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• W1485260270 abstract "The chapter reviews the role of network dynamics in brain function and focuses on the role of recurrent dynamics within local circuits in cortex and other brain structures. A neuronal network is viewed as transforming incoming spike trains to outgoing spike trains. The space of possible spike trains, however, is enormous and the dynamical description of the transformation between them is in general quite complex. At the microscopic level the dynamics of neuronal circuits is cast in terms of conductances, currents, membrane potentials and spikes. The circumstances under which this microscopic dynamics are reduced to relatively simple rate based dynamics are discussed. The chapter also discusses an alternative recently developed model for persistence and integration, which is based on regenerative calcium dynamics in single neurons. The minimal nonlinearity present in rate based models is threshold rectification of the firing rates. This nonlinearity is sufficient to endow networks with novel functions not present in linear models. The chapter discusses the “ring model,” which is the simplest recurrent network model for feature selectivity. Binary networks are used extensively in computational models. Among them is the well known Hopfield model of associative memory." @default.
• W1485260270 created "2016-06-24" @default.
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• W1485260270 date "2005-01-01" @default.
• W1485260270 modified "2023-09-25" @default.
• W1485260270 title "Course 8 Theory of large recurrent networks: From spikes to behavior" @default.
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• W1485260270 doi "https://doi.org/10.1016/s0924-8099(05)80014-9" @default.
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