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1 | (12) |
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What is computational neuroscience? |
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1 | (2) |
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Domains in computational neuroscience |
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3 | (3) |
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6 | (3) |
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9 | (1) |
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From exploration to a theory of the brain |
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10 | (3) |
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Neurons and conductance-based models |
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13 | (25) |
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Modelling biological neurons |
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13 | (1) |
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Neurons are specialized cells |
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14 | (2) |
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Basic synaptic mechanisms |
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16 | (6) |
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The generation of action potentials: Hodgkin-Huxley equations |
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22 | (7) |
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Dendritic trees, the propagation of action potentials, and compartmental models |
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29 | (3) |
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Above and beyond the Hodgkin-Huxley neuron: fatigue, bursting, and simplifications |
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32 | (6) |
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Spiking neurons and response variability |
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38 | (18) |
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Integrate-and-fire neurons |
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38 | (4) |
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42 | (2) |
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44 | (4) |
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Noise models for IF-neurons |
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48 | (8) |
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56 | (33) |
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Organizations of neuronal networks |
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56 | (9) |
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Information transmission in networks |
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65 | (7) |
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Population dynamics: modelling the average behaviour of neurons |
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72 | (7) |
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79 | (5) |
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Networks with nonclassical synapses: the sigma-pi node |
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84 | (5) |
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Representations and the neural code |
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89 | (31) |
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89 | (6) |
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95 | (5) |
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Information in spike trains |
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100 | (7) |
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Population coding and decoding |
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107 | (5) |
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Distributed representation |
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112 | (8) |
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Feed-forward mapping networks |
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120 | (26) |
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Perception, function representation, and look-up tables |
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120 | (5) |
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The sigma node as perception |
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125 | (5) |
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Multilayer mapping networks |
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130 | (4) |
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Learning, generalization, and biological interpretations |
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134 | (4) |
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Self-organizing network architectures and genetic algorithms |
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138 | (2) |
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Mapping networks with context units |
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140 | (2) |
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Probabilistic mapping networks |
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142 | (4) |
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Associators and synaptic plasticity |
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146 | (28) |
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Associative memory and Hebbian learning |
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146 | (3) |
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An example of learning associations |
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149 | (4) |
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The biochemical basis of synaptic plasticity |
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153 | (1) |
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The temporal structure of Hebbian plasticity: LTP and LTD |
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154 | (4) |
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Mathematical formulation of Hebbian plasticity |
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158 | (3) |
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161 | (4) |
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Neuronal response variability, gain control, and scaling |
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165 | (5) |
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Features of associators and Hebbian learning |
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170 | (4) |
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Auto-associative memory and network dynamics |
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174 | (33) |
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Short-term memory and reverberating network activity |
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174 | (2) |
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Long-term memory and auto-associators |
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176 | (3) |
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Point-attractor networks: the Grossberg-Hopfield model |
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179 | (6) |
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The phase diagram and the Grossberg-Hopfield model |
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185 | (5) |
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Sparse attractor neural networks |
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190 | |
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Chaotic networks: a dynamic systems view |
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187 | (15) |
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Biologically more realistic variations of attractor networks |
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202 | (5) |
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Continuous attractor and competitive networks |
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207 | (26) |
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Spatial representations and the sense of direction |
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207 | (4) |
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Learning with continuous pattern representations |
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211 | (4) |
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Asymptotic states and the dynamics of neural fields |
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215 | (7) |
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'Path' integration, Hebbian trace rule, and sequence learning |
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222 | (4) |
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Competitive networks and self-organizing maps |
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226 | (7) |
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Supervised learning and rewards systems |
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233 | (21) |
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Motor learning and control |
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233 | (4) |
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237 | (4) |
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241 | (5) |
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246 | (8) |
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System level organization and coupled networks |
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254 | (30) |
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System level anatomy of the brain |
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254 | (4) |
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258 | (5) |
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Coupled attractor networks |
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263 | (5) |
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268 | (5) |
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273 | (6) |
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An interconnecting workspace hypothesis |
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279 | (5) |
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A MATLAB guide to computational neuroscience |
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284 | (32) |
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Introduction to the MATLAB programming environment |
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284 | (6) |
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Spiking neurons and numerical integration in MATLAB |
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290 | (8) |
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Associators and Hebbian learning |
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298 | (3) |
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Recurrent networks and network dynamics |
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301 | (5) |
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Continuous attractor neural networks |
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306 | (5) |
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Error-back-propagation network |
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311 | (5) |
A Some useful mathematics |
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316 | (4) |
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Vector and matrix notations |
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316 | (2) |
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318 | (1) |
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319 | (1) |
B Basic probability theory |
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320 | (7) |
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Random variables and their probability (density) function |
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320 | (1) |
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Examples of probability (density) functions |
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320 | (3) |
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Cumulative probability (density) function and the Gaussian error function |
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323 | (1) |
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Moments: mean and variance |
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324 | (1) |
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Functions of random variables |
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325 | (2) |
C Numerical integration |
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327 | (6) |
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327 | (1) |
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327 | (1) |
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328 | (1) |
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328 | (3) |
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331 | (2) |
Index |
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333 | |