p039tbl01.03 [consciousness, movement] links: visual, auditory, emotional.html p042tbl01.04 six main resonances which support different kinds of conscious awareness.html p051fig02.01 laterial inhibition: darker appears darker; lighter appears lighter.html p052fig02.02 Adaptive Resonance reactivation: features bottom-up; categories top-down.html p057fig02.03 neuron basic [anatomy, physiology].html p058fig02.04 Learning a global arrow in time.html p059fig02.05 Effects of intertrial and intratrial intervals.html p059fig02.06 Bow due to backward effect in time.html p060fig02.07 Error gradients depend on list position.html p061fig02.08 neural networks can learn forward and backward associations.html p063fig02.09 Short Term Memory (STM): Additive Model.html p064fig02.10 STM Shunting Model, mass action in membrane equations.html p064fig02.11 MTM habituative transmitter gate; LTM gated steepest descent learning.html p065fig02.12 Three sources of neural network research: [binary, linear, nonlinear].html p068fig02.13 Hartline: lateral inhibition in limulus retina of horseshoe crab.html p068fig02.14 Hodgkin and Huxley: spike potentials in squid giant axon.html p071fig02.15 Noise-Saturation Dilemma: functional unit is a spatial activity pattern.html p071fig02.16 Noise-Saturation Dilemma:sensitivity to ratios of inputs.html p072fig02.17 Vision: brightness constancy, contrast normalization.html p072fig02.18 Vision: brightness contrast, conserve a total quantity, total activity normalization.html p073fig02.19 Computing in a bounded activity domain, Gedanken experiment.html p073fig02.20 Shunting saturation occurs when inputs get larger to non-interacting cells.html p073fig02.21 Shunting saturation: how shunting saturation turns on all of a cells excitable sites as input intensity increases.html p073fig02.22 Computing with patterns: how to compute the pattern-sensitive variable.html p074fig02.23 Shunting on-center off-surround network: no saturation! infinite dynamical range, conserve total activity.html p075fig02.24 Membrane equations of physiology: shunting equation, not additive.html p076fig02.25 Weber law, adaptation, and shift property, convert to logarithmic coordinates.html p076fig02.26 Mudpuppy retina neurophysiology, adaptation- sensitivity shifts for different backgrounds.html p077fig02.27 Mechanism: cooperative-competitive dynamics, subtractive lateral inhibition.html p077fig02.28 Weber Law and adaptation level: hyperpolarization vs silent inhibition.html p078fig02.29 Weber Law and adaptation level: adaptation level theory.html p078fig02.30 Noise suppression: attenuate zero spatial frequency patterns- no information.html p078fig02.31 Noise suppression -> pattern matching: mismatch (out of phase) suppressed, match (in phase) amplifies pattern.html p079fig02.32 Substrate of resonance: match (in phase) of BU and TD input patterns amplifies matched pattern due to automatic gain control by shunting terms.html p080fig02.33 How do noise suppression signals arise: symmetry-breaking during morphogenesis, opposites attract rule.html p080fig02.34 Symmetry-breaking: dynamics and anatomy.html p081fig02.35 Ratio contrast detector: reflectance processing, contrast normalization, discount illuminant.html p081fig02.36 [Noise suppression, contour detection]: uniform patterns are suppressed, contrasts are selectively enhanced, contours are detected.html p082fig02.37 Modelling method and cycle (brain): proper level of abstraction; cannot derive a brain in one step.html p085fig02.38 Modelling method and cycle, technological applications: at each stage [behavioural data, design principles, neural data, math model and analysis].html p087fig03.01 Emerging unified theory of visual intelligence: BU-TD interactions overcome complementary processing deficiencies.html p089fig03.02 What do you think lies under the two grey disks (on a checkers board).html p090fig03.03 Kanizsa square and reverse-contrast Kanizsa square precepts.html p091fig03.04 blind spot and veins can occlude light to the retina.html p092fig03.05 A cross-section of the retinal layers: light stimuli need to go through all retinal layers.html p093fig03.06 Every line is an illusion!: boundary completion, surface filling-in.html p094fig03.07 Complementary properties of boundaries and surfaces.html p095fig03.08 Computer simulation of a Kanizsa square percept.html p095fig03.09 Simulation of a reverse-contrast Kanizsa square percept.html p096fig03.10 The visual illusion of eon color spreading.html p096fig03.11 Another example of neon color spreading.html p098fig03.12 Einstein's face: [edges, texture, shading] are overlaid.html p100fig03.13 Ehrenstein percept weakened as lines deviate from perpendicular.html p100fig03.14 Perpendicular induction at line ends: [locally [,un], globally] preferred.html p100fig03.15 orientations: [transient before, equilibrium after] choice.html p102fig03.16 Ts and Ls group together based on shared orientations, not identities.html p102fig03.17 Positions of squares give rise to a percept of three regions.html p103fig03.18 different spatial arrangements of inducers: emergent [horizontal, diagonal] groupings, but inducers have vertical orientations.html p103fig03.19 [diagonal, perpendicular, parallel]: thats how multiple orientations can induce boundary completion of an object.html p104fig03.20 Sean Williams: how boundaries can form.html p104fig03.21 Four examples of how emergent boundaries can form in response to different kinds of images.html p105fig03.22 3D vision and figure-ground separation: [multiple-scale, depth-selective] boundary webs.html p105fig03.23 pointillist painting: Georges Seurat, A Sunday on la Grande Jatte.html p106fig03.24 Do these ideas work on hard problems: Synthetic Aperture Radar [discount illuminant, filling-in, boundaries].html p107fig03.25 Matisse, The Roofs of Collioure.html p107fig03.26 drawing directly in color leads to colored surface representations.html p108fig03.27 Matisse: Open Window, Collioure, [continuously, sparsely] indiced surfaces.html p108fig03.28 Baingio Pinna, Watercolor illusion filled-in regions bulge in depth, [multiple-scale, depth-selective] boundary web.html p109fig03.29 Chiaroscuro- Rembrandt self-portrait; Trompe l oeil- Graham Rust.html p109fig03.30 Jo Baer triptych: Primary Light Group [red, green, blue].html p110fig03.31 Henry Hensche painting: The Bather, is suffused with light.html p110fig03.32 Claude Monet painting: Poppies Near Argenteuil.html p112fig03.33 Boundary web gradient can cause self-luminosity, similar to watercolor illusion.html p112fig03.34 Examples of Ross Bleckner's self-luminous paintings.html p113fig03.35 Highest Luminance As White (HLAW) rule, Hans Wallach.html p113fig03.36 Blurred Highest Luminance As White (BHLAW) rule.html p114fig03.37 Perceived reflectance vs cross-section of visual field: anchored brightness, self-luminous.html p114fig03.38 Color field painting: Jules Olitski, spray paintings of ambiguous depth.html p115fig03.39 Gene Davis paintings [full color, monochromatic]: percepts of grouping and relative depth.html p116fig03.40 Mona Lisa by Leonardo da Vinci: T-junctions and perspective cues give strong percept