LEADER 05721nam 2200721Ia 450 001 9910784591403321 005 20200520144314.0 010 $a1-281-04744-9 010 $a9786611047443 010 $a0-08-053638-7 035 $a(CKB)1000000000357994 035 $a(EBL)313978 035 $a(OCoLC)476104699 035 $a(SSID)ssj0000201877 035 $a(PQKBManifestationID)12066819 035 $a(PQKBTitleCode)TC0000201877 035 $a(PQKBWorkID)10245389 035 $a(PQKB)10049720 035 $a(Au-PeEL)EBL313978 035 $a(CaPaEBR)ebr10191640 035 $a(CaONFJC)MIL104744 035 $a(OCoLC)469635234 035 $a(MiAaPQ)EBC313978 035 $a(PPN)178937134 035 $a(EXLCZ)991000000000357994 100 $a20050121d2005 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMethods and models in neurophysics$b[electronic resource] =$eMethodes et modeles en neurophysique : E?cole d'E?te? de Physique des Houches, Session LXXX, 28 July-29 August 2003, Nato Advanced Study Institute, Ecole Thematique du CNRS /$fedited by C.C. Chow ... [et al.] 205 $a1st ed. 210 $aAmsterdam ;$aSan Diego, Calif. $cElsevier$d2005 215 $a1 online resource (863 p.) 225 1 $aLes Houches 300 $aDescription based upon print version of record. 311 $a0-444-51792-8 320 $aIncludes bibliographical references. 327 $aFront 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 level 327 $a5. 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. Conclusion 327 $aAppendix 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. Introduction 327 $a2. 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. Introduction 327 $a2. 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. Introduction 327 $a2. Persistent neuronal activity during delayed response experiments 330 $aNeuroscience is an interdisciplinary field that strives to understand the functioning of neural systems at levels ranging from biomolecules and cells to behaviour and higher brain functions (perception, memory, cognition). Neurophysics has flourished over the past three decades, becoming an indelible part of neuroscience, and has arguably entered its maturity. It encompasses a vast array of approaches stemming from theoretical physics, computer science, and applied mathematics. This book provides a detailed review of this field from basic concepts to its most recent development. 410 0$aLes Houches 606 $aNeural networks (Neurobiology)$xComputer simulation$vCongresses 606 $aNervous system$xComputer simulation$vCongresses 606 $aComputational neuroscience$vCongresses 615 0$aNeural networks (Neurobiology)$xComputer simulation 615 0$aNervous system$xComputer simulation 615 0$aComputational neuroscience 676 $a573.8/01/13 676 $a573.850113 701 $aChow$b C. C$g(Carson C.)$01517896 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784591403321 996 $aMethods and models in neurophysics$93755151 997 $aUNINA