The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics

PLoS Computational Biology

Volumn 10, Issue 1, 2014, Pages

  Helias, Moritz ^a   Tetzlaff, Tom ^a   Diesmann, Markus ^a,b  

^a Jülich Research Centre and JARA   (Germany)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; STOCHASTIC SYSTEMS;

AMOUNT OF INFORMATION; CORRELATION STRUCTURE; EXTERNAL INPUT; NATURAL CONSEQUENCES; NETWORK CONNECTIVITY; NEURONAL ACTIVITIES; NEURONAL NETWORKS; POPULATION ACTIVITIES; POPULATION LEVELS; POSTSYNAPTIC NEURON;

NEURONS;

ACTION POTENTIAL; ARTICLE; BINOMIAL DISTRIBUTION; CELL STRUCTURE; CONNECTOME; CONTROLLED STUDY; CORRELATION COEFFICIENT; DYNAMICS; FEEDBACK SYSTEM; MATHEMATICAL ANALYSIS; NERVE CELL NETWORK; NERVE CELL STIMULATION; NOISE; SENSORY STIMULATION; STATISTICS; STOCHASTIC MODEL; SYNAPSE;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; FEEDBACK, PHYSIOLOGICAL; HAPLORHINI; MODELS, NEUROLOGICAL; NERVE NET; NEURONAL PLASTICITY; NEURONS; SIGNAL TRANSDUCTION; STOCHASTIC PROCESSES; SYNAPSES; SYNAPTIC TRANSMISSION;

EID: 84896722208     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003428     Document Type: Article

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