Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks

The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'The data relates to 4 benchmarks:Spiking Heidelberg Digits (SHD).Spiking Speech Commands, derived from Google Speech Commands.Yin-Yang dataset.Braille letter reading dataset.The data was generated and analysed with the code available on GitHub at https://github.com/mbalazs98/deventprop/results.py contains all test accuracies shown in figures 4-7. The other 3 files contain the trained models that achieved the corresponding accuracies. Each zip file contains directories for each architecture:for YY hidden layers of sizes, 5, 10, 15, 20, 25, 30 feedforward architecures with and without delaysfor SHD, SSC and Braille: hidden layers of sizes 64, 128, 256, 512 and 1024, feedforward and recurrent architectures with and without delaysEach architecture directory contains 8 subdirectories for the 8 random seeds. For each connection in a given architecture the synaptic weights are in the files ending in -g.npy and if relevant, delays are provided in the architectures ending in -d.npyTo test the networks run test.py from the GitHub repository provided above, with "arguments" set to the appropriate value. To load the networks through mlGeNN, the package needs to be installed (a description of how to do this is provided at https://github.com/genn-team/ml_genn). Also see attached mlGeNN_readme.txtAbstract:Spiking Neural Networks compute using sparse communication and are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. While standard Artificial Neural Networks are stateless, spiking neurons are stateful and hence intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, the duration of this intrinsic memory is limited by synaptic and membrane time constants. Delays are a powerful additional mechanism and, in this paper, we propose an event-based training method for Spiking Neural Networks with delays, grounded in the EventProp formalism, which enables the calculation of exact gradients with respect to weights and delays. Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. We evaluate our method on a simple sequence detection task, as well as the Yin-Yang, Spiking Heidelberg Digits, Spiking Speech Commands and Braille letter reading datasets, demonstrating that our algorithm can optimise delays from suboptimal initial conditions and enhance classification accuracy compared to architectures without delays. We also find that recurrent delays are particularly beneficial in small networks. Finally, we show that our approach uses less than half the memory of the current state-of-the-art delay-learning method and is up to 26x faster.