Richer Network Dynamics of Intrinsically Non-regular Neurons Measured through Mutual Information

Autor:	Pablo Varona, Henry D. I. Abarbanel, Mikhail I. Rabinovich, Francisco de Borja Rodríguez Ortiz, Ramon Huerta
Rok vydání:	2001
Předmět:	Information transfer Artificial neural network Computer science business.industry Motor system Central pattern generator Mutual information Artificial intelligence Network dynamics Network topology business Topology
Zdroj:	Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence ISBN: 9783540422358 IWANN (1)
DOI:	10.1007/3-540-45720-8_58
Popis:	Central Pattern Generators (CPGs) are assemblies of neurons that act cooperatively to produce regular signals to motor systems. The individual behavior of some members of the CPGs has often been observed as highly variable spiking-bursting activity. In spite of this fact, the collective behavior of the intact CPG produces always regular rhythmic activity. In this paper we show that simple networks built out of intrinsically non-regular units can display modes of regular collective behavior not observed in networks composed of intrinsically regular neurons. Using a measure of mutual information we characterize several patterns of activity observed by changing the coupling strength and the network topology. We show that the cooperative behavior of these neurons can display a rich variety of information transfer while maintaining the regularity of the rhythms.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a448072d0bf2027e129e2d903cdfc8b7 https://doi.org/10.1007/3-540-45720-8_58 Zobrazit plný text záznamu