Generating Temporal Signals from Static Images for Spiking Neural Networks
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Abstract
Spiking neural networks (SNNs), i.e., neural architectures that represent and transmit information in the form of temporally distributed spikes, require time-dependent input, whereas data in computer vision are most commonly available as static images. This study addresses the transformation pipeline “image → temporal signal → spikes” and examines how the choice of input encoding influences SNN training dynamics, spike activity density, and computational cost. The experimental section implements and compares two encoding families: first-spike-time encoding (Latency) and intensity-based Poisson encoding (Poisson). Within these families, four operating modes are considered: baseline Latency without background suppression, modified Latency with a silence threshold, stochastic Poisson, and deterministic Poisson. The evaluation employs the following metrics: the average number of spikes per sample, the number of synaptic operations, an energy-related proxy metric, and indicators characterizing competition among hidden-layer neurons. Experiments conducted on the MNIST dataset (60000 training and 10000 test images) using a network with a hidden layer of 100 neurons and a simulation horizon of 200 time steps demonstrate that all examined modes support stable learning without activity collapse. Among them, the modified Latency mode with a silence threshold of = 0.05 achieves the most favorable balance between useful activity and computational cost: at 323.41 spikes per sample, it requires 14925.09 synaptic operations, whereas the baseline Latency mode without background filtering, despite exhibiting a comparable level of output activity (311.22 spikes per sample), requires 78400 synaptic operations.
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References
2. Maass W. Networks of Spiking Neurons: The Third Generation of Neural Network Models // Neural Networks. 1997. Vol. 10, No. 9. P. 1659–1671. https://doi.org/10.1016/S0893-6080(97)00011-7
3. Davies M. et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning // IEEE Micro. 2018. Vol. 38, No. 1. P. 82–99. https://doi.org/10.1109/MM.2018.112130359
4. Diehl P.U., Cook M. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity // Frontiers in Computational Neuroscience. 2015. Vol. 9. Art. 99. https://doi.org/10.3389/fncom.2015.00099
5. Rueckauer B. et al. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification // Frontiers in Neuroscience. 2017. Vol. 11. Art. 682. https://doi.org/10.3389/fnins.2017.00682
6. Guo W. et al. Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems // Frontiers in Neuroscience. 2021. Vol. 15. Art. 638474. https://doi.org/10.3389/fnins.2021.638474
7. LeCun Y., Bottou L., Bengio Y., Haffner P. Gradient-Based Learning Applied to Document Recognition // Proceedings of the IEEE. 1998. Vol. 86, No. 11. P. 2278–2324. https://doi.org/10.1109/5.726791
8. Izhikevich E.M. Which Model to Use for Cortical Spiking Neurons? // IEEE Transactions on Neural Networks. 2004. Vol. 15, No. 5. P. 1063–1070. https://doi.org/10.1109/TNN.2004.832719
9. Caporale N., Dan Y. Spike Timing-Dependent Plasticity: A Hebbian Learning Rule // Annual Review of Neuroscience. 2008. Vol. 31. P. 25–46. https://doi.org/10.1146/annurev.neuro.31.060407.125639
10. Thorpe S., Delorme A., Van Rullen R. Spike-Based Strategies for Rapid Processing // Neural Networks. 2001. Vol. 14, No. 6–7. P. 715–725. https://doi.org/10.1016/S0893-6080(01)00083-1
11. Hazan H. et al. BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python // Frontiers in Neuroinformatics. 2018. Vol. 12. Art. 89. https://doi.org/10.3389/fninf.2018.00089
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