Neuronal dynamics : (Record no. 1788)

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000 -LEADER
fixed length control field 05256nam a22001697a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220909162017.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220909b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107635197
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 612.8
Item number GER
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gerstner, Wulfram
245 ## - TITLE STATEMENT
Title Neuronal dynamics :
Remainder of title from single neurons to networks and models of cognition
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. , Cambridge, United Kingdom,
Name of publisher, distributor, etc. Cambridge University Press
Date of publication, distribution, etc. 2014
300 ## - PHYSICAL DESCRIPTION
Page number 577P:
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note <br/>Machine generated contents note: pt. ONE FOUNDATIONS OF NEURONAL DYNAMICS<br/>1. Introduction: neurons and mathematics<br/>1.1. Elements of neuronal systems<br/>1.2. Elements of neuronal dynamics<br/>1.3. Integrate-and-fire models<br/>1.4. Limitations of the leaky integrate-and-fire model<br/>1.5. What can we expect from integrate-and-fire models?<br/>1.6. Summary<br/>2. Ion channels and the Hodgkin<br/>Huxley model<br/>2.1. Equilibrium potential<br/>2.2. Hodgkin<br/>Huxley model<br/>2.3. The zoo of ion channels<br/>2.4. Summary<br/>3. Dendrites and synapses<br/>3.1. Synapses<br/>3.2. Spatial structure: the dendritic tree<br/>3.3. Spatial structure: axons<br/>3.4.Compartmental models<br/>3.5. Summary<br/>4. Dimensionality reduction and phase plane analysis<br/>4.1. Threshold effects<br/>4.2. Reduction to two dimensions<br/>4.3. Phase plane analysis<br/>4.4. Type I and type II neuron models<br/>4.5. Threshold and excitability<br/>4.6. Separation of time scales and reduction to one dimension<br/>4.7. Summary. Contents note continued: pt. TWO GENERALIZED INTEGRATE-AND-FIRE NEURONS<br/>5. Nonlinear integrate-and-fire models<br/>5.1. Thresholds in a nonlinear integrate-and-fire model<br/>5.2. Exponential integrate-and-fire model<br/>5.3. Quadratic integrate and fire<br/>5.4. Summary<br/>6. Adaptation and firing patterns<br/>6.1. Adaptive exponential integrate-and-fire<br/>6.2. Firing patterns<br/>6.3. Biophysical origin of adaptation<br/>6.4. Spike Response Model (SRM)<br/>6.5. Summary<br/>7. Variability of spike trains and neural codes<br/>7.1. Spike-train variability<br/>7.2. Mean firing rate<br/>7.3. Interval distribution and coefficient of variation<br/>7.4. Autocorrelation function and noise spectrum<br/>7.5. Renewal statistics<br/>7.6. The problem of neural coding<br/>7.7. Summary<br/>8. Noisy input models: barrage of spike arrivals<br/>8.1. Noise input<br/>8.2. Stochastic spike arrival<br/>8.3. Subthreshold vs. superthreshold regime<br/>8.4. Diffusion limit and Fokker<br/>Planck equation (*)<br/>8.5. Summary. Contents note continued: 9. Noisy output: escape rate and soft threshold<br/>9.1. Escape noise<br/>9.2. Likelihood of a spike train<br/>9.3. Renewal approximation of the Spike Response Model<br/>9.4. From noisy inputs to escape noise<br/>9.5. Summary<br/>10. Estimating parameters of probabilistic neuron models<br/>10.1. Parameter optimization in linear and nonlinear models<br/>10.2. Statistical formulation of encoding models<br/>10.3. Evaluating goodness-of-fit<br/>10.4. Closed-loop stimulus design<br/>10.5. Summary<br/>11. Encoding and decoding with stochastic neuron models<br/>11.1. Encoding models for intracellular recordings<br/>11.2. Encoding models in systems neuroscience<br/>11.3. Decoding<br/>11.4. Summary<br/>pt. THREE NETWORKS OF NEURONS AND POPULATION ACTIVITY<br/>12. Neuronal populations<br/>12.1. Columnar organization<br/>12.2. Identical neurons: a mathematical abstraction<br/>12.3. Connectivity schemes<br/>12.4. From microscopic to macroscopic<br/>12.5. Summary. Contents note continued: 13. Continuity equation and the Fokker<br/>Planck approach<br/>13.1. Continuity equation<br/>13.2. Stochastic spike arrival<br/>13.3. Fokker<br/>Planck equation<br/>13.4.Networks of leaky integrate-and-fire neurons<br/>13.5.Networks of nonlinear integrate-and-fire neurons<br/>13.6. Neuronal adaptation and synaptic conductance<br/>13.7. Summary<br/>14. Quasi-renewal theory and the integral-equation approach<br/>14.1. Population activity equations<br/>14.2. Recurrent networks and interacting populations<br/>14.3. Linear response to time-dependent input<br/>14.4. Density equations vs. integral equations<br/>14.5. Adaptation in population equations<br/>14.6. Heterogeneity and finite size<br/>14.7. Summary<br/>15. Fast transients and rate models<br/>15.1. How fast are population responses?<br/>15.2. Fast transients vs. slow transients in models<br/>15.3. Rate models<br/>15.4. Summary<br/>pt. FOUR DYNAMICS OF COGNITION<br/>16.Competing populations and decision making<br/>16.1. Perceptual decision making. Contents note continued: 16.2.Competition through common inhibition<br/>16.3. Dynamics of decision making<br/>16.4. Alternative decision models<br/>16.5. Human decisions, determinism, and free will<br/>16.6. Summary<br/>17. Memory and attractor dynamics<br/>17.1. Associations and memory<br/>17.2. Hopfield model<br/>17.3. Memory networks with spiking neurons<br/>17.4. Summary<br/>18. Cortical field models for perception<br/>18.1. Spatial continuum model<br/>18.2. Input-driven regime and sensory cortex models<br/>18.3. Bump attractors and spontaneous pattern formation<br/>18.4. Summary<br/>19. Synaptic plasticity and learning<br/>19.1. Hebb rule and experiments<br/>19.2. Models of Hebbian learning<br/>19.3. Unsupervised learning<br/>19.4. Reward-based learning<br/>19.5. Summary<br/>20. Outlook: dynamics in plastic networks<br/>20.1. Reservoir computing<br/>20.2. Oscillations: good or bad?<br/>20.3. Helping patients<br/>20.4. Summary
520 ## - SUMMARY, ETC.
Summary, etc. This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience
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Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Inventory number Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Currency Koha item type Collection code Shelving location
    Dewey Decimal Classification     IIITDM Kurnool IIITDM Kurnool 09.09.2022 Technical Bureau India 38.99 TB1118 DT 3/8/22   612.8 GER 0004870 09.09.2022 38.99 09.09.2022 GBP Books    
    Dewey Decimal Classification   Not For Loan IIITDM Kurnool IIITDM Kurnool 09.09.2022 Technical Bureau India 38.99 TB1118 DT 3/8/22   612.8 GER 0004871 09.09.2022 38.99 09.09.2022 GBP Reference Reference Reference
    Dewey Decimal Classification     IIITDM Kurnool IIITDM Kurnool 09.09.2022 Technical Bureau India 38.99 TB1118 DT 3/8/22   612.8 GER 0004872 09.09.2022 38.99 09.09.2022 GBP Books    
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