Dynamics of a fully stochastic discretized neuronal model with excitatory and inhibitory neurons

neural network;neuronal network;inhibitory neurons;inhibition;non-monotonic;synchrony;mean-field analysis;integrate-and-fire;limit theorem

Berning, Stephen R

关键词: neural network;    neuronal network;    inhibitory neurons;    inhibition;    non-monotonic;    synchrony;    mean-field analysis;    integrate-and-fire;    limit theorem;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/88189/BERNING-DISSERTATION-2015.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

We consider here an extension and generalization of the stochasticneuronal network model developed by DeVille et al.; their modelcorresponded to an all-to-all network of discretizedintegrate-and-fire excitatory neurons where synapses arefailure-prone. It was shown that this model exhibits differentmetastable phases of asynchronous and synchronous behavior, since themodel limits on a mean-field deterministic system with multipleattractors.Our work investigates adding inhibition into themodel. The new model exhibits the same metastable phases, but alsoexhibits new non-monotonic behavior that was not seen in the DeVilleet al. model. The techniques used by DeVille et al. for finding themean-field limit are not suitable for this new model. We exploreearly attempts at obtaining a new mean-field deterministic system thatwould give us an understanding of the behavior seen in the newmodel. After redefining the process we do find a mean-fielddeterministic system that the model limits on, and we investigate thebehavior of the new model studying the mean-field system.