of depth.html p117fig03.41 Boundary contours and feature contours- no inhibition, feature signals survive and spread.html p117fig03.42 Two paintings by Frank Stella.html p120fig03.43 Four paintings by Monet of the Rouen cathedral under different lighting conditions.html p120fig03.44 Rouen Cathedral at sunset (Monet 1892-1894): equiluminant, obscured and less depth.html p121fig03.45 Rouen Cathedral full sunlight (Monet 1892-1894): non-uniform lighting, more detail and depth.html p121fig03.46 Rouen Cathedral full sunlight (Monet 1892-1894): T-junctions greater depth.html p123fig04.01 Combining stabilized images with filling-in.html p124fig04.02 closed boundaries prevent brightness from flowing.html p126fig04.03 Color constancy: compute ratios, discount the illuminant, compute lightness.html p128fig04.04 reflectance changes at contours: fill-in illuminant-discounted colors.html p129fig04.05 reflectance changes at contours: color contours.html p129fig04.06 reflectance changes at contours: fill-in color; resolve uncertainty.html p130fig04.07 brightness constancy: boundary peaks spatially narrower than feature peaks.html p131fig04.08 brightness constancy: discount illuminant, ratio-sensitive feature contours.html p131fig04.09 Simulation of brightness contrast.html p132fig04.10 Simulation of brightness assimilation.html p132fig04.11 Simulation of double step and COCE.html p133fig04.12 Simulation of the 2D COCE.html p133fig04.13 Contrast constancy, relative luminances can be reversed, discounting illuminant.html p134fig04.14 Experiments on filling-in: in-the-act; simulation.html p138fig04.15 oriented filtering to grouping and boundary completion.html p139fig04.16 Simplest simple cell model: threshold linear, half-wave rectification.html p140fig04.17 Complex cells: pool like-oriented simple cells of opposite polarity.html p141fig04.18 Binocular Disparity to reconstruct depth from 2D retinal inputs.html p141fig04.19 Laminar cortical circuit for complex cells.html p142fig04.20 [, reverse-]Glass patterns give rise to different boundary groupings.html p143fig04.21 Hierarchical resolution of uncertainty for a given field size.html p144fig04.22 End Gap and End Cut simulation.html p145fig04.23 A perceptual disaster in the feature contour system.html p145fig04.24 Hierarchical resolution of uncertainty- End Cuts.html p146fig04.25 How are end cuts created: two stages of short-range competition.html p148fig04.26 End cut during neon color spreading via 2 stages.html p149fig04.27 Bipole cells boundary completion: long cooperation & short inhibition.html p150fig04.28 Bipole property: boundary completion via long-range cooperation.html p151fig04.29 bipole cells in cortical area V2: first neurophysiological evidence.html p151fig04.30 anatomy: horizontal connections in V1.html p152fig04.31 Bipoles through the ages.html p153fig04.32 Double filter and grouping network.html p156fig04.33 emergent boundary groupings can segregate textured regions.html p157fig04.34 texture: Boundary Contour System resolves errors of complex channels model.html p159fig04.35 Spatial impenetrability prevents grouping.html p159fig04.36 Graffiti art by Banksy: amodal boundary completion; spatial impenetrability.html p161fig04.37 Boundary Contour System model: analog-sensitive boundary completion Kanizsas.html p162fig04.38 Cooperation and competition during grouping.html p163fig04.39 LAMINART model explains key aspects of visual cortical anatomy and dynamics.html p164fig04.40 Koffka-Benussi ring.html p165fig04.41 Kanizsa-Minguzzi ring.html p166fig04.42 Computer simulation of Kanizsa-Minguzzi ring percept.html p167fig04.43 T-junction sensitivity: image, Bipole cells, boundary.html p168fig04.44 main [boundary, surface] formation stages: LGN-> V1-> V2-> V4.html p168fig04.45 ON and OFF feature contours: filled-in regions when adjacent to boundary.html p170fig04.46 regions can fill-in feature contour inputs when [adjacent to, collinear with] boundary contour inputs.html p170fig04.47 A double-opponent network processes output signals from FIDOs.html p171fig04.48 closed boundaries -> filling-in; open boundaries -> color spread.html p171fig04.49 DaVinci stereopsis and occlusion.html p173fig04.50 closed boundary at prescribed depth: addition of [bi, mon]ocular boundaries.html p174fig04.51 figure-ground separation, complementary consistency [boundaries, surfaces].html p174fig04.52 Stereogram surface percepts: surface lightnesses are segregated in depth.html p176fig04.53 OC-OS [within position, across depth]: brighter Kanizsas look closer.html p178fig04.54 figure-ground separation: bipole cooperation and competition.html p178fig04.55 Amodal completion of boundaries and surfaces in V2.html p179fig04.56 Visible surface 3D perception: boundary enrichment, surface filling-in.html p181fig04.57 relative contrasts induce: unimodal and bistable transparency; or flat 2D surface.html p182fig04.58 LAMINART explains many percepts of transparency.html p186fig05.01 Learn many-to-one (compression, naming), one-to-many (expert knowledge) maps.html p186fig05.02 Many-to-one map, two stage compression: [visual, auditory] categories.html p186fig05.03 Many-to-one map: IF-THEN rules: [symptom, test, treatment]s; length of stay.html p189fig05.04 hippocampus & several brain regions [learn, remember] throughout life.html p192fig05.05 LGN [ON, OFF] cells respond differently to [side, end]s of lines.html p192fig05.06 BU-TD circuits between the LGN and cortical area V1, ART Matching Rule.html p193fig05.07 detailed connections between [retinal ganglion cells, LGN, V1].html p193fig05.08 LGN [activation, inhibition], with[, out] top-down feedback.html p194fig05.09 [feature, boundary] contours from Ehrenstein disk stimulus.html p198fig05.10 Competitive learning and Self-Organized Maps (SOMs).html p199fig05.11 Instar learning: bottom-up adaptive filter for feature patterns.html p200fig05.12 Duality of [outstar, instar] networks.html p200fig05.13 Expectations focus attention: instar BU filters, outstar TD expectations.html p200fig05.14 Outstar learning, both [in, de]creases for LTM to learn STM pattern.html p201fig05.15 Spatial learning pattern, outstar learning.html p202fig05.16 Geometry of choice and learning, classifying vector.html p202fig05.17 Geometry of choice and learning, trains the closest LTM vector.html p205fig05.18 catastrophic forgetting due to [competition, associative] learning.html p207fig05.19 ART: [attentional, orienting] systems learn novel categories, no catastophic forgetting.html p211fig05.20 [PN match, N200 mismatch] computationally complementary potentials.html p211fig05.21 ART predicted correlated P120-N200-P300 ERPs during oddball learning.html p213fig05.22 If inputs incorrectly activate a category, how to correct the error.html p213fig05.23 A [category, symbol, other] cannot determine whether an error has occurred.html p214fig05.24 Learning top-down expectations occurs during bottom-up learning.html p214fig05.25 Error correction: [learn, compare] TD-BU inputs, Processing Negativity ERP.html p214fig05.26 Mismatch triggers nonspecific arousal, N200 ERP from orienting system.html p215fig05.27 Every event has [specific