spike_based_homeostasis

Spike-based homeostasis

With input from FIPPA, Arbor was extended with the possibility for event-driven plasticity. This allows for spike-based homeostasis. Following the approach in O. Breitwieser's thesis "Towards a Neuromorphic Implementation of Spike-Based Expectation Maximization", two poisson stimuli are connected to a neuron. One with a varying rate and the other with a fixed rate. The synaptic weight from the varying rate stimulus to the neuron is fixed. The synaptic weight from the fixed rate stimulus to the neuron is plastic and tries to keep the neuron at a firing rate that is determined by the parameters of the plasticity rule.

The implementation is validated against the Brian 2 simulator (also see here).

How to

First, install Arbor. If you install from source, make sure arbor-build-catalogue from the Arbor scripts directory is in PATH, or modify the Makefile accordingly.

Next, if necessary, adapt run_arbor.sh and run_brian2.sh according to your environment.

Parameters can be changed in config.json or copied to a new configuration file, e.g., config_modified.json.

Then, simply call

make

to obtain the following plots:

Target case with plasticity:

Control case without plasticity:

You may also call

make CASE=target

or

make CASE=control

to run the two cases separately.

Requirements

• Brian2 >= 2.4.2
• Arbor == 0.10.0
• matplotlib >= 3.4.1
• scikit-learn >= 1.5.0