Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

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 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 57mW. 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.

arXiv commentAdded measurements with noise in NEST simulation, add notice about journal publication. Frontiers in Neuromorphic Engineering (2019)

References in corpus (4)

• First-spike based visual categorization using reward-modulated STDP
• An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture
• Stochastic inference with spiking neurons in the high-conductance state
• Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Cited by in corpus (28)

• Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks
• Control of criticality and computation in spiking neuromorphic networks with plasticity
• Neuromorphic Hardware learns to learn
• Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate
• Accelerated physical emulation of Bayesian inference in spiking neural networks
• Extending BrainScaleS OS for BrainScaleS-2
• Neuromorphic Computing for Content-based Image Retrieval
• Local plasticity rules can learn deep representations using self-supervised contrastive predictions
• Harnessing adaptive dynamics in neuro-memristive nanowire networks for transfer learning
• Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
• hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
• Spiking neuromorphic chip learns entangled quantum states
• Sampling scheme for neuromorphic simulation of entangled quantum systems
• Fast and energy-efficient neuromorphic deep learning with first-spike times
• Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
• Learning through structure: towards deep neuromorphic knowledge graph embeddings
• Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
• Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
• Backpropagation through space, time, and the brain
• Closed-loop experiments on the BrainScaleS-2 architecture
• Reinforcement Learning with Feedback-modulated TD-STDP
• Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware
• Relational representation learning with spike trains
• Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
• Simulation of neural function in an artificial Hebbian network
• Minimal spiking neuron for solving multi-label classification tasks
• Telling neuronal apples from oranges: analytical performance modeling of neural tissue simulations
• Alternative Paradigms of Computation