05754nam 2200733Ia 450 991045726400332120200520144314.01-281-04744-997866110474430-08-053638-7(CKB)1000000000357994(EBL)313978(OCoLC)476104699(SSID)ssj0000201877(PQKBManifestationID)12066819(PQKBTitleCode)TC0000201877(PQKBWorkID)10245389(PQKB)10049720(MiAaPQ)EBC313978(PPN)178937134(Au-PeEL)EBL313978(CaPaEBR)ebr10191640(CaONFJC)MIL104744(OCoLC)469635234(EXLCZ)99100000000035799420050121d2005 uy 0engurcn|||||||||txtccrMethods and models in neurophysics[electronic resource] =Methodes et modeles en neurophysique : École d'Été de Physique des Houches, Session LXXX, 28 July-29 August 2003, Nato Advanced Study Institute, Ecole Thematique du CNRS /edited by C.C. Chow ... [et al.]1st ed.Amsterdam ;San Diego, Calif. Elsevier20051 online resource (863 p.)Les HouchesDescription based upon print version of record.0-444-51792-8 Includes bibliographical references.Front Cover; Methods and Models in Neurophysics; Copyright Page; Contents; Course 1. Experimenting with theory; 1. Overcoming communication barriers; 2. Modeling with biological neurons-the dynamic clamp; 3. The traps inherent in building conductance-based models; 4. Theory can drive new experiments; 5. Conclusions; References; Course 2. Understanding neuronal dynamics by geometrical dissection of minimal models; 1. Introduction; 2. Revisiting the Hodgkin-Huxley equations; 3. Morris-Lecar model; 4. Bursting, cellular level5. Bursting, network generated. Episodic rhythms in the developing spinal cord6. Chapter summary; References; Course 3. Geometric singular perturbation analysis of neuronal dynamics; 1. Introduction; 2. Introduction to dynamical systems; 3. Properties of a single neuron; 4. Two mutually coupled cells; 5. Excitatory-inhibitory networks; 6. Activity patterns in the basal ganglia; References; Course 4. Theory of neural synchrony; 1. Introduction; 2. Weakly coupled oscillators; 3. Strongly coupled oscillators: mechanisms of synchrony; 4. ConclusionAppendix A. Hodgkin-Huxley and Wang-Buszaki models Appendix B. Measure of synchrony and variability in numerical simulations; Appendix C. Reduction of a conductance-based model to the QIF model; References; Course 5. Some useful numerical techniques for simulating integrate-and-fire networks; 1.Introduction; 2. The conductance-based I&F model; 3. Modified time-stepping schemes; 4. Synaptic interactions; 5. Simulating a V1 model; References; Course 6. Propagation of pulses in cortical networks: the single-spike approximation; 1. Introduction2. Propagating pulses in networks of excitatory neurons 3. Propagating pulses in networks of excitatory and inhibitory neurons; 4. Discussion; Appendix A. Stability of the lower branch; References; Course 7. Activity-dependent transmission in neocortical synapses; 1. Introduction; 2. Phenomenological model of synaptic depression and facilitation; 3. Dynamic synaptic transmission on the population level; 4. Recurrent networks with synaptic depression; 5. Conclusion; References; Course 8. Theory of large recurrent networks: from spikes to behavior; 1. Introduction2. From spikes to rates I: rates in asynchronous states 3. From spikes to rates II: dynamics and conductances; 4. Persistent activity and neural integration in the brain; 5. Feature selectivity in recurrent networks-the ring model; 6. Models of associative memory; 7. Concluding remarks; References; Course 9. Irregular activity in large networks of neurons; 1. Introduction; 2. A simple binary model; 3. A memory model; 4. A model of visual cortex hypercolumn; 5. Adding realism: integrate-and-fire network; 6. Discussion; References; Course 10. Network models of memory; 1. Introduction2. Persistent neuronal activity during delayed response experimentsNeuroscience is an interdisciplinary field that strives to understand the functioning of neural systems at levels ranging from biomolecules and cells to behaviour and higher brain functions (perception, memory, cognition). Neurophysics has flourished over the past three decades, becoming an indelible part of neuroscience, and has arguably entered its maturity. It encompasses a vast array of approaches stemming from theoretical physics, computer science, and applied mathematics. This book provides a detailed review of this field from basic concepts to its most recent development.Les HouchesNeural networks (Neurobiology)Computer simulationCongressesNervous systemComputer simulationCongressesComputational neuroscienceCongressesElectronic books.Neural networks (Neurobiology)Computer simulationNervous systemComputer simulationComputational neuroscience573.8/01/13573.850113Chow C. C(Carson C.)914821MiAaPQMiAaPQMiAaPQBOOK9910457264003321Methods and models in neurophysics2050115UNINA