attentional cue, nonspecific orienting arousal].html p215fig05.28 BU+TD mismatch arousal and reset if degree of match < ART vigilance.html p220fig05.29 Vigilance [excitation: search better match, inhibition: resonance & learning].html p221fig05.30 predictive error -> vigilance increase just enough -> minimax learning.html p221fig05.31 Fuzzy ARTMAP can associate categories between ART networks, minimax learn.html p224fig05.32 Learning the alphabet with two different levels of vigilance.html p225fig05.33 Some early ARTMAP benchmark studies (no image - link instead).html p225fig05.34 ARTMAP learned maps of natural terrains better than AI expert systems.html p226fig05.35 Code instability sequences: [competitive learning, self-organizing map].html p226fig05.36 catastrophic forgetting without ART Matching Rule due to superset recoding.html p228fig05.37 neurotrophic Spectrally Timed ART (nSTART) model.html p230fig05.38 Synchronous Matching ART (SMART) spiking neurons in laminar cortical hierarchy.html p231fig05.39 SMART: vigilance increase via nucleus basalis of Meynert acetylcholine.html p232fig05.40 SMART generates γ oscillations for good match; β oscillations for bad match.html p232fig05.41 mismatch reset interlaminar events sequence [data, SMART predictions].html p233fig05.42 Evidence for the [gamma, beta] prediction in 3 parts of the brain.html p236fig05.43 nucleus basalis of Meynert releases ACh, reduces AHP, increases vigilance.html p240fig05.44 models using only local computations look like an ART prototype model.html p240fig05.44 The 5-4 category structure example: ART learns the same kinds of categories as human learners.html p242fig05.46 Distributed ARTMAP variants learn the 5-4 category structure.html p245fig05.47 [long-range excitatory, short-range disynaptic inhibitory] connections realize the bipole grouping law.html p246fig05.48 LAMINART model: BU adaptive filtering, horizontal bipole grouping, TD attentional matching.html p248fig05.49 LAMINART explains Up and Down states during slow wave sleep, ACh dynamics.html p252fig06.01 surface-shroud resonance forms as objects bid for spatial attention.html p253fig06.02 Surface-shroud resonance BU-TD OC-OS: perceptual surfaces -> competition -> spatial attention.html p254fig06.03 ARTSCAN Search model learns to recognize and name invariant object categories.html p255fig06.04 The ARTSCAN Search for a desired target object in a scene: Wheres Waldo.html p257fig06.05 Spatial attention flows along object boundaries: Macaque V1.html p258fig06.06 Neurophysiological data & simulation: attention can flow along a curve.html p258fig06.07 Top-down attentional spotlight becomes a shroud.html p259fig06.08 dARTSCN spatial attention hierarchy [Fast Where, Slow What] stream.html p260fig06.09 Crowding: visible objects & confused recognition, increased flanker spacing at higher eccentricity.html p260fig06.10 cortical magnification transforms coordinates: artesian (retina) to log polar (V1).html p261fig06.11 Crowding: visible objects and confused recognition.html p261fig06.12 A more serial search is needed due to overlapping conjunctions of features.html p265fig06.13 basal ganglia gate perceptual, cognitive, emotional, etc through parallel loops.html p267fig06.14 Perceptual consistency and figure-ground separation.html p268fig06.15 saccades within an object: figure-ground outputs control eye movements via V3AA.html p270fig06.16 Predictive remapping of eye movements, from V3A to LIP.html p271fig06.17 Persistent activity in IT to [view, position, size]-invariant category learning by positional ARTSCAN.html p272fig06.18 pARTSCAN: positionally-invariant object learning.html p272fig06.19 persistent activity needed to learn positionally-invariant object categories.html p273fig06.20 pARTSCAN simulation of Li & DiCarlo IT cell swapping data.html p274fig06.21 pARTSCAN [position invariance, selectivity] trade-off of Zoccolan etal 2007.html p274fig06.22 pARTSCAN: IT cortex processes image morphs with high vigilance.html p275fig06.23 IT responses to image morphs, data vs model.html p275fig06.24 Sterogram surface percepts: surface lightnesses are segregated in depth.html p276fig06.25 saccades: predictive gain fields [binocular fusion, filling-in of surfaces].html p277fig06.26 Predictive remapping maintains binocular boundary fusion as eyes move.html p278fig06.27 knowing vs seeing resonances: What [knowing, feature-prototype], Where [seeing, surface-shroud].html p278fig06.28 knowing vs seeing resonances: visual agnosia- reaching without knowing.html p283fig07.01 Boundary competition: spatial habituative gates, orientation gated dipole, bipole grouping.html p284fig07.02 Persistence decreases with flash illuminance & duration [data, simulations].html p285fig07.03 Persistence decrease: rebound to input offset inhibits bipole cells.html p286fig07.04 Illusory contours persist longer than real contours.html p286fig07.05 Illusory contours inhibited by OFF cell rebounds, propagate to center.html p287fig07.06 Persistence: [less, more] as adaptation orientation [same, orthogonal].html p287fig07.07 Persistence increases with distance, due to weaker spatial competition in hypercomplex cells.html p290fig08.01 Motion pools contrast-sensitive information moving in the same direction.html p291fig08.02 Complex cells respond to motion: opposite [direction, contrast polarities].html p292fig08.03 Visual aftereffects: [form- MacKay 90 degree, motion- waterfall 180].html p293fig08.04 Local vs overall motion: aperture problem of EVERY neurons receptive field.html p295fig08.05 sparse feature tracking signals [capture ambiguous, determine perceived] motion direction.html p296fig08.06 Simplest example of apparent motion: two dots turning on and off.html p296fig08.07 continuous motion illusions: [Beta with, Phi without] percept.html p297fig08.08 Delta motion when [luminance, contrast] of flash 2 is larger than flash 1.html p297fig08.09 motion in opposite directions perceived when 2 later flashes on either side of 1st flash.html p298fig08.10 motion speed-up perceived when flash duration decreases.html p298fig08.11 illusory contours: double illusion in V1-V2, motion V2-MT interaction.html p300fig08.12 Single flash: Gaussian receptive fields, recurrent OC-OS winner-take-all.html p300fig08.13 Nothing moves: [single flash, exponential decay], Gaussian peak fixed.html p300fig08.14 Visual inertia: flash decay after the flash shuts off.html p301fig08.15 two flashes: cell activation by first waning while second one is waxing.html p301fig08.16 sum Gaussian flash activity profiles: [waning 1st, waxing 2nd] -> travelling wave.html p302fig08.17 maximum long-rang apparent motion: Gaussian kernel spans successive flashes.html p302fig08.18 G-wave theorem 1: wave moves continuously IFF L <= 2*K.html p303fig08.19 No motion vs motion at multiple scales: flash distance L, Gaussian width K.html p303fig08.20 G-wave theorem 2: [speed-up, scale] independent of [distance, scale size].html p304fig08.21 Equal half-time property: multiple scales generate motion percept.html p304fig08.22 Korte Laws: ISIs in the hundreds of milliseconds can cause apparent motion.html p305fig08.23 Ternus