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Scott A. — Neuroscience: a mathematical primer
Scott A. — Neuroscience: a mathematical primer



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Íàçâàíèå: Neuroscience: a mathematical primer

Àâòîð: Scott A.

Àííîòàöèÿ:

This is an introductory text of mathematical neuroscience intended for anyone who wants to appreciate the role that mathematics and mathematical modeling and analysis can do to aid an understanding of how the brain works and the nature of the mind. In particular, the book will be of interest to established neuroscientists and neuroscience students who wish to know what roles mathematical formulations can play in attempting to comprehend the dynamics of a human brain. It is expected that this text will be interesting for mathematics faculty teaching in neuroscience programs. It also aims to serve as a general introduction to neuromathematics in neuroscience programs at both undergraduate and graduate levels. Physical scientists and bioengineers who plan to extend their research activities into the realms of cognitive science will find this an ideal guide, as will philosophers and social scientists who wish to understand the degree to which dynamics of a brain can be reduced to mathematical formulations. Mathematical formulations in neuroscience are of five sorts: (i) Exact descriptions of well understood dynamic processes, like the Hodgkin — Huxley theory of the nerve impulse. (ii) Metaphorical descriptions of more complex phenomena, like the stationary states of a Hopfield model. (iii) Information theory for dealing with the storage and transmission of data. (iv) Logical calculus (Boolean algebra) for the analysis of information processing systems. (v) Number theory for counting large numbers of possibilities. (vi) Statistical tools for organizing and evaluating data.


