data.txt

经典的算法实例

neural networks;sigmoid function
primary visual-cortex;cats striate cortex;spatiotemporal organization;filters;images;normalization;linearity;responses
primary visual-cortex;shine-through;contextual influences;lateral inhibition;texture-perception;dynamics;segmentation;elements;model;visibility

independent component analysis;machines;mixture
pulse-coupled oscillators;neural networks;spiking neurons;visual-cortex;gamma-rhythms;firing rates;in-vitro;dynamics;interneurons;model
finite-state automata;model;nets;length;power;noise
neural networks;cortex;neurons;model
neural model;parietal cortex;arm;networks;transformations;constraints;information;dynamics;position;monkey
long-range guidance;commissural axons;lateral position;growth cones;receptors;drosophila;midline;slit;netrins;roles
blind deconvolution;information;systems
spatially structured activity;working-memory;persistent activity;neocortical slices;lateral-inhibition;prefrontal cortex;neural networks;synchronization;model;bistability
ocular dominance columns;competition;plasticity;cortex
human stereo vision;human infants;energy models;visual-system;networks;motion;noise
primary visual-cortex;independent component analysis;blue-sensitive cones;color-vision;striate cortex;spectral sensitivity;simple cells;images;macaque;monkey
pulse-coupled oscillators;spiking neurons;neural networks;dynamics;equilibrium
vanishing point detection;order parameters
optimization problems;svm hyperparameters
machines;algorithm;framework
t-cell epitopes;algorithms;ligands;motifs
separation;inference;networks;filters
spatiotemporal firing patterns;spiking activity;unitary events;replay;sequences;cortex;hippocampus;modulation;precision;neurons
vc-dimension
term synaptic plasticity;pyramidal cells;learning rules;hebbian plasticity;barrel cortex;neurons;depression;hippocampus;coincidence;efficacy

spike-frequency adaptation;p-type electroreceptors;ca1 pyramidal neurons;somatosensory cortex;neural oscillators;discharge;locking;variability;patterns;delay
synaptic plasticity;hippocampal-neurons;hebbian plasticity;pyramidal cells;barrel cortex;potentiation;propagation;coincidence;depression;efficacy
neocortical pyramidal neurons;invertebrate ganglion cells;lobster cardiac ganglion;long-term potentiation;nervous-system;postsynaptic potentials;slow potentials;integration;information;interneurons
asynchronous states;spiking neurons;cerebral-cortex;marginal state;in-vivo;dynamics;networks;gain;oscillations;model
visual-cortex;response variability;cortical-neurons;precision;trains;information;inputs;interneurons;patterns;nerve
respiratory rhythm generation;pre-botzinger complex;pacemaker neurons;reduction;simulation;dissection;spiking
neural networks;fire neurons;excitability;dynamics;spiking;model;oscillations;system
neocortical pyramidal neurons;dynamic synapses;neural networks;plasticity
hippocampal pyramidal neurons;activated potassium channels;small-conductance;neocortical neurons;calcium transients;guinea-pig;cells;curves;synchronization;oscillators

visual-system development;orientation columns;horizontal connections;receptive-fields;functional-role;retinal waves;early infancy;mothers face;cortex;newborn
visual-cortex;orientation selectivity;synaptic plasticity;ocular dominance;network
recurrent neural networks;self-organizing maps;short-term-memory;spiking neurons;projection pursuit;blind separation;temporal association;vector quantization;adaptive resonance;maximum-likelihood
neurons;invariant;model
fire neurons;model;spiking;impact;output;state
spike trains;visual information;receptive-fields;model;reduction;cortex;transmission;reliability;responses;precision
movement-sensitive neuron;receptive-fields;visual neurons;adaptation;responses;stimuli/;retina;model;code
activated potassium channels;gabaergic interneurons;coupled oscillators;chattering neurons;small-conductance;rat;model;hippocampus;cells;afterdepolarization
cortical network model;long-term-memory;prefrontal cortex;cerebral-cortex;neural networks;neurons;simulation;plasticity;synapses;organization
cortex;events

neural networks;reversible deactivation;neuroscience;evolution;neuropsychology;controllers;memory;agents;cortex;face
artificial neural networks;topology
smoothing spline anova;bernoulli observations
statistical physics;neural networks;dynamics
primary visual-cortex;winner-take-all;shunting inhibition;neural circuitry;temporal cortex;eye position;area v4;model;macaque;selection
winner-take-all;neural networks;model;cortex
fisher information;pattern-formation;cortical-neurons;codes;orientation;direction;discharge;cortex;representation;variability
neural networks;approximation;separation
pattern-recognition;information maximization;object recognition;self-organization;receptive-fields;neural network;model;constraints;face
principal
temporal visual-cortex;channel-coded systems;cortical-neurons;response variability;movement direction;fisher information;spiking neurons;striate cortex;single neurons;discharge
primary visual-cortex;orientation selectivity;direction selectivity;neuronal oscillations;asynchronous states;simple cells;networks
projection pursuit;regression
monte-carlo methods;a-priori distinctions;learning algorithms;linear-model;bootstrap;inference;choice;selection;utility

self-organizing maps;phase-transitions
self-organizing maps
classification
independent factor-analysis;networks
networks;architecture;computation;limitations;algorithm

arm movements;muscle-activity;visual targets;cortex;direction;variability;discharge;units;space;performance
em algorithm
neocortical pyramidal neurons;information-transmission;cortical-neurons;precision;unreliability;interneurons;responses;recovery;channels
central pattern generator;binocular-rivalry;medicinal leech;neurons;model;coordination;modulation;frequency;dynamics;circuit
event-related potentials;blind source separation;visual-spatial attention;projected rap music;spatiotemporal localization;magnetic-fields;human brain;meg data;responses;algorithm

