Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks
NeutralArtificial Intelligence
- A new study proposes a sleep-based homeostatic regularization scheme to stabilize spike-timing-dependent plasticity (STDP) in recurrent spiking neural networks (SNNs). This approach aims to mitigate issues such as unbounded weight growth and catastrophic forgetting by introducing offline phases where synaptic weights decay towards a homeostatic baseline, enhancing memory consolidation.
- The development is significant as it addresses critical challenges in SNNs, particularly in maintaining stability and preserving learned structures during training. By implementing low to intermediate sleep durations, the model shows improved performance on benchmarks like MNIST without requiring extensive hyperparameter tuning.
- This advancement reflects ongoing efforts to enhance the functionality of spiking neural networks, which are increasingly recognized for their potential in processing complex data. Innovations such as supervised learning rules and new training methods are emerging, indicating a broader trend towards optimizing SNNs for various applications, including spatio-temporal data processing and classification tasks.
— via World Pulse Now AI Editorial System
