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Front Neurosci. 2019 Mar 26;13:260. doi: 10.3389/fnins.2019.00260. eCollection 2019.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.

Author information

1
Department of Physics, Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, Germany.
2
Department of Physiology, University of Bern, Bern, Switzerland.

Abstract

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

KEYWORDS:

BrainScaleS; STDP; mixed-signal; neuromorphic computing; plasticity; reinforcement learning; spiking neural networks

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