Modeling basal ganglia actor-critic architecture for reinforcement learning on spiking neural networks

Neuromorphic computing, inspired by the intricate functionalities of the human brain, seeks to emulate its efficient computation through advanced models of neuron dynamics and synaptic interactions. A central challenge in this field is the development of effective learning mechanisms that extend beyond local synaptic changes, such as those described by Spike-Timing-Dependent Plasticity (STDP), to encompass more complex, system-wide processes. This research specifically focuses on the role of the basal ganglia and its use of dopaminergic reinforcement learning—a critical component often leveraged for its pivotal role in decision-making and reward-based learning in biological systems. By implementing and analyzing the basal ganglia's dopaminergic learning mechanisms on Spiking Neural Networks (SNNs), we aim to enhance the learning capabilities of these networks. Our study delves into the detailed computational modeling of these mechanisms, highlighting their potential to facilitate complex learning tasks such as sequence learning through reinforcement signals. The insights gained from this research contribute to a deeper understanding of neural processing and learning in artificial systems, marking a step forward in the quest for more sophisticated, biologically-inspired computing architectures.