mip_generator – Create spike trains as described by the MIP model
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Description
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The ``mip_generator`` generates correlated spike trains using an Multiple
Interaction Process (MIP) as described in [1]_. Underlying principle is a
Poisson parent process with rate r, the spikes of which are copied into the
child processes with a certain probability p. Every node the mip_generator is
connected to receives a distinct child process as input, whose rate is `p*r`.
The value of the pairwise correlation coefficient of two child processes
created by a MIP process equals p.
The MIP generator may emit more than one spike through a child process
during a single time step, especially at high rates. If this happens,
the generator does not actually send out n spikes. Instead, it emits
a single spike with n-fold synaptic weight for the sake of efficiency.
Furthermore, note that as with the Poisson generator, different threads
have their own copy of a MIP generator. By using the same mother_seed
it is ensured that the mother process is identical for each of the
generators.
.. include:: ../models/stimulation_device.rst
rate
Mean firing rate of the parent process, spikes/s
p_copy
Copy probability
Set parameters from a stimulation backend
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The parameters in this stimulation device can be updated with input
coming from a stimulation backend. The data structure used for the
update holds one value for each of the parameters mentioned above.
The indexing is as follows:
0. rate
1. p_copy
Sends
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SpikeEvent
References
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.. [1] Kuhn A, Aertsen A, Rotter S (2003). Higher-order statistics of input
ensembles and the response of simple model neurons. Neural Computation
15:67-101.
DOI: https://doi.org/10.1162/089976603321043702
See also
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:doc:`Device `, :doc:`Generator `
Examples using this model
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.. listexamples:: mip_generator