Abstract
Highly efficient performance-resources trade-off of the biological brain is a motivation for research on neuromorphic computing. Neuromorphic engineers develop event-based spiking neural networks (SNNs) in hardware. Learning in SNNs is a challenging topic of current research. Reinforcement learning (RL) is a particularly promising learning paradigm, important for developing autonomous agents. In this paper, we propose a digital multiplier-less hardware implementation of an SNN with RL capability. The network is able to learn stimulus-response associations in a context-dependent learning task. Validated in a robotic experiment, the proposed model replicates the behavior in animal experiments and the respective computational model. Index Terms-Neuromorphic engineering, spiking neural networks, reinforcement learning, context-dependent task.
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