A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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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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Publication status
published
Editor
Book title
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Journal / series
Volume
Pages / Article No.
123 - 127
Publisher
IEEE
Event
2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Neuromorphic engineering; Spiking neural networks; Reinforcement learning; Context-dependent task
Organisational unit
09699 - Indiveri, Giacomo / Indiveri, Giacomo
Notes
Conference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.