Computation in Neurons and Neural SystemsFrank H. Eeckman Springer Science & Business Media, 30 juin 1994 - 319 pages Computation in Neurons and Neural Systems contains the collected papers of the 1993 Conference on Computation and Neural Systems which was held between July 31--August 7, in Washington, DC. These papers represent a cross-section of the state-of-the-art research work in the field of computational neuroscience, and includes coverage of analysis and modeling work as well as results of new biological experimentation. |
Table des matières
III | 3 |
IV | 9 |
VI | 15 |
VII | 21 |
VIII | 27 |
IX | 33 |
XII | 39 |
XIII | 45 |
XXXVII | 165 |
XXXVIII | 171 |
XXXIX | 173 |
XL | 179 |
XLI | 185 |
XLII | 191 |
XLIII | 197 |
XLIV | 203 |
XIV | 47 |
XV | 53 |
XVI | 59 |
XVII | 65 |
XVIII | 71 |
XIX | 73 |
XX | 79 |
XXI | 85 |
XXII | 91 |
XXIII | 97 |
XXIV | 103 |
XXV | 109 |
XXVI | 115 |
XXVII | 121 |
XXVIII | 127 |
XXIX | 133 |
XXX | 139 |
XXXII | 145 |
XXXIII | 148 |
XXXIV | 153 |
XXXV | 159 |
XLV | 209 |
XLVI | 215 |
XLVII | 217 |
XLVIII | 223 |
XLIX | 229 |
L | 235 |
LI | 241 |
LIII | 247 |
LIV | 255 |
LV | 257 |
LVI | 263 |
LVII | 269 |
LIX | 275 |
LX | 281 |
LXI | 287 |
LXII | 293 |
LXIII | 299 |
LXV | 305 |
LXVI | 311 |
| 317 | |
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Expressions et termes fréquents
action potential activity adaptation afferent amplitude analysis auditory behavior biological brain burst Ca2+ calcium concentration cerebellar cerebellum changes channels compartments Computational Neuroscience conductance cone connections constant correlation cortical cortical columns coupling decrease delay dendritic depolarization dynamics effects electric electroreceptors equations excitation excitatory input experimental fibers Figure firing rate frequency function gymnotiform horizontal cell hyperpolarizing illusory contours information processing inhibition inhibitory interneurons kinetic learning lesion LHRH linear mechanisms membrane potential model neurons modulation motor msec muscimol nerve neural network neural processing elements Neural Systems Neuronal Modeling NMDA oscillators output parameters pathways pattern phase pheromone photoreceptors Physiol physiological plasticity plots postsynaptic predict presynaptic properties pyramidal cells receptive field receptor release represent representation response sequence shown shows signal simulation spatial spike trains stimulus stochastic strong input structure synaptic currents synaptic input synaptic threshold temporal vector vesicle visual cortex voltage weak input