ßçûê: en

Ðóáðèêà: Ìàòåìàòèêà/

Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö

ed2k: ed2k stats

Ãîä èçäàíèÿ: 2002

Êîëè÷åñòâî ñòðàíèö: 352

Äîáàâëåíà â êàòàëîã: 10.12.2005

Îïåðàöèè: Ïîëîæèòü íà ïîëêó | Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
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Ïðåäìåòíûé óêàçàòåëü
Eigenvalues, continuous discrete (point)      134 326
Einstein, Albert      58
Einstein’s relation      58—59 60—61 65
Electric field in capacitor      54
Electricity, atmospheric, chemical, and electrical      1
Electrochemical potential      61
Electronic charge      56
Elsasser, Walter      74 222 243 297
Emergence      43 234 297 301 301 302 304
Emergence of biological levels      294 298 303 306
Emergence of biological levels cell assemblies      18 268
Emergence of biological levels nerve impulses      78 119
Emergence of biological levels patterns      13 234 241
Emergence, ontological nature of      294
Emmeche, Claus      302
Emotion      17 258 311
Enhancement zone      87—89 90 91 278
Ephaptic interactions      45 165—183 270 335—340
Ephaptic interactions assemblies of impulses      182
Ephaptic interactions coupling parameter      169 179 335
Ephaptic interactions FitzHugh — Nagumo model      174—177 182 337—340
Ephaptic interactions impulse synchronization      166—167 182 183
Ephaptic interactions leading-edge analysis of      169—171 173 182 335—337
Ephaptic interactions lowering of threshold      166
Ephaptic interactions Markin — Chizmadzhev analysis of      167—169
Ephaptic interactions on myelinated nerves      177—181 182—183
Ephaptic interactions on myelinated nerves continuum limit      179—180
Ephaptic interactions on myelinated nerves dynamics      180—181
Ephaptic interactions on myelinated nervesfailure      180
Ephaptic interactions physiological implications      181—182
Ephaptic interactions, qualitative analysis of      171—174
Ephaptic interactions, stability of      172—174 176
Equilibrium (Nernst) potential      37 97 142 143 144
Equivalent cylinder      195—199 224
Ermentrout, G.B.      251
Euler equations      see Lagrange — Euler equations
Evans function      135 136
Evans, John      135 328
Evolutionary explanation for squid axon branching      218
Excitatory postsynaptic potential (EPSP)      37
Exocytosis      37 38
Exponential growth      15 18 269 294
Facilitation and extinction      235
Failure of impulse propagation      7 10 140 149 180
Falk, C.X.      284
Fanselow, E.E.      282
Faraday, Michael      2—3
Fatty (lipid) molecules      50 51
feedback control systems      13—14 68 299—300
Feedback control systems loop      see Positive feedback
Fiber geometry      6 199—206
Field theories of neocortex      248—252
Fife, Paul      135
Final cause      299 310
Fitz Hugh — Nagumo (F-N) nerve model      5—6 122—124 136
Fitz Hugh — Nagumo nerve model ephaptic coupling      174—177 337—340
Fitz Hugh — Nagumo nerve model equation      123
Fitz Hugh — Nagumo nerve model homoclinic trajectories for      131
Fitz Hugh — Nagumo nerve model impulse, perturbation theory for      331—333
Fitz Hugh — Nagumo nerve model impulse, perturbation theory for mathematical stability      134—136
Fitz Hugh — Nagumo nerve model impulse, perturbation theory for numerical stability      132—133
Fitz Hugh — Nagumo nerve model impulse, perturbation theory for qualitative stability      133—134
Fitz Hugh — Nagumo nerve model impulse, perturbation theory for, stability of      132—136 328
Fitz Hugh — Nagumo nerve model leading edge of      130 131 133 136
Fitz Hugh — Nagumo nerve model periodic solutions of      125—126
Fitz Hugh — Nagumo nerve model stability of impulse      132—136
Fitz Hugh — Nagumo nerve model structure of impulse      130—131
Fitz Hugh — Nagumo nerve model, trailing edge of      131 132 133—134
FitzHugh, Richard      5 87 123 125 136 143 219
Flame-front propagation      4 102—103
Flip-flop circuit      273
Flourens, Marie-Jean-Pierre      11
Flow of conserved quantity      315
Fontana, Walter      309
Forces, interatomic      29 49—50
Formal cause(s)      298—299 300 301 302 306
Frank—Kamenetsky, David      4 102
Fransen, Erik      278 280 282
Fraser, J.T.      34
Fredholm’s theorem      321—322 332 336 338 339
Free charge      53 55
Free running multivibrator      242
Frog (Rana pipiens), enhancement zone for      90
Frog, enhancement zone for impulse velocity      2 142 149
Frog, enhancement zone for internode distance      142 150
Frog, enhancement zone for standard axon      142—143 148—151
Frog, enhancement zone for “three-eyed”      251
Frontal lobes      258 307
Fujita, M.      155 156 157
Fundamental equation of neuroscience      31 33
Gage, Phineas      258
Galileo Galilei      294 299
Galvani, Luigi      1 2 6 139 158
Gap junctions      39—40 45 170
Gap junctions, resistance      39—40
Geometric ratio      205—206 217 224
George, S.A.      90
Gerstein, G.L.      286
Gerstner, W.      42 116
Gestalt psychology      234 262
Ghazanfar, A.A.      282
Goldfinger, Mel      223
Goldstein, S.S.      205
Golgi stain      209
Goodsell, David S.      51
Goodwin, Brian      302
Googol      241 243 297
Governor      13
GR      see Geometric ratio
Green function      193—194 195 221 223
Green function for impulse stability      328—328
Green, George      193
Greenland shark (Somniosus microcephalus)      158
Griffith, J.S.      249 276
Grossman, Y.      219
Gurovich, V.T.      115
H-H      see Hodgkin — Huxley
Habituation effects      263 270
Hadean oceans      14 52
Haken, Hermann      273
Hallucinations      251 264
Hamiltonian formulation      32 317
Harth, Erich      12
Hausser, Michael      45
Head group      50 51
Heaviside step function      42 237 238
Hebb, Donald      17 252 257 259 262 271 272 278 287 310
Hebbian synapse      259—260
Helmholtz, Hermann      1—2 158 296
Hering, Ewald      165
Heron, W.      264
Heterogeneous sets      243 244 298
Hierarchical dynamics      10 45
Hierarchical dynamics of neurons      41
Hierarchical dynamics organization of memory      261—262 274 287
Hierarchical dynamics, biological      293—296
Hierarchical dynamics, cognitive      305—309
Higgs boson      296
Hodgkin — Huxley (H-H) model      5 9 295 319—320
Hodgkin — Huxley model axon      67—84
Hodgkin — Huxley model axon, leading-edge approximation for      95—97 105
Hodgkin — Huxley model condition for AND bifurcation      209
