independent component analysis;blind source separation;natural images;representations;emergence;artifacts;algorithm;bases
hippocampal place cells;movement direction;drawing movements;neural discharge;cortex;representation;neurons;information;algorithms;prediction
fire neurons;oscillations;locking;rates
blind signal separation;algorithm
neocortical neurons;synaptic input;feedforward networks;modulation;propagation;dynamics;noise;transmission;attention;discharge
object recognition;gender classification;principal component;facial expressions;neural-network;shape;categorization;representation;mechanisms;perceptron
hierarchical mixtures;of-experts;models;approximation
honeybee apis-mellifera;short-term-memory;spatial-distribution;confocal microscopy;neurons;brain;representation;interneurons;dynamics;information

face recognition;physical attractiveness;perception;symmetry;information;average;classification;selection;beholder;women
single-photon responses;retinal ganglion-cells;mammalian retina;bipolar cells;absolute threshold;x-cell;y-cell;photoreceptors;transmission;sensitivity
em algorithm;classification
ventral cochlear nucleus;auditory-nerve fibers;neural synchronization;coincidence detection;stochastic resonance;mathematical-models;potassium currents;discharge patterns;synaptic inputs;onset neurons
support vector machines;numbers
independent component analysis;maximum-likelihood;signal separation;algorithm;statistics;infomax
ica mixture-models;blind separation;wavelet-domain;images;classification;emergence;filters
metabotropic glutamate receptors;cerebellar purkinje-cells;network model;cortex;activation;responses;perception;neurons;nuclei
temporal predictability;deconvolution
constrained em algorithm;component analysis
head-direction;persistent activity;fisher information;stimulus direction;recurrent network;population codes;visual-cortex;motor cortex;neurons;model
support vector machines;sample complexity;learning-theory;covering number;networks;classification;consistency
long-term potentiation;dependent synaptic plasticity;rat neostriatal neurons;postsynaptic action-potentials;hippocampal pyramidal neurons;asymmetric hebbian plasticity;rabbit following stimulation;cerebellar purkinje-cells;loop behavioral system;selective simple cells
neural-network;errors

neural-networks;perceptron
basic network principles;neural architecture;behaving monkeys;stochastic interaction;synaptic modification;neuronal assemblies;frontal-cortex;visual-cortex;connectivity;emergence
regression;reduction;algorithms;mixtures
exponential stability;absolute stability
self-organizing maps;em algorithm;density;entropy
independent component analysis;projection pursuit;maximum-likelihood;em algorithm;approximations
independent component analysis;projection pursuit;mutual information;parzen window

synaptic efficacy;redistribution;neurons;slices;epsps
membrane;initiation;equations;neurons;voltage;axon
control-dependent noise;multiplicative noise;feedback-control;linear systems;movements;state;model;coordination;principles;separation
selective visual-attention;neocortical pyramidal cells;covert spatial attention;shunting inhibition;synaptic input;interneuron networks;neural mechanisms;area v4;gabaergic interneurons;electrical synapses
probability;error;entropy;cortex;classification;performance;uncertainty;synergy;brain
performance;noise

neural networks;inhibition;stability;cortex;model
neural-networks;reinforcement;inhibition;memory;noise;model
periodically-modulated inhibition;postsynaptic consequences;presynaptic irregularity;pacemaker inhibition;stretch-receptor;neurons;information;discharges;transients;responses
classification
mathematical-theory;representations;communication;signals
nucleus-accumbens dopamine;conditioned avoidance-response;primate orbitofrontal cortex;cs-us interval;neuroleptic-induced anhedonia;stimulus reward associations;neural-network model;prefrontal cortex;d-amphetamine;psychopharmacological agents
boolean functions;complexity;perceptron
real-time control;motor cortex;cortical representation;direction;discharge;neurons;monkey
driven synaptic plasticity;inferior temporal cortex;mean firing rates;neural-network;perceptron;periods;model
monkey aotus-trivirgatus;organizing feature maps;primary visual-cortex;human auditory-cortex;somatosensory cortex;self-organization;macaque monkey;cortical areas;motor cortex;body-surface
vc-dimension;neural networks;descartes rule;bounds;signs
neurons in-vivo;neocortical neurons;fluctuations;model
saccadic eye-movements;long-term potentiation;primary visual-cortex;inferior temporal cortex;neural-networks;selective attention;dopamine neurons;nmda receptors;single neurons;somatosensory cortex
neurons in-vivo;neocortical pyramidal neurons;fire neurons;current-driven;model neurons;white-noise;integrate;input;cells;gain

