Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons

PLoS Computational Biology

Volumn 7, Issue 12, 2011, Pages

  Pecevski, Dejan ^a   Buesing, Lars ^a,b   Maass, Wolfgang ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHIC METHODS; NEURONS; SPEECH RECOGNITION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

COMPUTATIONAL NEUROSCIENCE; COMPUTATIONAL OPERATIONS; GRAPHICAL MODEL; IN NETWORKS; NETWORK OF NEURONS; PROBABILISTIC INFERENCE; SIMPLE++; SPIKING NEURONES; STOCHASTIC FEATURES; STOCHASTIC NETWORKS;

BAYESIAN NETWORKS;

ARTICLE; BAYESIAN LEARNING; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; DENDRITE; INTERNEURON; NERVE CELL INHIBITION; NERVE CELL NETWORK; NERVE CELL PLASTICITY; POSTSYNAPTIC POTENTIAL; PRESYNAPTIC POTENTIAL; PROBABILITY; REFRACTORY PERIOD; STEADY STATE; STOCHASTIC MODEL;

EID: 84855256984     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002294     Document Type: Article

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