type
(electronic bk.)
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)885338083
Label
Neuronal dynamics : from single neurons to networks and models of cognition, Wulfram Gerstner, Werner M. Kistler, Richard Naud, Liam Paninski
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Elements of neuronal dynamics
  • Noise input
  • 8.2.
  • Stochastic spike arrival
  • 8.3.
  • Subthreshold vs. superthreshold regime
  • 8.4.
  • Diffusion limit and Fokker--Planck equation (*)
  • 8.5.
  • Summary
  • 9.
  • 1.3.
  • Noisy output: escape rate and soft threshold
  • 9.1.
  • Escape noise
  • 9.2.
  • Likelihood of a spike train
  • 9.3.
  • Renewal approximation of the Spike Response Model
  • 9.4.
  • From noisy inputs to escape noise
  • 9.5.
  • Integrate-and-fire models
  • Summary
  • 10.
  • Estimating parameters of probabilistic neuron models
  • 10.1.
  • Parameter optimization in linear and nonlinear models
  • 10.2.
  • Statistical formulation of encoding models
  • 10.3.
  • Evaluating goodness-of-fit
  • 10.4.
  • 1.4.
  • Closed-loop stimulus design
  • 10.5.
  • Summary
  • 11.
  • Encoding and decoding with stochastic neuron models
  • 11.1.
  • Encoding models for intracellular recordings
  • 11.2.
  • Encoding models in systems neuroscience
  • 11.3.
  • Limitations of the leaky integrate-and-fire model
  • Decoding
  • 11.4.
  • Summary
  • pt. THREE
  • NETWORKS OF NEURONS AND POPULATION ACTIVITY
  • 12.
  • Neuronal populations
  • 12.1.
  • Columnar organization
  • 12.2.
  • 1.5.
  • Identical neurons: a mathematical abstraction
  • 12.3.
  • Connectivity schemes
  • 12.4.
  • From microscopic to macroscopic
  • 12.5.
  • Summary
  • 13.
  • Continuity equation and the Fokker--Planck approach
  • 13.1.
  • What can we expect from integrate-and-fire models?
  • Continuity equation
  • 13.2.
  • Stochastic spike arrival
  • 13.3.
  • Fokker--Planck equation
  • 13.4.
  • Networks of leaky integrate-and-fire neurons
  • 13.5.
  • Networks of nonlinear integrate-and-fire neurons
  • 13.6.
  • 1.6.
  • Neuronal adaptation and synaptic conductance
  • 13.7.
  • Summary
  • 14.
  • Quasi-renewal theory and the integral-equation approach
  • 14.1.
  • Population activity equations
  • 14.2.
  • Recurrent networks and interacting populations
  • 14.3.
  • Summary
  • Linear response to time-dependent input
  • 14.4.
  • Density equations vs. integral equations
  • 14.5.
  • Adaptation in population equations
  • 14.6.
  • Heterogeneity and finite size
  • 14.7.
  • Summary
  • 15.
  • 2.
  • Fast transients and rate models
  • 15.1.
  • How fast are population responses?
  • 15.2.
  • Fast transients vs. slow transients in models
  • 15.3.
  • Rate models
  • 15.4.
  • Summary
  • pt. FOUR
  • Machine generated contents note:
  • Ion channels and the Hodgkin--Huxley model
  • DYNAMICS OF COGNITION
  • 16.
  • Competing populations and decision making
  • 16.1.
  • Perceptual decision making
  • 16.2.
  • Competition through common inhibition
  • 16.3.
  • Dynamics of decision making
  • 16.4.
  • 2.1.
  • Alternative decision models
  • 16.5.
  • Human decisions, determinism, and free will
  • 16.6.
  • Summary
  • 17.
  • Memory and attractor dynamics
  • 17.1.
  • Associations and memory
  • 17.2.
  • Equilibrium potential
  • Hopfield model
  • 17.3.
  • Memory networks with spiking neurons
  • 17.4.
  • Summary
  • 18.
  • Cortical field models for perception
  • 18.1.
  • Spatial continuum model
  • 18.2.
  • 2.2.
  • Input-driven regime and sensory cortex models
  • 18.3.
  • Bump attractors and spontaneous pattern formation
  • 18.4.
  • Summary
  • 19.
  • Synaptic plasticity and learning
  • 19.1.
  • Hebb rule and experiments
  • 19.2.
  • Hodgkin--Huxley model
  • Models of Hebbian learning
  • 19.3.
  • Unsupervised learning
  • 19.4.
  • Reward-based learning
  • 19.5.
  • Summary
  • 20.
  • Outlook: dynamics in plastic networks
  • 20.1.
  • 2.3.
  • Reservoir computing
  • 20.2.
  • Oscillations: good or bad?
  • 20.3.
  • Helping patients
  • 20.4.
  • Summary
  • The zoo of ion channels
  • 2.4.
  • Summary
  • 3.
  • pt. ONE
  • Dendrites and synapses
  • 3.1.
  • Synapses
  • 3.2.
  • Spatial structure: the dendritic tree
  • 3.3.
  • Spatial structure: axons
  • 3.4.
  • Compartmental models
  • 3.5.
  • FOUNDATIONS OF NEURONAL DYNAMICS
  • Summary
  • 4.
  • Dimensionality reduction and phase plane analysis
  • 4.1.
  • Threshold effects
  • 4.2.
  • Reduction to two dimensions
  • 4.3.
  • Phase plane analysis
  • 4.4.
  • 1.
  • Type I and type II neuron models
  • 4.5.
  • Threshold and excitability
  • 4.6.
  • Separation of time scales and reduction to one dimension
  • 4.7.
  • Summary
  • pt. TWO
  • GENERALIZED INTEGRATE-AND-FIRE NEURONS
  • 5.
  • Introduction: neurons and mathematics
  • Nonlinear integrate-and-fire models
  • 5.1.
  • Thresholds in a nonlinear integrate-and-fire model
  • 5.2.
  • Exponential integrate-and-fire model
  • 5.3.
  • Quadratic integrate and fire
  • 5.4.
  • Summary
  • 6.
  • 1.1.
  • Adaptation and firing patterns
  • 6.1.
  • Adaptive exponential integrate-and-fire
  • 6.2.
  • Firing patterns
  • 6.3.
  • Biophysical origin of adaptation
  • 6.4.
  • Spike Response Model (SRM)
  • 6.5.
  • Elements of neuronal systems
  • Summary
  • 7.
  • Variability of spike trains and neural codes
  • 7.1.
  • Spike-train variability
  • 7.2.
  • Mean firing rate
  • 7.3.
  • Interval distribution and coefficient of variation
  • 7.4.
  • 1.2.
  • Autocorrelation function and noise spectrum
  • 7.5.
  • Renewal statistics
  • 7.6.
  • The problem of neural coding
  • 7.7.
  • Summary
  • 8.
  • Noisy input models: barrage of spike arrivals
  • 8.1.
Control code
ocn885338083
Dimensions
unknown
Extent
1 online resource (xi, 577 pages)
File format
unknown
Form of item
online
Isbn
9781107447615
Isbn Type
(electronic bk.)
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)885338083

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
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