Ординатура / Офтальмология / Английские материалы / Computational Maps in the Visual Cortex_Miikkulainen_2005

532 Subject Index

in strabismus see visual deprivation, strabismus

LISSOM model of 106–113

potential sources of eye differences 106, 382

OD see ocular dominance

old age, effect on cortical processing 151 olfactory bulb 395

ON/OFF channels 16 in animals 17 properties of 17 role of 71, 138–142

open contours 274, 275, 294 open-source software development 406 opponent cell receptive fields 380 optic chiasm 16

optic nerve 16

optical character recognition (OCR) 314 optical imaging 19, 87, 365, 373, 380 OR see orientation

orientation (OR) 89

orientation biases, effect on orientation maps 194–198, 202

orientation jitter 273–302

orientation maps see also combined maps calculating see feature maps, calculat-

ing

computational models of 93 developed from large unoriented pat-

terns 104

effect of input pattern types on 104 HLISSOM model of 189–202

in ferret 89

in macaque monkey 18, 20, 21, 89 in newborns 8, 192, 194, 202 LISSOM model of 95–106 measuring 97

PGLISSOM model of 249–255 orientation preference, development of 4 orthographic representations 395 oscillations 34, 395

overtraining 384

owl, multi-modal integration in 394

pacman-like disks 277, 279 parameter search 331

parameter values, setting see LISSOM, parameters, setting values for

pattern generation see internally generated patterns

pattern recognition 396, 400

PCA see principal component analysis PDP++ 403

Pearson’s correlation 281 perceived orientation

computation example 163 computation in LISSOM 161

perceptrons 315–319

perceptual grouping 8–10, see also PGLISSOM, model of contour integration

effect of normalization on 348 transitive see transitive grouping via lateral connections 347

via temporal coding 33, 47 periphery 15

contour integration in 277, 304

face preferences in 204, 205, 212, 236, 368

modeling of 69, 387, 388 visual statistics in 277, 303, 371

permissive role for spontaneous activity 31

PGLISSOM (perceptual grouping LISSOM) 241

activation function 246 architecture 241–255

assumptions see LISSOM, assumptions

binding 280–285

connections between locations in visual space 284

contour completion 288–304 decreasing excitatory radius 248 effect of jitter on 282

excitatory and inhibitory lateral connections 350

illusory contour response 288–304 initial weights 247

learning rule 248

limits on contours that can be segmented 287

model of contour integration 280–304 parameters 435–438

predictions 368–372 scaling up 303 segmentation 286–287

size independence 265 synapse model 244 unit model 244

PGO see ponto-geniculo-occipital waves phaclofen 365

phase columns 378 phase invariance 378 phase transition 62

phonetic representations 395 photoreceptors 15, 31 phylogeny 368

piecewise linear sigmoid see sigmoid pinwheel centers 20, 86, 89, 97, 102, 115,

122, 124, 251

PIP see posterior intraparietal area

pizza box model of ocular dominance 62, 63

plasticity 133–153 in CBL models 93

predictions on 365–366 Poggendorf illusion 28

ponto-geniculo-occipital (PGO) waves 32–33, 178, 355–357

generator in HLISSOM 178 hypothesized shape of 212 in twins 32

population oscillations 36

posterior intraparietal area (PIP) 395 postnatal development 194–198, 383 postnatal face learning, HLISSOM model

of 228–233

postsynaptic normalization see normalization

postsynaptic potential (PSP) 244, 258, 270, 271, 369–370, 372, 376, 411

predictions see also LISSOM, assumptions

general overview 362–373

combined OR/DR maps in different species 119

compensation for delays with decay rate 369

contour integration in newborns 371 cortical activity patterns during the

TAE 171, 365

decay rate adaptation in synchronization 369

effect of artificial environments 363

effect of differences in internally generated patterns between species 366 effect of face outline shape for new-

borns 234, 368

effect of flipping visual environment during development 371

effect of inhibitory blockade on sparseness 373

effect of modifying internally generated patterns 367

effect of natural scene statistics on connection patterns 297, 371

effect of natural scene statistics on contour integration performance 299 effect of refractory period on synchro-

nization 370

effect of TMS on contour integration 368

effect of V1 size 363

extent of cortical plasticity depending on lateral interaction lengths 151, 366

extent of reorganization to retinal scotoma 142, 366

functional role of cortical layers in synchronization 372

holistic face learning in newborns 204, 233, 368

indirect TAE caused by weight normalization 170

infant preferences for realistic faces 232, 234, 368

lateral connections 130

after rearing in deprived environments 364

effect of natural scene statistics on 371

in color and spatial frequency maps 364

in direction maps 117, 364

in orientation pinwheels, saddle points, and fractures 102, 363

reflecting activity correlations 363 lateral interactions increasing sparse-

ness 372

learning rate scaling 342

natural scene statistics differences between upper and lower hemifields 371

534 Subject Index

newborn cortical response to faces 217 newborn preferences

for facelike vs. top-heavy patterns 235, 368

for faces 219, 224 for mother 233, 368

orientation-specific effects on cortical plasticity 149, 366

pruning level depending on number of connections 343

response to indirect TAE test pattern increasing during adaptation 365

RF shapes in newborns 193, 367

role of input variance and correlations 363

role of retinal waves in OD maps 131 role of short-term plasticity in synchro-

nization 370

sign and magnitude of TAE under inhibitory blockade 171, 365

spatial configuration of PGO waves 367

spatial frequency map after rearing in deprived environments 364

prefrontal cortex 370

prenatal and postnatal visual activity 31, 189–202, 382–384

presynaptic normalization see normalization

primary visual cortex (V1) 4, 16 feature preferences in 18 layers (lamination) in 18