motion: ISI [small- stationary, intermediate- element, larger- group].html p305fig08.24 Reverse-contrast Ternus motion: ISI [small- stationarity, intermediate- group (not element!), larger- group] motion.html p306fig08.25 Motion BCS model [explain, simulate]s long-range motion percepts.html p306fig08.26 3D FORMOTION model: track objects moving in depth.html p307fig08.27 Ternus motion: [element- weak, group- strong] transients, element [visual persistence, perceived stationarity].html p308fig08.28 Ternus group motion: Gaussian filter of 3 flashes forms one global maximum.html p310fig08.29 when individual component motions combine, their perceived direction & speed changes.html p311fig08.30 3D FORMOTION model: feature tracking [get directional, inhibit inconsistent] signals.html p311fig08.31 Motion BCS stages: locally ambiguous motion signals -> globally coherent percept, solving the aperture problem.html p312fig08.32 Schematic of motion filtering circuits.html p312fig08.33 Processing motion signals by a population of speed-tuned neurons.html p314fig08.34 VISTARS navigation model: FORMOTION front end for navigational circuits.html p315fig08.35 How to select correct direction and preserve speed estimates.html p316fig08.36 Motion capture by directional grouping feedback.html p317fig08.37 Motion capture by directional grouping feedback: [short, long]-range filters, transient cells.html p319fig08.38 Solving the aperture problem takes time.html p320fig08.39 Simulation of the barberpole illusion direction field at two times.html p321fig08.40 [, in]visible occluders [do, not] capture boundaries they share with moving edges.html p322fig08.41 motion transparency: asymmetry [near, far], competing opposite directions.html p323fig08.42 Chopsticks: motion separation in depth via [, in]visible occluders [display, percept].html p324fig08.43 ambiguous X-junction motion: MT-MST directional grouping bridges the ambiguous position.html p325fig08.44 The role of MT-V1 feedback: [motion-form feedback, bipole boundary completion.html p325fig08.45 Closing formotion feedback loop [MT, MST]-to-V1-to-V2-to-[MT, MST].html p326fig08.46 How do we perceive relative motion of object parts.html p327fig08.47 Two classical examples of part motion: Symmetrically moving inducers; Duncker wheel.html p328fig08.48 vector decomposition: (retinal - common = part) motion.html p328fig08.49 What is the mechanism of vector decomposition, prediction: directional peak shift.html p329fig08.50 How is common motion direction computed? retinal motion-> bipole grouping (form stream)-> V2-MT formotion.html p329fig08.51 Large and small scale boundaries differentially form illusory contours.html p330fig08.52 Correct motion directions after the peak shift top-down expectation acts.html p330fig08.53 Simulation of the various directional signals of the left dot through time.html p331fig08.54 Motion directions of a single dot moving slowly along a cycloid curve through time.html p331fig08.55 Duncker Wheel, large: stable rightward motion at the center captures motion at the rim.html p332fig08.56 Duncker Wheel, small: wheel motion as seen when directions are collapsed.html p332fig08.57 MODE (MOtion DEcision) model: Motion BCS -> saccadic target selection -> basal ganglia.html p333fig08.58 LIP responses during RT task correct trials: coherence and [activation, inhibition].html p334fig08.59 LIP responses for FD task: predictiveness decreases with increasing coherence.html p334fig08.60 [RT, FD] task behavioral data: more coherence in the motion causes more accurate decisions.html p335fig08.61 RT task behavioural data: reach time (ms) vs % coherence.html p335fig08.62 LIP encodes not only where, but also when, to move the eyes - No Bayes.html p338fig09.01 optic flow through brain regions: moving observer [navigate, track] moving object.html p338fig09.02 Heading (focus of velocity field) from optic flow: humans accurate +- 1 to 2 degrees.html p339fig09.03 Heading with [body move, eye rotate, combined] -> optic flow [expand, translate, rotate].html p339fig09.04 How can translation flow (eye rotation) be subtracted from spiral flow to recover the expansion flow.html p340fig09.05 efference copy command: may use outflow movement commands to eye muscles.html p340fig09.06 Corollary discharges from outflow movement commands that move muscles.html p340fig09.07 Log polar remapping of optic flow: [expansion, circular] motion maps to single direction.html p341fig09.08 optic flows [retina, V1, MT, MSTd, parietal cortex], V1 log polar mapping.html p341fig09.09 MSTd cells are sensitive to [spiral, rotation, expansion] motion.html p342fig09.10 Retina -> log polar -> MSTd cell, heading eccentricity.html p342fig09.11 importance of efference copy in real movements.html p343fig09.12 two retinal views of the Simpsons: [separate, recognize] overlapping figures.html p343fig09.13 How do our brains figure out which views belong to which pear.html p344fig09.14 Heading sensitivity unimpaired: MT tuning width 38°, MSTd spiral tuning 61°.html p345fig09.15 MT double opponent directional fields: relative motions [objects, backgrounds].html p346fig09.16 macrocircuit of 13 brain regions used to move the eyes.html p347fig09.17 leftward eye movement model: retina-> MT-> MST[v,d]-> pursuit.html p347fig09.18 MST[v,d] circuits enable predictive target tracking by the pursuit system.html p348fig09.19 MSTv cells: target speed on retina, background speed on retina, pursuit speed command.html p349fig09.20 Steering from optic flow: goals are attractors, obstacles are repellers.html p349fig09.21 Steering dynamics goal approach: [obstacle, goal, heading] -> steering.html p350fig09.22 negative Gaussian of an obstacle: avoid obstacle without losing sight of goal.html p350fig09.23 Unidirectional transient cells: [lead, trail]ing boundaries, driving video.html p351fig09.24 Directional transient cells respond most to motion in their preferred directions.html p351fig09.25 M+ computes global motion estimate from noisy local motion estimates.html p352fig09.26 heading direction final stage: beautiful optic flow, accuracy matches humans.html p354fig10.01 [Top-down attention, folded feedback] supports predicted ART Matching Rule.html p355fig10.02 seeing vs knowing distinction is difficult because they interact so strongly.html p356fig10.03 Laminar computing: [self-stabilize learning, fuse [BU pre-,TD]attentive processing, perceptual grouping no analog sensitivity].html p357fig10.04 Laminar Computing: combines feed[forward, back], [analog, digital], [pre,]attentive learning.html p359fig10.05 Activation of V1 by direct excitatory signals from LGN to layer 4 of V1.html p359fig10.06 Why another layer 6-to-4 signal: on-center off-surround.html p359fig10.07 Together [LGN-to-4 path, 6-to-4 OC-OS] do contrast normalization if cells obey shunting or membrane equation dynamics.html p360fig10.08 [IC 6-to-4, BU-OS LGN-to-6-to-4] excitations BOTH needed to activate layer 4, ART Matching Rule.html p360fig10.09 Grouping starts in layer 2-3: long-range horizontal excitation, short-range inhibition of target pyramidal.html p361fig10.10 Bipole property controls perceptual grouping: inputs [excitatory sum, inhibitory normalize].html p362fig10.11 Final grouping: folded feedback, strongest enhanced on-center, weaker suppressed off-surround, interlaminar functional columns.html p363fig10.12 V2 repeats V1 circuitry at larger spatial scale.html p364fig10.13 6-to-4 decision circuit common to [BU adaptive filter, intracortical grouping, top-down intercortical attention].html p364fig10.14 Explanation: grouping and attention share the same modulatory decision circuit.html p367fig10.15 Attention protects target from masking stimulus.html p367fig10.16 Flankers can enhance or suppress targets.html p368fig10.17 Attention has greater effect on low contrast targets.html p368fig10.18 Texture reduces response to a bar: [iso-orientation, perpendicular] suppression.html p369fig10.19 Unconscious learning of motion direction, without [extra-foveal attention, awareness] of stimuli.html p371fig11.01 FACADE theory explains how the 3D boundaries and surfaces are formed to see the world in depth.html p372fig11.02 3D surface filling-in of [lightness, color, depth] by a single process: FACADE.html p373fig11.03 Both [contrast-specific binocular fusion, contrast-invariant boundary perception] are needed to see the world in depth.html p374fig11.04 Three processing stages of [monocular simple, complex] cells.html p374fig11.05 Contrast constraint on binocular fusion: only contrasts which are derived from the same objects in space are binoculary matched.html p375fig11.06 Binocular fusion by obligate cells in V1-3B when =[left,right] contrasts.html p375fig11.07 3D LAMINART: [mo, bi]nocular simple cells binocularly fuse like image contrasts.html p376fig11.08 Correspondance problem: How does the brain inhibit false matches? contrast constraint not enough.html p376fig11.09 V2 disparity filter solves correspondence problem: false matches suppressed by line-of-sight inhibition.html p376fig11.10 3D LAMINART with disparity filter: 3D boundary representations via bipole grouping cells.html p377fig11.11 DaVinci stereopsis: monocular information and depth percept.html p378fig11.12 3D LAMINART: V2 monocular+binocular line of sight inputs -> depth perception.html p379fig11.13 3D LAMINART, DaVinci stereopsis (occlusion): emergent from simple mechanisms working together.html p380fig11.14 3D LAMINART, DaVinci stereopsis (polarity): same explanation as occlusion.html p381fig11.15 DaVinci stereopsis variant of (Gillam, Blackburn, Nakayama 1999): same mechanisms.html p382fig11.16 DaVinci stereopsis of [3 narrow, one thick] rectangles: same explanation.html p383fig11.17 Venetian blind effect: [left, right] eye matching bars.html p384fig11.18 Venetian blind effect: Surface[, -to-boundary] surface contour signals.html p385fig11.19 Dichoptic masking: [left, right] images have sufficiently different contrasts.html p385fig11.20 Dichoptic masking, Panum's limiting case: simplified version of Venetian blind effect.html p386fig11.21 Craik-O'Brien-Cornsweet Effect: 2D surface at a very near depth.html p387fig11.22 Julesz stereogram: boundaries with[out, ] surface contour feedback.html p388fig11.23 Sparse stereogram, large regions of ambiguous white: correct surface in depth.html p388fig11.24 depth-ambiguous feature contours: boundary groups lift to correct surface in depth.html p389fig11.25 Boundaries: not just edge detectors, or a shaded ellipse would look [flat, uniformly gray].html p390fig11.26 Multiple-scale depth-selective groupings determine perceived depth.html p391fig11.27 Multiple-scale grouping and size-disparity correlation.html p391fig11.28 Ocular dominance columns, LGN mappings into layer 4C of V1.html p392fig11.29 3D vision figure-ground separation: multiple-scale, depth-selective boundary webs.html p392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.htmlp392fig11.30 How multiple scales vote for multiple depths, scale-to-depth and depth-to-scale maps.png p393fig11.31 LIGHTSHAFT model: determining depth-from-texture percept.html p393fig11.32 Kulikowski stereograms: binocular matching of out-of-phase [Gaussians, rectangles].html p394fig11.33 Kaufman stereogram: simultaneous fusion and rivalry.html p395fig11.34 3D LAMINART vs 7 other rivalry models: stable vision and rivalry.html p396fig11.35 Three properties of bipole boundary grouping in V2: boundaries oscillate with rivalry-inducing stimuli.html p397fig11.36 temporal dynamics of [rivalrous, coherent] boundary switching.html p398fig11.37 Simulation of the no swap baseline condition (Logothetis, Leopold, Sheinberg 1996).html p399fig11.38 Simulation of the swap condition of (Logothetis, Leopold, Sheinberg 1996).html p399fig11.39 Simulation of the eye rivalry data of (Lee, Blake 1999).html p400fig11.40 How do ambiguous 2D shapes contextually define a 3D object form.html p401fig11.41 3D LAMINART: [angle, disparity-gradient] cells learn 3D representations.html p401fig11.42 hypothetical cortical hypercolumn: how [angle, disparity-gradient] cells may self-organize during development.html p402fig11.43 A pair of disparate images of a scene from the University of Tsukuba.html p402fig11.44 3D LAMINART disparities [5, 6, 8, 10, 11, 14]: images of objects in common depth planes.html p403fig11.45 SAR processing by multiple scales: reconstruction of a SAR image.html p405fig12.01 [What ventral, Where-How dorsal] cortical streams for [audition, vision].html p406fig12.02 Three S's of movement: Synergy formation, muscle Synchrony, volitional Speed.html p407fig12.03 Motor cortical cells: vectors for [direction, length] of commanded movement.html p409fig12.04 VITE simulations: difference vector emergent from network interactions.html p410fig12.05 VITE: velocity profile invariance [short, long] movements for same GO signal.html p410fig12.06 Monkeys transform movement: 2 -> 10 o'clock target, 50 or 100 msec after activation of 2 o'clock target.html p411fig12.07 VITE: higher peak velocity due to target switching.html p411fig12.08 GO signals gate agonist-antagonist [difference, present position] vector processing stages.html p412fig12.09 Vector Associative Map: difference vector mismatch learning calibrates [target, present] position vectors.html p413fig12.10 VITE: cortical area [4,5] combine [trajectory, inflow] signals from [spinal cord, cerebellum] for [variable loads, obstacles].html p414fig12.11 [data, simulation]s from cortical areas 4 and 5 during a reach.html p415fig12.12 [VITE, FLETE, cerebellar, opponent muscle] model for trajectory formation.html p416fig12.13 DIRECT model: Endogenous Random Generator learns volitional reaches.html p416fig12.14 DIRECT reaches [unconstrained, with TOOL, elbow@140°, blind].html p417fig12.15 From Seeing & Reaching (DIRECT) to Hearing & Speaking (DIVA): homologous circular reactions, [tool use, coarticulation].html p418fig12.16 Anatomy of DIVA model processing stages.html p419fig12.17 Auditory continuity illusion: backwards in time through noise, ART Matching Rule.html p420fig12.18 ARTSTREAM: auditory continuity illusion, stream as a spectral-pitch resonance.html p422fig12.19 ARTSTREAM: derive streams from [pitch, source direction].html p423fig12.20 SPINET: log polar spatial sound frequency spectrum to distinct