Neuromorphic Reinforcement Learning with Spiking Temporal Memory

Abstract

This paper investigates the potential of neuromorphic reinforcement learning (RL) by integrating Spiking Temporal Memory (STM) networks. Traditional RL algorithms often struggle with complex, temporally extended tasks due to their reliance on explicit state representations and the challenges of handling noisy and asynchronous sensory data. STM, a biologically inspired neural network, excels at encoding and recalling temporal patterns directly from spiking neural activity. This work proposes an architecture where an RL agent is implemented using an STM network, allowing it to learn directly from raw sensory inputs, effectively capturing and utilizing the temporal dynamics inherent in the environment. The core claim is that this approach will lead to more efficient and robust RL by leveraging the temporal reasoning capabilities of STM. We outline the key components of the system, including the STM network architecture, the RL objective function, and the learning algorithm. The paper concludes with a discussion on the potential benefits and future research directions for this combined approach.