Effects of synaptic connectivity on liquid state machine performance

Neural Networks

Volumn 38, Issue , 2013, Pages 39-51

  Ju, Han ^a,b   Xu, Jian Xin ^b   Chong, Edmund ^a   VanDongen, Antonius M J ^a  

^a DUKE NUS MEDICAL SCHOOL   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Genetic algorithm; Liquid state machine; Neural microcircuit optimization; Spatiotemporal pattern classification

Indexed keywords

BENCH-MARK PROBLEMS; CENTRAL NERVOUS SYSTEMS; CLASSIFICATION ACCURACY; COMPUTATIONAL NEURAL NETWORKS; DYNAMIC SYNAPSIS; HIGH-DIMENSIONAL FEATURE SPACE; KERNEL FUNCTION; LINEAR SEPARATION; LIQUID STATE MACHINES; MINIMUM STRUCTURES; NEURAL MICROCIRCUIT; NEURAL NETWORK MODEL; PARAMETER SETTING; REAL TIME COMPUTING; SPATIOTEMPORAL PATTERNS; SPIKING NEURON; SYNAPTIC CONNECTIVITY; SYNAPTIC STRENGTHS; SYNAPTIC WEIGHT; TIME VARYING; TWO PARAMETER;

BENCHMARKING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; NEURAL NETWORKS; OPTIMIZATION; PATTERN RECOGNITION; STRUCTURAL DESIGN; SUPPORT VECTOR MACHINES;

LIQUIDS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CALCULATION; GENETIC ALGORITHM; LIQUID STATE MACHINE; MACHINE LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; POISSON DISTRIBUTION; PRIORITY JOURNAL; PROBABILITY; PROCESS OPTIMIZATION; SIGNAL NOISE RATIO; SIMULATION; SYNAPSE; SYNAPTIC TRANSMISSION;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; HUMANS; NEURAL NETWORKS (COMPUTER); SYNAPSES;

EID: 84870714013     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.11.003     Document Type: Article

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