"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) Temporal integration by stochastic recurrent network dynamics with bimodal neurons. J Neurophysiol 97:3859-67 [PubMed]