Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.
Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.
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The SNN conversion toolbox (SNN-TB) is a framework to transform rate-based artificial neural networks into spiking neural networks, and to run them using various spike encodings. A unique feature about SNN-TB is that it accepts input models from many different deep-learning libraries (Keras / TF, pytorch, ...) and provides an interface to several backends for simulation (pyNN, brian2, ...) or deployment (SpiNNaker, Loihi).
Please
refer to the Documentation _ for a complete
user guide and API reference. See also the accompanying articles
[Rueckauer et al., 2017] , [Rueckauer and Liu, 2018] , and [Rueckauer and Liu, 2021] _.
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