rat somatosensory cortex;retinal ganglion-cells;motor cortical-neurons;primary visual-cortex;stimulus location;synchronization;discrimination;discharges;ensembles;responses
thalamic reticular nucleus;spike frequency adaptation;pulse-coupled oscillators;gap-junctions;gabaergic interneurons;cortical oscillators;synaptic inhibition;asynchronous states;gamma oscillations;neural oscillators

classifiers
optimization;stimulus;cortex
generalized distributive law;parity-check codes;belief propagation;capacity
primary visual-cortex;classical receptive-field;monkey striate cortex;spatial-frequency selectivity;range horizontal connections;neurons in-vivo;cortical-neurons;natural images;simple cells;complex cells
space
train data-analysis;cooperative firing activity;point process systems;unit-activity;neurons;hippocampus;patterns;identification;sleep;rat
probability-distributions;stochastic complexity;density-estimation;transmission
pyramidal neurons;hippocampal-neurons;alzheimers-disease;mental-retardation;morphology;stress;ca3;generation;plasticity;topology
event-driven simulation;cortical neural-networks;spiking neurons;synchronous spiking;synaptic input;pyramidal neurons;synfire chains;dynamics;propagation;integration
time
firing rate;attention;propagation;cortex;input

spinal motoneurons;secondary range;potential trajectories;injected currents;firing rates;model;hyperpolarization;conductances;adaptation;summation
time-series
cat visual-cortex;hopf-bifurcation;differential equation;oscillatory responses;feedforward networks;synchronous spiking;neural-networks;synaptic input;in-vitro;dynamics
encoding neural assemblies;rat olfactory-bulb;mushroom body;network;neurons;representation;cells;discrimination;interneurons;excitability
visual neurons;responses;models;cells
blind source separation;primary auditory-cortex;tonotopic organization;maximum-likelihood;complex tones;information;sparse;representation;perception;signals
independent component analysis;separation;algorithm
models
neural-network;real-time;potential functions;mobile robot;navigation;architecture;performance;environment;behaviors;descent
evidence framework;classification;distributions;algorithm;model
asymmetric hebbian plasticity;neocortical pyramidal neurons;synaptic plasticity;efficacy
stochastic resonance;fisher information;accuracy;stimulus;codes
traveling salesman problem;combinatorial optimization;1/f noise;dynamics;bifurcations;performance;memory
neural spike trains;tree weighting method;probability-distributions;visual information;mutual information;natural images;complexity;compression;statistics;sequences
pulse-coupled oscillators;asynchronous states;phase-transitions;active rotators;populations;hippocampus;systems
recurrent circuits;computation;prediction
distributions
finite-state automata;grammatical inference;architectural bias;context-free;machines;time;computation;knowledge;representations;complexity

cortical-neurons;statistics;models;cortex
cat visual-cortex;signal-to-noise;pyramidal neurons;somatosensory cortex;prefrontal cortex;synaptic inputs;cell responses;working-memory;locus-ceruleus;basket cells
independent component analysis;auditory scene analysis;visual receptive-fields;blind separation;speech-intelligibility;neural oscillators;segregation;perception;algorithm;attention
recurrent neural networks;finite-state machines;classification;framework
visual-cortex;spiking neurons;synfire chains;stable propagation;lateral-inhibition;cortical activity;receptive-fields;model;variability;populations
age-related differences;prefrontal cortices;parkinsons-disease;working-memory;dark-matter;pet;fmri;connectivity;cognition;networks
timing-dependent plasticity;synaptic plasticity;temporal-order;visual-cortex;stimulus location;barrel cortex;firing rates;neural codes;cells;efficacy
natural images;representations;models;code
parameters
cat sensorimotor cortex;ion channels;evolution;cerebellar;calcium
independent component analysis;linear neural networks;blind-deconvolution;minor components;least-squares;backpropagation algorithm;principal;separation;information;extraction
central pattern generators;parametric working-memory;prefrontal cortex;parietal cortex;recurrent network;model;chaos;neurons;representations;integration
spatiotemporal firing patterns;neurons;ensembles;display;cortex;memory;trains
perceptron
asymptotic level density
visual receptive-fields;auditory-system;mixture-models;alopex;algorithm;responses;frequency;feedback
global exponential stability;model;systems;neurons;nets
machines
neurons;homeostasis;modulation;stability;channels
texture segmentation;sensory segmentation;oscillators
sparsely connected networks;hippocampus in-vitro;spiking neurons;asynchronous states;visual-cortex;coupled oscillators;gamma oscillations;neural networks;gap-junctions;synchronization
cerebellar granule cells;methyl-d-aspartate;shunting inhibition;synaptic input;mossy fibers;rat cerebellum;neuronal gain;fire neurons;model;transmission

continuous attractor networks;perirhinal cortex lesions;single unit-activity;freely-moving rat;place cells;entorhinal cortex;path-integration;memory impairment;firing patterns;retrohippocampal lesions
neural-networks;spiking neurons;associative memory;working-memory;synchronous spiking;cerebral-cortex;synfire chains;model;dynamics;propagation
pattern-recognition;error rate;classification
neural-networks;global convergence;minimization problems;stability;systems;tank;step
visual-cortex;pyramidal cells;barrel cortex;neurons;potentiation;coincidence;synapses;efficacy;model;hippocampus
neural-network;universal approximators;classification rules;verification system;feature-selection;inference system;takagi-sugeno;interpretability;controllers;generation
multilayer perceptrons;attention modulation;learning machines;likelihood ratio;neural-networks;codes;information;asymptotics;geometry;spiking
face recognition;component analysis;transformation
information geometry;boltzmann machines;algorithm;manifolds
independent component analysis;high-order contrasts;natural gradient;2nd-order statistics;learning algorithms;maximum-likelihood;model selection;neural-networks;information;criterion
variational-inequalities;complementarity-problems;programming problems
object recognition;fourier-transform;phase;amplitude;filters;cortex;model
em algorithm
boltzmann machines;belief propagation;correctness
monkey prefrontal cortex;human cerebral-cortex;macaque monkey;pyramidal neurons;projection neurons;visual-cortex;image-analysis;spine density;layer-iii;3-dimensional structure
hippocampal pyramidal cells;temporal code;place fields;spatial map;memory;model;experience;dynamics;rhythm;oscillations
superior colliculus;anti-saccades;point process;pro-saccades;models
finite-state automata;time;induction;rules
learning-curves;statistical-mechanics;generative models
optic-nerve signals;visual-cortex;neural mechanisms;simple cells;horizontal disparity;depth discrimination;complex cells;macaque v1;perception;responses
coupled inhibitory neurons;macaque visual-cortex;gamma oscillations;area v4;synaptic inhibition;asynchronous states;spiking neurons;fire neurons;attention;dynamics

independent component analysis
spiking activity;unitary events;dynamics;reactivation;modulation
neural-network model;trajectory formation;arm movements;visual feedback;coordination
regression
visual-cortex;hippocampus;sequences;systems;region;replay;model
generalized cross-validation;ridge-regression;algorithms;shrinkage;parameter
memory;search
statistical-mechanics;density-estimation

primary visual-cortex;receptive-fields;spike trains;neurons;statistics;scenes;transmission;efficiency;stimuli;images