Hodgkin — Huxley model condition for AND bifurcation for block      201—202
Hodgkin — Huxley model equations, traveling-wave solutions of      79—84
Hodgkin — Huxley model experimental techniques      70—74
Hodgkin — Huxley model impulse, ignition of      109—110
Hodgkin — Huxley model impulse, ignition of, speed of      83 84 119
Hodgkin — Huxley model impulse, ignition of, stability of      83—84 328
Hodgkin — Huxley model measurements      40 70—74
Hodgkin — Huxley model membrane oscillations      90—91
Hodgkin — Huxley model parameters      83 98 120
Hodgkin — Huxley model results      5 70—74
Hodgkin — Huxley model, dynamics      319—320
Hodgkin, Alan      5 142 159
Holograph      277
Holograph-like recall      248
Homoclinic trajectory in phase plane      106 107
Homogeneous sets      243 298
Honeybee (Apis mellifera)      195
Hopfield model      244—248 307
Hopfield, John      234 244
Human culture      308—309
Humboldt, Frederic von      1
Hunger      13
Hutchinson, N.A.      153 155 157
Huxley, Andrew      5 158
Hyperpolarization      37
Hysteresis in ionic conduction      105
Ignition of cell assembly      18
Immense numbers      243 305
Impulse      4 84 118 131 212
Impulse ignition      109—111
Impulse propagation      45 79—84
Impulse stability of      6 132—136 323—330
Impulse stability of in leading-edge approximation      108—109
Impulse steering      219 224
Impulse synchronism      45 165—182
Impulse, blocking of      201—204
Impulse, blocking of, on dendrites      206—213
Independent variable transformations      134 323
Inductance, phenomenological      91 128
Information, meaningful      310
Information, meaningful processing      8
Information, meaningful processing in axons      217—220
Information, meaningful processing in dendrites      206—217
Information, meaningful, measure of      310
Inhibitory postsynaptic potential (IPSP)      37
Inhomogeneous active fibers      199—206 223 224
Initial segment      9 27
Inner (dot) product      239 283 321
Instability      see Stability
insulators      53 55
Integrate and fire model      116 146
Intentionality      310
Interconnection matrices      18
Interconnection matrices in associative memories      277
Interconnection matrices in associative memories attractor neural network      245 247
Interconnection matrices in associative memories McCulloch — Pitts model      42 237 238
Interconnection matrices probabilities      250
Internal feedback loop      13 306—307
Intrinsic membrane proteins      10 53 57 61
Ionic batteries      see Membrane batteries
Ionic conductance      4 56 73
Ionic conductance current      5 37 56—60
Ionic conductance current total      63 73
Ionic conductance current transmembrane      56—60 68—69 70—74
Jack, J.J.B.      157 199
James, William      16—17
Japanese puffer fish (Spheroides rubrides)      61
Jefferys, J.G.R.      166
Jones, Chris      135 136
Jupiter’s Great Red Spot      304
Kalu, K.U.      157
Katz, B.      166 169
Kauffman, Stuart      302
Keat, J.      42
Keener, James      147 199
Keller, J.B.      126
Kelvin, Lord (William Thomson)      51
Khodorov, Boris      201—202 210 211 212 219 224
Kim, Jaegwon      295 303 304
kinetic energy      30 34
Kirchhoff’s circuit laws      77—78 167 177
Kleinfeld, D.      251
Koch, Christof      44 187 199 211 213 221
Kompaneyets, A.S.      115
Kunov, Hans      146
Lagerstrom, P. A.      331
Lagrange — Euler equations      128 317
Lagrangian density      127 316 317 318
Lagrangian density formulation      32 316—318
Langmuir, Irving      50
Lansner, Anders      278 280 282
Laplace transform      221 329
Laurent, G.      284 285
Law, Margaret      251
Leading edge of squid impulse      4
Leading edge of squid impulse approximation      95—97 111
Leading edge of squid impulse charge      109—111 116
Leading edge of squid impulse of FitzHugh — Nagumo impulse      130—132
Leading edge of squid impulse waveforms      102—104
Leakage current      73
Learning Curve      259
Learning curve machines      234—241 252
Learning curve phase      12 260—261
Learning curve to see      259 260
Legendy, Charles      274 276 277
Leonardo da Vinci      206
Leonardo’s law      206 211 218
Lettvin, Jerry      10
Liebovic, K.N.      91
Lindbergh, Charles      265
Lindgren, A.G.      199—200 324
Linear diffusion      32 318
Linear diffusion across synaptic cleft      37
Linear diffusion in dendritic models      188—194 224
Linear discriminant plane      238
Linear discriminant plane stability analysis      108—109 112
Linear discriminant plane stability analysis criteria      325
Linear discriminant plane stability analysis theorem      108
Linear discriminant plane stability analysis threshold unit (LTU)      42 238
Linear discriminant plane stability analysis versus nonlinear      300—301
Linear separability      240
Linearization about a traveling wave      134 324—325
Lipid bilayers      50—53
Lipid bilayers artificial      52—53
Lipid bilayers, capacitance of      52
Lipid bilayers, permeability of      53
Llinas, Rodolfo      207 208
Locust (Schistocerca americana)      284—285
Logical computations      33 206—213 235—237
Logistic equation      15 16 269 271 294
Longitudinal resistance      31 77 83 98
Longitudinal resistance of myelinated nerve      142 143
Lorente de No, Rafael      11 12 86
Louie, K.      287
Luther, Robert      3 97
Luther’s factor      97 98 104 132
Luzader, Steve      170 174
Lyapunov functional      246 252
M-C      see Markin — Chizmadzhev
M-P      see McCulloch — Pitts
MacGregor, R      279
Maginu, K.      324
Maldonado, P.E.      286
Malsburg, C. von der      282
Mandelbrot, B.B.      206
Markin — Chizmadzhev (M-C) model      223 224
Markin — Chizmadzhev model analysis of block      202 205
Markin — Chizmadzhev model of ephaptic coupling      167—169 182
Markin — Chizmadzhev model of ephaptic coupling nerve impulse      115—122 136
Markin, V.S.      105 115 136 167 168 202
Material cause      298 299 300 301
Mauro, Alex      91
Maxwell’s equations      78 295
McCulloch — Pitts network      12 222 233 235—237 244
McCulloch — Pitts network neuron      9 10 46 300
McCulloch — Pitts network neuron in cell assembly model      266 267 270 271 272 274
McCulloch — Pitts network neuron in perceptron      238
McCulloch — Pitts network neuron, definition of      41—43
1 2 3 4
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