This directory contains example scripts using the PyNN IF_cond_exp_gsfa_grr
neuron model.

Specifically, it includes a PyNN implementation of the model described in:

Muller, E., Meier, K., & Schemmel, J. (2004). Methods for simulating
high-conductance states in neural microcircuits. Proc. of BICS2004. 

The excitatory neuron parameterization was then the subject of an
analytical study using the adapting Markov process in:

Muller, E., Buesing, L., Schemmel, J., & Meier, K. (2007).  Spike-frequency
adapting neural ensembles: Beyond mean adaptation and renewal theories.
Neural Computation, 19, 2958-3010.

The standard parameters are in: standard_neurons.yaml

The iaf_sfa_network.py is a MPI enabled simulation of a 10x10x10
lattice modeling cortical layer 4, as described in Muller
et. al. 2004 (above), with a transient step increase in stimulation at
1s<=t<1.2s, and can be run as follows:

$ /opt/mpich2/bin/mpiexec -n 4 python iaf_sfa_network.py

It produces an output figure as myFigure.pdf, which can be compared to
the expected output myFigure_expected.pdf.  It is interesting to
Increase the connection factor ICFactorE_E = 0.12 to something like
0.16 or 0.2, and observe the spontaneous ~10Hz oscillatory activity
that results from excitatory avalanches due to the network being in a
supra-critical excitatory feedback regime.  The period of network
oscillations/bursts is determined by the time-constant of
Spike-Frequency Adapation, an intrinsic neuronal self-inhibition
mechanism which transiently prevents the avalanches, and thus causes
them to occur at regular intervals.




