Computational Neuroscience: A Comprehensive ApproachJianfeng Feng CRC Press, 20 oct. 2003 - 656 pages How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding. |
Table des matières
| 1 | |
2 Atomistic Simulations of Ion Channels | 30 |
3 Modelling Neuronal Calcium Dynamics | 70 |
4 StructureBased Models of NO Diffusion in the Nervous System | 91 |
5 Stochastic Modelling of Single Ion Channels | 126 |
6 The Biophysical Basis of Firing Variability in Cortical Neurons | 153 |
7 Generating Quantitatively Accurate but Computationally Concise Models of Single Neurons | 185 |
8 Bursting Activity in Weakly Electric Fish | 217 |
High and LowLevel Views | 337 |
Stimulus Localisation in the Barrel Cortex | 368 |
14 Modelling Fly Motion Vision | 389 |
15 MeanField Theory of Irregularly Spiking Neuronal Populations and Working Memory in Recurrent Cortical Networks | 425 |
16 The Operation of Memory Systems in the Brain | 485 |
17 Modelling Motor Control Paradigms | 532 |
18 Computational Models for Generic Cortical Microcircuits | 570 |
19 Modelling Primate Visual Attention | 604 |
9 Likelihood Methods for Neural Spike Train Data Analysis | 252 |
10 BiologicallyDetailed Network Modelling | 284 |
11 Hebbian Learning and SpikeTimingDependent Plasticity | 300 |
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Expressions et termes fréquents
activity analysis approach approximation areas attention attractor average becomes brain burst calcium calculated cells changes coding components computational concentration conductance connections consider constant continuous correlation cortex cortical dendritic depends described detailed diffusion direction discussed distribution dynamics effect Equation estimate example excitatory experimental experiments field Figure filter firing rate frequency function given important increase independent inhibitory input integration interactions interval ion channels learning mean measured mechanisms membrane memory methods module motion movement Nature neural neurons Neurosci noise object observed output oxytocin parameters particular patterns population positive postsynaptic potential present probability properties pyramidal receptor recorded recurrent relative release represented response selectivity shown shows signal similar simulations single solution space spatial spike trains stimulus structure studies synaptic task temporal term theory threshold variability visual