number of neurons and connections in see number of, neurons and connections in V1

organization of vii–x, 4 size of 18, 341

principal component analysis (PCA) 59 principal curves 60–63

principal surfaces 59–63

projections between LISSOM sheets 421 proprioception 211, 404

prototypes of faces 230, 231 pruning

in LISSOM 77, 352

in PGLISSOM 248

of lateral connections 25, 347 scaling of 330

PSP see postsynaptic potential

pulvinar 16, 212 Purkinje cells 42 push–pull

cortical RFs 376, 377, see also normalization, afferent

effect resulting from afferent normalization 180

lateral connections 107 LGN RFs 75

pyramidal cells 40, 41, 43, 350 Python 406

quantization effects 330

rabbit, direction-selective ganglion cells in 381

radial basis function networks 317 random noise

input patterns 104, 191, 192, 367 role in desynchronization 46

role in development see internally generated patterns

role in synchronization 263

random weight values, effect of 184, 326 rapid-eye-movement (REM) sleep 32–33, 212, 237, 355–357, 383, 398

in embryos 32 receptive field (RF) 4, 17

in higher cortical areas 20 scatter 72

shapes in adults 17

shapes in newborns 193, 201, 367 reciprocal connections 23, 69, 303, 398 reconstruction error for principal surfaces

60

reconstruction of visual input 309–314 recovery from injury or surgery see plas-

ticity

rectangular grid model of the cortical sheet 51, 330

red/green opponent cells 380 reduced HLISSOM 225–233 reduced LISSOM 138–142, 326–341

parameters 427–428

redundancy reduction see decorrelation reference simulation 417

refractory period 246, 370 regression 151

REM see rapid-eye-movement sleep

resampling 326, 333 retina 15

activity waves in 7, 31, 140, 182, 367 computational model of 191–194 pharmacological manipulation of 367 sufficiency for developing OR maps

189

density scaling of 327

ganglion cells in 15, 31, 381, 388 lesions in see lesions, retinal

retinotopic maps

calculating see feature maps, calculating

distortions due to other maps 100 LISSOM model of 78–81

SOM model of 55–57 retrograde amnesia 356 retrograde transport 88

reverberating circuits 274, 275, 294, 379 rewiring visual inputs to go to auditory

cortex 22

RF see receptive field

rhesus macaque monkey see monkey RMS see root mean square

robots, biological modeling using 399 robustness 7, 8, 105, 133, 153, 175, 200,

201, 224, 227, 265

root mean square (RMS) 284, 338, 442 rTMS see transcranial magnetic stimula-

tion

saccades

modeling of 387, 389

saddle points 20, 89, 98, 102, 115, 122, 251

salience

of contours 282, 283, 294 of illusory patterns 293 testing in animals 359

Sc (schematics, figure label) 226

scalar product 51, 70, 73, 75, 185, 317, 321

scaling

cortical density 330 lateral connections 330 model complexity 405 retinal density 327

to human cortex size 339 visual field area 326

221, 225–227, 243

scotoma 134, 145, see also lesions, retinal scripting language 405, 406 segmentation 10, 43, 242, 286–287 selectivity of orientation maps 87, 99 self-organization see input-driven self-

organization self-organizing maps 50–57

biological plausibility of 67 general architecture 51

SEM see standard error of the mean separable representations 314

SG (self-organization and grouping column model) 244, 251, 350, 351, 378

shadows in face images 211

sigmoid 45, 68, 75, 76, 184, 225, 229, 246 signal transduction 30

simple cells 18

simulators for cortical maps see Topographica

situated perception 399

sizes of cortical areas 343, 381, 392 SMAP (self-organization map in PGLIS-

SOM) 242

SoG see sum of Gaussians

SOM (self-organizing feature map algorithm) 52–57

activation function 53 comparison with LISSOM 78–81 continuous model 333

decreasing excitatory radius 53, 77

for handwritten character recognition 314–324

growing model 333 hierarchical 389

lateral interactions (neighborhood function) 52

learning rule 55

model of cortical maps 93, 346 model of plasticity 137

model of retinotopic maps 55–57 neighborhood function 346 parameters 439–440 winner-take-all mechanism 52, 346

somatosensory cortex 26, 137, 395, 404 sparse coding 307–314, 372–373 spatial frequency maps 132, 364, 380