ocular-dominance columns;striate cortex;visual-cortex;orientation maps;coverage
inferotemporal cortex;temporal cortex;face;representation;monkeys;areas
lateral geniculate-nucleus;cat striate cortex;retinal ganglion-cells;nonparametric identification;system-identification;spatial summation;natural scenes;wiener systems;contrast gain;neurons
cat visual-cortex;excitable dendrites;neural networks;models;representations;competition;recognition;neurons;spines
framework;networks
visual-cortex;models;quantization;algorithm
computational limitations;networks;classification;complexity;errors;nets
long-term potentiation;thalamocortical synapses;neural networks;neurons;memory;propagation;integration;depression;plasticity;mechanisms
information capacity;neurons;brain;photoreceptors;hypothesis;system;cost
single neurons;adaptation
vapnik-chervonenkis dimension;finite-state automata;piecewise polynomial networks;invariant pattern-recognition;vc-dimension;sample complexity;parietal cortex;object approach;dendritic tree;wide-field
approximation;bounds;neurons;vision;model
finite pseudo-dimension;vc dimension;approximation
context-sensitive languages
synaptic plasticity;pyramidal cells;hippocampal-neurons;associative memory;olfactory-bulb;model;acetylcholine;segmentation;dependence;cortex
stochastic-approximation;learning algorithm
analog;converter
inferior temporal cortex;metric-space analysis;visual-cortex;neurons;population;responses;ensemble;samples
neocortical pyramidal neurons;ornstein-uhlenbeck process;synaptic input;temporal correlation;neural networks;spike output;variability;cortex;model;synchronization
maximum-likelihood;unlabeled data;regression;samples;em
visual concepts;3-d objects;appearance;model;errors;system

macaque visual-cortex;binocular disparity;anticorrelated stereograms;uniqueness constraint;computational theory;energy models;neural model;simple cells;vision;depth
multilayer neural networks;separation;algorithm
vehicle-routing problem;guilty net;heuristics;algorithm;maps
neocortical pyramidal neurons;neural networks;synaptic depression;learning rules;behavior;cortex
spike-timing precision;leaky integrate;neuron models;noise;resonance;oscillations;information;dynamics;ensemble;systems
coupled neural oscillators;response curves;pacemaker cell;model;locomotion;ring;multistability;systems;neurons;vagal
developing visual-cortex;inhibitory synapses;cortical plasticity;synaptic plasticity;projection neurons;dopamine neurons;nucleus basalis;rat neostriatum;nerve fields;in-vivo
temporal visual-cortex;spatiotemporal firing patterns;prestriate cortical-neurons;single-neuron;unitary events;representational capacity;probability-distributions;population activity;movement direction;spiking activity
algorithm;machines
model selection;neural networks
em algorithm
algorithms
cat visual-cortex;cross-correlation analysis;asynchronous states;neural networks;dynamics;synchronization;population;memory;model
associative memory;neural networks;neurons;spiking
dynamics;neurons
pulse-propagation networks;simple neuronal circuits;deterministic variability;interspike intervals;neocortical neurons;neural assemblies;visual-cortex;time-series;synchronization;trains
time-varying signals;stochastic resonance;neuronal membranes;noise;trains;reliability;threshold;kinetics;systems
layer backpropagation algorithm;statistical-analysis;networks
networks;recognizers;systems;neurons;power
machines;smo
in-vivo;ca1;plasticity;synapses;segmentation;microscopy;tissue;model;ltp
synchronous spiking;neural networks;input
complex spike activity;inferior olive;neural networks;purkinje-cells;neurons;rat;nuclei;oscillations;organization;invitro

stochastic resonance;spiking neurons;network;model
2nd-order learning algorithm;active-set methods;neural-network;backpropagation networks;image-restoration;back-propagation;regularization;nonmonotone;convergence;parameter

classification;separation;algorithms;model
neural networks;algorithm;recognition
long-term potentiation;medial septal-lesions;source density analysis;synaptic plasticity;pyramidal cells;gabaergic modulation;neuronal-activity;delayed-nonmatch;positive phase;behaving rat
recurrent;time
visual cortical areas;object recognition;neural mechanisms;population codes;cortex;neurons;memory;v4;v2;v1
criterion;machines;order
spatiotemporal firing patterns;cortical activity;event analysis;synchronization;connectivity;modulation;trains;time
retinal lateral inhibition;receptive-fields;natural images;information;responses;contrast;infomax
visual-cortex;cortical activity;frontal-cortex;synchronization;monkey;responses;connectivity;sensitivity;discharge;dynamics
spatiotemporal firing patterns;motor cortical-neurons;visual-cortex;behaving monkeys;frontal-cortex;ensemble activity;cell assemblies;synchronization;oscillations;movement
cat striate cortex;receptive-field structure;gain-control;simple cells;neurons;selectivity;adaptation;monkey;plasticity;dynamics
monte-carlo simulation;glutamate-receptor channels;cultured hippocampal-neurons;cerebellar purkinje-cells;ampa kainate receptors;time-course;synaptic currents;pyramidal cells;cochlear nucleus;rat hippocampus


stochastic resonance;neural networks;enhancement
population-density approach;spiking neurons;neural networks;synaptic input;asynchronous states;pyramidal neurons;colored noise;interneurons;currents;neocortex
blind
pyramidal neurons;neurobiology;variability;computation;spine
ocular dominance columns;basic network principles;visual-cortex;neural architecture;emergence;experience;topography;simulation;monkeys;neurons
ocular dominance;competition;goldfish