Exploration of Deep Spiking Neural Network Architectures for Reward-Modulated STDP Learning

Spiking neural networks (SNNs) are a class of neural networks that more closely mimic the behavior of biological neurons compared to traditional artificial neural networks (ANNs). Unlike ANNs, which rely on continuous activations, SNNs process information through discrete spikes, where the precise timing of these spikes encodes information. This event-driven nature makes SNNs highly efficient in terms of energy consumption, making them an attractive option for neuromorphic computing. However, the non-differentiable nature of spike-based activation functions presents challenges for training, especially with gradient-based methods like backpropagation. Reward-modulated spike-timing-dependent plasticity (STDP) offers a biologically plausible learning mechanism for SNNs, incorporating reward signals to drive synaptic changes. While classical STDP relies solely on the timing of pre- and postsynaptic spikes, the addition of a reward signal allows for more sophisticated learning paradigms, particularly in tasks requiring temporal precision. Despite this, deep SNN architectures, while promising for neuromorphic computing due to their event-driven nature, face challenges in matching the performance of traditional deep neural networks (DNNs). Architectural innovations such as inception-like blocks, skip connections, and population coding have been explored to improve the efficiency and scalability of deep SNNs. These modifications aim to enhance network adaptability and learning capacity in complex, real-world scenarios. Furthermore, Liquid State Machines (LSMs) offer an additional approach for capturing temporal dynamics in SNNs, leveraging their liquid-like behavior for efficient processing of time-varying inputs. This work examines recent advances in reward-modulated STDP and deep SNN architectures, focusing on their potential to bridge the performance gap between SNNs and traditional DNNs, while maintaining the energy efficiency and biological relevance that make SNNs attractive for neuromorphic systems.