Computational model
Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
"Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor–mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. ..."
  • Okamoto H, Isomura Y, Takada M, Fukai T (2007) Show Other
Other categories referring to Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
Revisions: 6
Last Time: 10/9/2008 4:52:22 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin