Continual learning (CL) in spiking neural networks (SNNs) poses a significant challenge due to the inherent plasticity of synaptic weights. Traditional CL approaches in deep neural networks (DNNs) often rely on weight consolidation techniques, such as knowledge distillation, which are not directly applicable to SNNs.
Time-Domain Compression for CL in SNNs
We propose a novel time-domain compression (TDC) method for CL in SNNs. TDC involves compressing the temporal dynamics of SNN activations into a compressed representation. This representation captures the essential information required for task performance while discarding redundant or irrelevant details.
Benefits of TDC
* Reduced Synaptic Plasticity: TDC reduces the need for substantial synaptic weight updates, mitigating the risk of catastrophic forgetting.
* Improved Efficiency: Compressing temporal dynamics significantly reduces the computational cost of training and inference in SNNs.
* Robustness to Noise: TDC enhances the robustness of SNNs to noise and interference, as it focuses on salient temporal features.
Implementation of TDC
TDC can be implemented using various techniques, including:
* Spike Train Encoding: Encoding spike trains into compressed representations, such as binary vectors or histogram representations.
* Reservoir Computing: Utilizing recurrent neural networks with fixed weights to extract temporal features from SNN activations.
* Time-Delay Neural Networks: Exploiting time delays to compress temporal dynamics and extract relevant patterns.
Evaluation and Results
We evaluate our TDC method on a range of CL benchmarks for SNNs. Experiments demonstrate:
* Improved CL Performance: TDC significantly reduces catastrophic forgetting and improves overall CL performance.
* Increased Efficiency: TDC reduces training time and memory consumption, enabling faster and more scalable CL in SNNs.
* Enhanced Robustness: SNNs with TDC exhibit improved robustness to noise and interference during CL.
Conclusion
Time-domain compression provides a powerful approach to enable continual learning in spiking neural networks. By compressing temporal dynamics, TDC mitigates catastrophic forgetting, improves efficiency, and enhances robustness to noise. This approach paves the way for the development of more capable and adaptable SNNs that can learn and retain multiple tasks over time.
Kind regards
J.O. Schneppat
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