On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions

IEEE Transactions on Neural Networks

Volumn 15, Issue 5, 2004, Pages 995-1001

  Schmitt, Michael ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; COMPUTATIONAL COMPLEXITY; COMPUTER VISION; LEARNING ALGORITHMS; MATHEMATICAL MODELS; SPEECH RECOGNITION; VLSI CIRCUITS;

COMPUTATIONAL LEARNING THEORY; GENERALIZATION CAPABILITY; PSEUDODIMENSION; PULSED NEURAL NETWORKS; SPIKE TIMING;

NEURAL NETWORKS;

ACTION POTENTIAL; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CENTRAL NERVOUS SYSTEM; DENDRITE; HUMAN; LEARNING; NERVE CELL; NERVE CELL NETWORK; NERVE TRACT; NONLINEAR SYSTEM; PHYSIOLOGY; REACTION TIME; SYNAPSE; SYNAPTIC TRANSMISSION; TIME; TIME PERCEPTION;

ACTION POTENTIALS; ANIMALS; CENTRAL NERVOUS SYSTEM; DENDRITES; HUMANS; LEARNING; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; NONLINEAR DYNAMICS; REACTION TIME; SYNAPSES; SYNAPTIC TRANSMISSION; TIME FACTORS; TIME PERCEPTION;

EID: 4644304318     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2004.832810     Document Type: Article

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