Builds upon #1027 by adding a new code generator option continuous_state_buffering_method to allow postsynaptic 3rd factor buffering using a spike-based buffer rather than a continuous time (simulation resolution) based buffer.
Should be merged after #1027.
TODO
[x] Update the third factor plasticity Jupyter notebook to demonstrate that when using the new code generator option, the synaptic delay is not taken into account.
[ ] Add the following reference: "Cartiglia et al. (2020) propose a modification of the Urbanczik-Senn rule underlying the model in Sacramento et al. (2018). This simplification only requires postsynaptic membrane potentials at the time of spike events, which makes the rule much more efficient to simulate and applicable to neuromorphic hardware." Cartiglia, M., Haessig, G., and Indiveri, G. (2020). "An error-propagation spiking neural network compatible with neuromorphic processors," in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) (Genova: IEEE), 84–88.
Builds upon #1027 by adding a new code generator option
continuous_state_buffering_methodto allow postsynaptic 3rd factor buffering using a spike-based buffer rather than a continuous time (simulation resolution) based buffer.Should be merged after #1027.
TODO