CSP and QUBO SCIF neurons inherit from a generalised AbstractSCIF. WTA spikes are only generated when switching states (+1 for off to on, -1 for on to off). Off to on transition is triggered by threshold crossing, on to off is triggered by expiration of "refractory" period.
Noise model:
noise added to neural dynamics = noise_amplitude x pseudo-random number.
Total noise precision is user-specified. Precision of noise amplitude is internally computed and subtracted from user-specified precision, to generate a pseudo-random number of remaining precision.
New hyperparameter to choose the neuron model:
Solver accepts neuron_model hyperparameter with values nebm (for NonEquilibrium Boltzmann Model) and scif (StoChastic Integrage and Fire).
Pull request checklist
Your PR fulfills the following requirements:
[x] Tests are part of the PR (for bug fixes / features)
CSP and QUBO SCIF neurons inherit from a generalised AbstractSCIF. WTA spikes are only generated when switching states (+1 for off to on, -1 for on to off). Off to on transition is triggered by threshold crossing, on to off is triggered by expiration of "refractory" period.
Noise model: noise added to neural dynamics = noise_amplitude x pseudo-random number. Total noise precision is user-specified. Precision of noise amplitude is internally computed and subtracted from user-specified precision, to generate a pseudo-random number of remaining precision.
New hyperparameter to choose the neuron model: Solver accepts
neuron_modelhyperparameter with valuesnebm(for NonEquilibrium Boltzmann Model) andscif(StoChastic Integrage and Fire).Pull request checklist
Your PR fulfills the following requirements:
pyb) passes locallypyb -E unit) or (python -m unittest) passes locallyPull request type
Please check your PR type: - [x] Feature ## What is the current behavior?-
What is the new behavior?
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Does this introduce a breaking change?