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Gerstner W., Kistler W.M. — Spiking Neuron Models
Gerstner W., Kistler W.M. — Spiking Neuron Models



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Название: Spiking Neuron Models

Авторы: Gerstner W., Kistler W.M.

Аннотация:

This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.


Язык: en

Рубрика: Биология/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2002

Количество страниц: 504

Добавлена в каталог: 21.12.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Spike response model, moroneuron model      114
Spike response model, simplified model $\textrm{SRM}_0$      119
Spike train      13
Spike train, irregular      157
Spiking neuron model      15
Spiking neuron model $\textrm{SRM}_0$      15 17
Spontaneous activity      157 249
Stability      83
Stability-Plasticity dilemma      376
Stable manifold      93
Static-pattern scenario with analog neurons      397
Static-pattern scenario with spiking neurons      420
Stein's model      185
Stimulation, sub-/superthreshold      195
Stochastic differential equation      184
Stochastic intensity      174
Stochastic process, doubly stochastic      416
Stochastic rate model      205
Stochastic resonance      200
Survivor function      163
Synapse      12 14 61
Synapse, AMPA      62
Synapse, excitatory      62
Synapse, inhibitory      61
Synapse, NMDA      62 387
Synapse, postsynaptic neuron      12 14
Synapse, postsynaptic potential      14
Synapse, presynaptic neuron      12 14
Synaptic plasticity competition      368
Synaptic plasticity cooperativity      364 366
Synaptic plasticity locality      366
Synaptic plasticity spike-time dependent      365 371
Synaptic plasticity, anti-Hebbian      366 367
Synaptic plasticity, associativity      364
Synaptic plasticity, consolidation of synaptic weights      376
Synaptic plasticity, Hebbian      361
Synaptic plasticity, non-Hebbian      367 372
Synaptic transmission failure      320
Synaptic transmission failures      160
Synaptic weights competition      402
Synaptic weights distribution      423
Synaptic weights hard bounds      424
Synaptic weights long-term behavior      398
Synaptic weights multiplicative normalization      404
Synaptic weights normalization      402
Synaptic weights soft bounds      424
Synaptic weights subtractive normalization      403
Synaptic weights, evolution of      397
Synchrony      31 293 351
Synfire chain      351 441
Threshold      15
Threshold of Hodgkin — Huxley model      47 49
Threshold of type I models      92
Threshold of type II models      94
Transfer function      276
Transfer function with diffusive noise      280
Transfer function with escape noise      279
Transfer function with slow noise      277
Transient response      269
Transient response with noise      272
Transient response, noise-free      270
Transinformation      320
Type I/II model      124
Type I/II model bifurcations      88
Type I/II model onset of oscillations      88
Type I/II model stable manifold      92
Type I/II model threshold      91
Volterra series      377
Waves in rate model      340
Waves in spiking model      345
Waves of spike packets      351 441
Wiener — Khinehin Theorem      170
Wilson — Cowan model      207 235
Wilson — Cowan model differential equation      207 238
Wilson — Cowan model field equation      254 325
Wilson — Cowan model integral equation      235 236 240
Working memory      331
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