A population density approach that facilitates large-scale modeling of neural networks: Analysis and an application to orientation tuning

Journal of Computational Neuroscience

Volumn 8, Issue 1, 2000, Pages 19-50

  Nykamp, Duane Q ^a   Tranchina, Daniel ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

Modeling; Neural networks; Orientation tuning; Population density; Visual cortex

Indexed keywords

COMPLEX NETWORKS; COMPUTATIONAL EFFICIENCY; INTEGRAL EQUATIONS; NEURAL NETWORKS; POPULATION DISTRIBUTION; POPULATION DYNAMICS; POPULATION STATISTICS; PROBABILITY DISTRIBUTIONS; STOCHASTIC SYSTEMS; TUNING;

COMPUTATIONALLY EFFICIENT; DENSITY APPROACHES; LARGE-SCALE MODELING; MODELING; NEURAL NETWORK ANALYSIS; NEURAL-NETWORKS; ORIENTATION-TUNING; POPULATION DENSITIES; PROBABILITY DENSITIES; VISUAL CORTEXES;

NEURONS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CAT; CONTROLLED STUDY; FEEDBACK SYSTEM; MATHEMATICAL MODEL; NERVE CONDUCTION; NERVE STIMULATION; PRESYNAPTIC NERVE; PRIORITY JOURNAL; PROBABILITY; SIMULATION; SYNAPSE; TECHNIQUE; THALAMUS NUCLEUS; VISUAL CORTEX;

EID: 0034076273     PISSN: 09295313     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008912914816     Document Type: Article

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