auditory streams.html p424fig12.21 Pitch shifts with component shifts, pitch vs lowest harmonic number.html p424fig12.22 Decomposition of a sound in terms of three of its harmonics.html p425fig12.23 ARTSTREAM: auditory continuity illusion- continuity does not occur without noise.html p426fig12.24 Spectrograms of -ba- and -pa- show the transient and sustained parts of their spectrograms.html p430fig12.26 NormNet: speaker normalization via specializations of mechanisms for auditory streams.html p431fig12.27 ARTSTREAM & NormNet strip maps: variants of occular dominance columns in visual cortex.html p432fig12.28 SpaN: spatial representations of numerical quantities in the parietal cortex.html p433fig12.29 What stream: place-value [number map, language category]s; to Where stream: numerical strip maps.html p436fig12.30 cARTWORD: laminar speech model- future disambiguates past, resonanct wave propagates through time.html p436fig12.31 Working memory: temporal order STM is often imperfect, then stored in LTM.html p437fig12.32 Free recall bowed serial position curve.html p437fig12.33 Working memory models: item and order, or competitive queuing.html p438fig12.34 LTM Invariance Principle: [STM, LTM] new words must not cause catastrophic forgetting of subwords.html p439fig12.35 Normalization Rule: total activity of working memory has upper bound independent of number of items.html p439fig12.36 [Item, Order] working memories: [content-addressable categories, temporal order, [excitatory, inhibitory] recurrence, rehearsal wave.html p440fig12.37 Normalization Rule: primacy bow as more items stored.html p441fig12.38 LTM Invariance Principle: new events do not change the relative activities of past event sequences.html p442fig12.39 [LTM invariance, Normalization Rule] Shunt normalization -> STM bow.html p442fig12.40 [LTM Invariance, normalization, STM steady attention]: only [primacy, bowed] gradients of activity can be stored.html p443fig12.41 Neurophysiology of sequential copying: [primacy gradient, self-inhibition].html p444fig12.42 LIST PARSE: Laminar cortical model of working memory and list chunking.html p445fig12.43 LIST PARSE laminar Cognitive Working Memory in VPC, is homologous to visual LAMINART circuit.html p446fig12.44 LIST PARSE: immediate free recall experiments transposition errors, list length.html p447fig12.45 LIST PARSE: order errors vs serial position with extended pauses.html p448fig12.46 Masking Field working memory is a multiple-scale self-similar recurrent shunting on-center off-surround network.html p449fig12.47 Masking Field self-similar [recurrent inhibitory, top-down excitatory] signals to the item chunk working memory.html p452fig12.48 Perceptual integration of acoustic cues: [silence vs noise] durations.html p453fig12.49 ARTWORD: acoustic cues, phonetic [features, WM], Masking Field unitized lists, gain control.html p453fig12.50 ARTWORD perception cycle: sequences-> chunks-> compete-> top-down expectations-> item working memory-> develops item-list resonance.html p454fig12.51 Resonant transfer: as silence interval increases, a delayed additional item can facilitate perception of a longer list.html p455fig12.52 cARTWORD dynamics 1-2-3: resonant activity in item and feature layers corresponds to conscious speech percept.html p456fig12.53 cARTWORD dynamics 1-silence-3: Gap in resonant activity of 1-silence-3 in [item, feature] layers corresponds to perceived silence.html p456fig12.54 cARTWORD dynamics: 1-noise-3: Resonance of 1-2-3 in [item, feature] layers restores item 2.html p457fig12.55 cARTWORD dynamics 1-noise-5: Figures 12.[54, 55] future context can disambiguate past noisy sequences that are otherwise identical.html p459fig12.56 Rank information on the position of an item in a list using numerical hypercolumns in the prefrontal cortex.html p460fig12.57 lisTELOS for saccades: prototype to [store, recall] other [cognitive, spatial, motor] information.html p461fig12.58 lisTELOS shows [BG nigro-[thalamic, collicular], FEF, ITa, PFC, PNR-THAL, PPC, SEF, SC, V1, V4-ITp, Visual Cortex input] and [GABA].html p462fig12.59 TELOS: balancing reactive vs. planned movements.html p463fig12.60 Rank-related activity in PFC and SEF from two different experiments.html p464fig12.61 SEF saccades microstimulating electrode: spatial gradient of habituation alters order, but not which, saccades are performed.html p464fig12.62 The most habituated position is foveated last: because stimulation spreads in all directions, saccade trajectories tend to converge.html p465fig12.63 lisTELOS and data: microstimulation biases selection so saccade trajectories converge toward a single location in space.html p467fig12.64 Some of the auditory cortical regions that respond to sustained or transient sounds.html p468fig12.65 [PHONET, ARTPHONE] linguistic properties: creates rate-invariant representations for variable-rate speech, paradoxical VC-CV category boundaries.html p469fig12.66 PHONET: relative duration of [consonant, vowel] pairs can [preserve, change] a percept.html p469fig12.67 PHONET [transient, sustained] cells that respond to certain [consonant transient, sustained vowel] sounds.html p471fig12.68 Mismatch vs resonant fusion: effect of silence interval length.html p473fig12.69 ART Matching Rule properties explain error rate and mean reaction time (RT) data from lexical decision experiments.html p474fig12.70 macrocircuit model to explain lexical decision task data.html p476fig12.71 Word frequency data model.html p481fig13.01 Cognitive-Emotional-Motor (CogEM): macrocircuit of [function, anatomy].html p483fig13.02 CogEM: motivated attention [closes cognitive-emotional feedback loop, focuses on relevant cues, blocks irrelevant cues].html p483fig13.03 CogEM: supported by anatomical connections [[sensory, orbitofrontal] cortices, amygdala].html p484fig13.04 Cognitive-Emotional resonance: top-down feedback from the orbitofrontal cortex closes a feedback loop.html p484fig13.05 Classical conditioning: perhaps simplest kind of associative learning.html p485fig13.06 Classical conditioning: inverted-U vs InterStimulus Interval (ISI).html p485fig13.07 Paradigm of secondary conditioning.html p486fig13.08 Blocking paradigm: cues lacking different consequences may fail to be attended.html p486fig13.09 Equally salient cues can be conditioned in parallel to an emotional consequence.html p486fig13.10 Blocking: both [secondary, attenuation of] conditioning at zero ISI.html p487fig13.11 CogEM : three main properties to explain how attentional blocking occurs.html p488fig13.12 Motivational feedback and blocking.html p489fig13.13 CogEM and conditioning: positive ISI; inverted-U vs ISI.html p490fig13.14 Cognitive-Emotional circuit: for proper conditioning, sensory needs >= 2 processing stages.html p490fig13.15 CogEM is an ancient design that is found even in mollusks like Aplysia.html p492fig13.16 Polyvalent CS sampling and US-activated nonspecific arousal.html p493fig13.17 Learning nonspecific arousal and CR read-out.html p494fig13.18 Learning to control nonspecific arousal and read-out of the CR: two stages of CS.html p494fig13.19 CogEM: secondary conditioning of [arousal, response], multiple [drive, input]s, motivational sets.html p496fig13.20 A single avalanche sampling cell can learn an arbitrary space-time pattern.html p497fig13.21 nonspecific arousal: primitive crayfish swimmerets, songbird pattern generator avalanche.html p498fig13.22 Adaptive filtering and Conditioned arousal: Towards Cognition, Towards Emotion.html p499fig13.23 Self-organizing avalanches [instars filter, serial learning, outstars read-out], Serial list learning.html p500fig13.24 Primary [excitatory, inhibitory] conditioning using opponent processes and their antagonistic rebounds.html p501fig13.25 Unbiased transducer in finite rate physical process: mass action by a chemical transmitter is the result.html p501fig13.26 Transmitter y [accumulation, release]: y restored < infinite rate, evolution has exploited this.html p502fig13.27 Transmitter minor mathematical miracle [accumulation, release]: S*y = S*A*B div (A + S) (gate, mass action).html p502fig13.28 Habituative transmitter gate: fast [increment, decrement]s of input lead to [overshoot, habituation, undershoot]s, Weber Law.html p503fig13.29 ON response to phasic ON input has Weber Law properties due to the habituative transmitter.html p504fig13.30 OFF-rebound transient due to phasic input offset: arousal level sets ratio ON vs OFF rebounds, Weber Law.html p504fig13.31 Behavioral contrast rebounds: decrease [food-> negative Frustration, shock-> positive Relief] reinforcers.html p505fig13.33 Novelty reset- rebound to arousal onset: equilibrate to [I, J]; keep phasic input J fixed; interpret this equation.html p506fig13.34 Novelty reset: rebound to arousal onset, reset of dipole field by unexpected event.html p506fig13.35 Shock [cognitive, emotional] effects: [reinforcer, sensory cue, expectancy].html p509fig13.36 Life-long learning: selective without [passive forgetting, associative saturation].html p510fig13.37 A disconfirmed expectation inhibits prior incentive, but is insufficient to prevent associative saturation.html p510fig13.38 Dissociation of LTM read-[out, in]: dendritic action potentials as teaching signals, early predictions.html p510fig13.39 Learn net dipole output pattern: [shunting competition, informational noise suppression] in affective gated dipoles, back-propagation.html p512fig13.40 Conditioned excitor extinguishes: [learning, forgetting] phases, shock expectation disconfirmed.html p513fig13.41 Conditioned inhibitor does not extinguish: [learn, forget] phases, same [CS, teacher] can be used.html p513fig13.42 Conditioned excitor extinguishes when expectation of shock is disconfirmed.html p513fig13.43 Conditioned excitor extinguishes: expectation that -no shock- follows CS2 is NOT disconfirmed.html p514fig13.44 Analog of the COgEM model maps of [object X, proto-self], assembly of second-order map.html p519fig14.01 Coronal sections of prefrontal cortex.html p520fig14.02 pART [cognitive-emotional, working memory] dynamics: main brain [regions, connections].html p523fig14.03 MOTIVATOR model generalizes CogEM by including the basal ganglia: supports motivated attention for [, un]conditioned stimuli.html p524fig14.04 Basal ganglia circuit for dopaminergic Now Print signals from the substantia nigra pars compacta in response to unexpected rewards.html p530fig14.05 Visual [pop-out, search]-> reaction time experiments.html p531fig14.06 ARTSCENE: classification of scenic properties as texture categories.html p531fig14.07 ARTSCENE voting achieves even better prediction of scene type.html p532fig14.08 ARTSCENE: using [sequence, location]s of already experienced objects to predict [what, where] the desired object is.html p533fig14.09 ARTSCENE search [data, simulation]s for 6 pairs of images.html p540fig15.01 [Delay, trace conditioning] paradigms: require a CS memory trace over the ISI.html p541fig15.02 nSTART hippocampal Cognitive-Emotional resonance: feeling of what happens, knowing causative event.html p541fig15.03 Timed responses from adaptively timed conditioning: Weber laws, inverted U as a function of ISI.html p542fig15.04 blinks of [nictitating membrane, eyelid] are adaptively timed: closure occurs at arrival of the US following the CS, obeys Weber Law.html p543fig15.05 Learning with two ISIs: each peak obeys Weber Law, strong evidence for spectral learning.html p543fig15.06 Circuit between [dentate granule, CA1 hippocampal pyramid] cells seems to compute spectrally timed responses.html p544fig15.07 Spectral timing: STM sensory representation-> Spectral activation.html p544fig15.08 Habituative transmitter gate: spectral activities-> sigmoid signals-> gated by habituative transmitters.html p544fig15.09 Habituative transmitter gate: increases with accumulation, decreases from gated inactivation.html p545fig15.10 A timed spectrum of gated sampling intervals.html p545fig15.11 Associative learning, gated steepest descent learning: output from each population is a doubly gated signal.html p546fig15.12 Computer simulation of spectral learning: fastest with large sampling signals when the US occurs.html p546fig15.13 Adaptive timing is a population property, random spectrum of rates achieves good collective timing.html p547fig15.14 [Un, ]expected non-occurences of goal: a predictive failure leads to: Orienting Reactions, Emotional- Frustration, Motor- Explorator.html p547fig15.15 Expected non-occurrence of goal: some rewards are reliable but delayed in time, do not lead to orienting reactions.html p548fig15.16 Homolog between ART and CogEM model: complementary systems.html p548fig15.17 The timing paradox: want [accurate timing, to inhibit exploratory behaviour throught ISI].html p549fig15.18 Weber Law: reconciling accurate and distributed timing, different ISIs- standard deviation = peak time, Weber law rule.html p549fig15.19 Conditioning, Attention, and Timing circuit: Hippocampus spectrum-> Amgdala orienting system-> neocortex motivational attention.html p550fig15.20 Adaptively timed Long Term Depression between parallel fibres and Purkinje cells-> movement gains within learned time interval.html p551fig15.21 Cerebellum: important cells types and circuitry.html p551fig15.22 Responses of a turtle retinal cone to brief flashes of light of increasing intensity.html p552fig15.23 Cerebellar biochemistry: mGluR supports adaptively timed conditioning at cerebellar Purkinje cells.html p556fig15.24 Cerebellar cortex responses: [data, model] short latency responses after lesioning.html p557fig15.25 Computer simulations of adaptively timed [LTD at Purkinje cells, activation of cereballar nuclear cells].html p557fig15.26 Brain [region, process]s that contribute to autistic behavioral symptoms.html p559fig15.27 Spectrally timed SNc learning: brain [region, process]s release of dopaminergic signals, unexpected reinforcing.html p559fig15.28 Neurophysiological data and simulations of SNc responses.html p560fig15.29 Excitatory pathways that support activation of the SNc by a US and the conditioning of a CS to the US.html p560fig15.30 Inhibitory pathway: striosomal cells predict [timing, magnitude] of reward signal to cancel it.html p561fig15.31 Expectation timing: timing spectrum, striosomal cells delayed transient