| Temporal integration by stochastic recurrent network (Okamoto et al. 2007) |
| Accession: 105501 |
"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. ..."
Reference: 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] |
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This is the readme file for the model associated with:
Okamoto H, Isomura Y, Takada M, Fukai T (2007) Temporal integration by
stochastic recurrent network dynamics with bimodal neurons. J
Neurophysiol 97:3859-67
Usage:
Compile the ansi C program with, for example under linux:
gcc -o run.exe Okamoto_etal_program.c -lm
then type
./run.exe
and enter a numerical seed for the random number generator.
The program finishes in less than a minute and produces two data files
whose graphs should look like:
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