STDP in adaptive neurons gives close-to-optimal information transmission

Frontiers in Computational Neuroscience

Volumn 4, Issue , 2010, Pages

  Hennequin, Guillaume ^a   Gerstner, Wulfram ^a   Pfister, Jean Pascal ^b  

^a EPFL   (Switzerland)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Information theory; Optimality; Plasticity; Spike frequency adaptation; STDP

Indexed keywords

ADAPTATION PROCESS; ADAPTIVE-NEURON; BIMODAL DISTRIBUTION; DYNAMIC RANGE; DYNAMICAL PROPERTIES; EXCITATORY SYNAPSIS; IN-VITRO; INFORMATION RATES; INFORMATION TRANSMISSION; LEARNING RULES; MAXIMAL INFORMATION; MINIMAL MODEL; OPTIMAL MODEL; OPTIMALITY; PHENOMENOLOGICAL MODELS; POSTSYNAPTIC CELLS; QUALITY OF INFORMATION; RESCALING; SENSORY STIMULUS; SPIKE-FREQUENCY ADAPTATION; SPIKE-TIMING-DEPENDENT PLASTICITY; SPIKING NEURON; STDP; SYNAPTIC WEIGHT; UNIMODAL; WEIGHT DISTRIBUTIONS; PHENOMENOLOGICAL MODELING;

INFORMATION THEORY; NEURONS; PLASTICITY; OPTIMIZATION;

OPTIMIZATION; NEURONS;

ARTICLE; IN VITRO STUDY; INTERNEURON; LONG TERM DEPRESSION; LONG TERM POTENTIATION; MATHEMATICAL ANALYSIS; NERVE CELL PLASTICITY; NERVE CONDUCTION; NEUROTRANSMISSION; POSTSYNAPTIC POTENTIAL; PREDICTION; PRESYNAPTIC POTENTIAL; PROCESS OPTIMIZATION; SPIKE TIMING DEPENDENT PLASTICITY; SPIKE WAVE; STATISTICAL DISTRIBUTION; STOCHASTIC MODEL; TARGET CELL;

EID: 79959593817     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2010.00143     Document Type: Article

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