signals, gate [learning, read-out].html p561fig15.32 Inhibitory pathway expectation magnitude: is a negative feedback control system for learning.html p563fig15.33 MOTIVATOR: thalamocortical loops through basal ganglia.html p563fig15.34 Distinct basal ganglia zones for each loop.html p564fig15.35 GO signal to recurrent shunting OC-OS networks: control of the [fore, hind] limbs.html p565fig15.36 (a) FOVEATE: control of saccadic eye movements within the peri-pontine reticular formation.html p566fig15.37 FOVEATE: steps in generation of a saccade.html p567fig15.38 Gated Pacemaker of [diurnal, nocturnal] circadian rythms: whether phasic light turns the pacemaker on or off.html p568fig15.39 MOTIVATOR hypothalamic gated dipoles: inputs, [object, value, object-value] categories, reward expectation filter.html p569fig15.40 GO and STOP movement signals: control by [direct, indirect] basal ganglia circuits.html p573fig16.01 Hippocampal place cells: discovery from rat [experimental chamber, neurophysiological recordings].html p574fig16.02 Neurophysiological recordings of 18 different place cell receptive fields.html p575fig16.03 Rat navigation: firing patterns of [hippocampal place, entrorhinal grid] cells.html p578fig16.04 Cross-sections of the hippocampal regions and the inputs to them.html p580fig16.05 GridPlaceMap hierarchy of SOMs with identical equations: learns 2D [grid, place] cells.html p581fig16.06 Trigonometry of spatial navigation: coactivation of stripe cells.html p582fig16.07 Stripe cells multiple [orientation, phase, scale]s: directionally-sensitive ring attractors, velocity, distance.html p582fig16.08 Evidence for stripe-like cells: entorhinal cortex data, Band Cells position from grid cell oscillatory interference.html p583fig16.09 GRIDSmap: stripe cells for rat trajectories, self-organizing map learned hexagonal grid cell receptive fields.html p583fig16.10 GRIDSmap embedded into hierarchy of SOMs: [angular head velocity, linear velocity] signals to place cells.html p584fig16.11 GRIDSmap learning of hexagonal grid fields, multiple phases per scale.html p584fig16.12 Temporal development of grid fields: orientations rotate to align with each other.html p585fig16.13 Hexagonal grid cell receptive fields: somewhat insensitive to [number, directional selectivities] of stripe cells.html p585fig16.14 GRIDSmap: Superimposed firing of stripe cells supports learning hexagonal grid.html p586fig16.15 Why is a hexagonal grid favored: stripe cells at intervals of 45 degrees, GRIDSmap does not learn, oscillatory interference does.html p586fig16.16 Grid-to-place SOM: formation of place cell fields via grid-to-place cell learning.html p587fig16.17 A refined analysis: SOM amplifies most frequent and energetic coactivations, stripe fields separated by [90°, 60°].html p588fig16.18 GridPlaceMap hierarchy of SOMs: coordinated learning of [grid, place, inomodal] cell receptive fields.html p589fig16.19 How does the spatial scale increase along the MEC dorsoventral axis.html p590fig16.20 Dorsoventral gradient in the rate of synaptic integration of MEC layer II stellate cells.html p590fig16.21 Frequency of membrane potential oscillations in grid cells decreases along the dorsoventral gradient of the MEC.html p591fig16.22 Dorsoventral [time constant, duration] gradients in AHP kinetics of MEC layer II stellate cells.html p591fig16.23 Spectral spacing model: map cells respond to stripe cell inputs of multiple scales, How do entorhinal cells solve the scale selection problem.html p592fig16.24 Parameter settings in the Spectral Spacing Model that were used in simulations.html p593fig16.25 Spectral Spacing Model equations for [STM, MTM, LTM].html p593fig16.26 Gradient of grid spacing along dorsoventral axis of MEC.html p594fig16.27 Gradient of field width along dorsoventral axis of MEC.html p595fig16.28 Peak and mean rates at different locations along DV axis of MEC.html p596fig16.29 Subthreshold membrane mV oscillations: decreasing Hz at different locations along DV axis of MEC.html p596fig16.30 Spatial phases of learned grid and place cells.html p597fig16.31 Multimodal place cell firing in large spaces.html p597fig16.32 Model fits data about grid cell development in juvenile rats: grid [score increases, spacing flat].html p598fig16.33 Model fits [place, grid, directional] cell data about grid cell development in juvenile rats: [spatial information, inter-trial stability] vs postnatal day.html p598fig16.34 spiking GridPlaceMap: generates theta-modulated place and grid cell firing, unlike the rate-based model.html p599fig16.35 anatomically overlapping grid cell modules: effects of [different modules in one animal, DV location, response rate].html p600fig16.36 entorhinal-hipppocampal system: ART spatial category learning system, place cells as spatial categories.html p602fig16.37 Hippocampal inactivation by muscimol disrupts grid cells.html p603fig16.38 Role of hippocampal feedback in maintaining grid fields, muscimol inhibition.html p605fig16.39 Disruptive effects of MS inactivation in MEC.html p607fig16.40 Effects of medial septum (MS) inactivation on grid cells: data, simulations, gridness.html p611fig16.41 back-propagating action potentials, recurrent inhibitory interneurons: control learning, regulate rythm- read-out is dissociated from read-in.html p612fig16.42 Macrocircuit of the main SOVEREIGN subsystems: visual, motor.html p613fig16.43 SOVEREIGN [visual form, motion processing] stream mechanisms.html p613fig16.44 SOVEREIGN[target position, difference] vectors, volitional GO computations] to control decision-making and action.html p614fig16.45 [distance, angle] computations learn dimensionally-consistent [visual, motor] information for [decision, action]s.html p615fig16.46 SOVEREIGN uses homologous processing stages to model the [What, Where] cortical streams, motivational mechanisms.html p615fig16.47 SOVEREIGN: multiple parallel READ circuits, sensory-drive heterarchy amplifies motivationally favored option.html p616fig16.48 SOVEREIGN tests using virtual reality 3D rendering of a cross maze.html p616fig16.49 SOVEREIGN animat converted inefficient exploration into an efficient direct learned path to the goal.html p617fig16.50 Spectral Spacing models of [perirhinal what, parahippocampal where] inputs, fused in the hippocampus.html p627tbl17.01 Homologs between [reaction-diffusion, recurrent shunting cellular network] models of development.html p628fig17.01 A hydra.html p628fig17.02 how different [cut, graft]s of the normal Hydra may [, not] lead to the growth of a new head.html p629fig17.03 How an initial morphogenetic gradient may be contrast enhanced to exceed the threshold for head formation.html p630fig17.04 Morphogenesis: use cellular models vs [chemical, fluid] reaction-diffusion models.html p631fig17.05 How a blastula develops into a gastrula.html p634fig17.06 How binary cells with a Gaussian distribution of output thresholds generates a sigmoidal population signal.html pxvifig00.01 Macrocircuit of the visual system.html z_Archive/p029tbl01.01 complementary streams [visual boundary, what-where, perception & recognition, object tracking, motor target] title.html