Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

PLoS Computational Biology

Volumn 9, Issue 4, 2013, Pages

  Nessler, Bernhard ^a   Pfeiffer, Michael ^a,b   Buesing, Lars ^a   Maass, Wolfgang ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DISTRIBUTED COMPUTER SYSTEMS; IMAGE SEGMENTATION; MAXIMUM PRINCIPLE; PROBABILITY DISTRIBUTIONS; TIMING CIRCUITS;

BAYESIAN COMPUTATION; COMPUTATIONAL FUNCTIONS; CORTICAL MICROCIRCUITS; GENERATIVE MODEL; NETWORK OF NEURONS; PYRAMIDAL NEURON; SHOW THROUGH; SPIKE TIMING DEPENDENT PLASTICITIES; SYNAPTIC WEIGHT; WINNER-TAKE-ALL CIRCUITS;

NEURONS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BRAIN NERVE CELL; COMPUTER SIMULATION; INFORMATION PROCESSING; INTERNEURON; LEARNING CURVE; MATHEMATICAL ANALYSIS; NERVE CELL EXCITABILITY; NERVE CELL PLASTICITY; PYRAMIDAL NERVE CELL; SPIKE BASED EXPECTATION MAXIMIZATION; SPIKE TIMING DEPENDENT PLASTICITY; SPIKE WAVE; WINNER TAKE ALL NETWORK;

EID: 84876928403     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003037     Document Type: Article

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