536 Subject Index

spatial frequency preference of LGN cell 16

spatiotemporal receptive fields 89, 90, 115, 362

species differences 132

due to internally generated patterns 8, 366

in cortical area sizes 342, 343 in cortical maps 18, 20

in map size 325, 363 in REM sleep 32 modeling of 381–382

spectral models see map models, spectral speech 43, 45, 358, 404

speeding up recurrent processing 324 spike generator 244, 246 spike-based coding 359

spike-timing-dependent plasticity (STDP) 49

spikes 33, 359–360, 372, see also PGLISSOM

spiking units 10, 43, 242, 244, 405 information capacity of 397

spinal cord 30

spontaneous activity see internally generated patterns

squint see visual deprivation, strabismus stacked model of cortical layers 243 standard error of the mean (SEM) 157,

164, 284, 288

STDP see spike-timing-dependent plasticity

stellate cells 43 stereo vision 28, 111

stereoscopic images 380

strabismus see visual deprivation, strabismus

striate cortex see primary visual cortex stroke 133, 135, 151

subcortical face processing areas 212, 235

subcortical pathways 15, 16, 395 subtractive normalization see normaliza-

tion

sum of Gaussians (SoG) 309–313, 320 sum-of-squares normalization see nor-

malization supercomputers 331, 341

superior colliculus 16, 211, 212, 389

superposition catastrophe 33, 34 supervised learning 310, 315, 400 support vector machines 317 surgery, effect of 151

symmetry breaking 62, 264

synapses, number of see number of, synapses in humans

synaptic decay rate see decay rate synaptic depression 360

synaptic transmission 268, 360 synchronization 10, 46–48, 242

as a binding mechanism 33–36, 43, 257–271, 358–359

between cortical areas 393–396

effect of excitatory and inhibitory connections on 258–263

effect of long refractory period on 269 effect of noise on 263–270

effect of strong excitation on 268 synergetic development of multiple con-

nection types 26, 347, 392 synfire chains 393

tabula rasa 29

TAE see tilt aftereffect tagging 401

temporal coding 33–36, 43, 46–48, 241, 358–360

temporal integrator 245 temporo-occipital (TEO) area 41 TEO see temporo-occipital area tetrodotoxin 25

thalamocortical connections see feedforward pathways

thalamocortical loop 395

three-dot patterns 211–213, 216, 367 generation of 236, 355

threshold adaptation 228, 229, 246, 375 tilt aftereffect (TAE) 155–172, 347, 384–

386, 391, 428 analysis of 166–170

computing magnitude of 162 computing perceived orientation for

161

dark recovery of 171 demonstration of 156 detailed mechanism of 167 direct 156, 168

effect of adaptation time on 156

in humans 157

in LISSOM vs. humans 164–166 indirect 156, 168, 170

measured neurally 365 measured psychophysically 156 predictions on 171, 365 saturation of 166, 171

theories and models of 158–160 decorrelation 159

fatigue 158, 166, 365 gain control 160

lateral inhibition 159, 166 time course of 165 variability of 171

with oblique contours 156 tilt illusion 155, 171, 384

TMS see transcranial magnetic stimulation

top lighting 211, 224

top-heavy face preference model 210, 368 topographic maps 18–20, 403

computational models of 91–95 in animals 85–91

Topographica 403–406

compared to other simulators 403 implementation 405

scope and design 404 trace learning rule 379, 390

training pattern stream, effect on selforganization 184

training time 333, 340

transcranial magnetic stimulation (TMS) 369

transformation invariance 390

transistors see number of, transistors in computing systems

transitive grouping 261, 262, 275, 359 translation invariance 390

tree shrew 15, 102

trophic factors 296, 352, 353 twins, PGO waves in 32

two-dimensional approximation of cortical structure 18

unit models 39, see also coupled oscillators; compartmental models; firing-rate models; integrate-and- fire-model

UNIVAC 50

bias/variance tradeoff variance of input

effect on cortical maps 362–363 effect on self-organizing maps 59–63

VC see Vapnik–Chervonenkis dimension vector quantization 400

vector sum see perceived orientation, computing in LISSOM; feature maps, calculating, vector sum method

ventral stream 178, 212 ventral visual area 4 (V4v) 180 vernier acuity 386

viewpoint invariance 390 VisNet 390

visual aftereffects 385, see also tilt aftereffect; motion aftereffect; visual illusions

with illusory contours 391 visual area 2 (V2)

color maps 380 disparity maps 380

illusory contours in 276, 288, 303 modeling of 389–394

RF shapes in 279 visual area 4 (V4)

modeling of 389–394 visual coding 310 visual deprivation

by dark rearing 22

by eyelid suture 4, 22, 32, 192, 199

by orientation biases 4, 22, 87, 176, see also orientation biases, effect on orientation maps

by strabismus 26, 87–88, 106–111 effect on lateral connections 176, 364

visual development

effect of thalamic input on 176

538 Subject Index

interaction between genetic and environmental factors 28–33, 177, 189– 202, 358

visual illusions 28, see tilt illusion, visual aftereffects

visual system anatomy 15 architecture 4

as a model system for understanding the brain viii, xi

development 5, 176 in humans 16

visually evoked activity 15 voltage-sensitive dyes 19

waterfall illusion see motion aftereffect (MAE)

wavelets 275

weight interpolation 333–336

weight values, effect of initial 184, 326 winner-take-all mechanism 346

wiring length 396