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2008 NIPS NeurIPS 2008

Short-Term Depression in VLSI Stochastic Synapse

Abstract

We report a compact realization of short-term depression (STD) in a VLSI stochastic synapse. The behavior of the circuit is based on a subtractive single release model of STD. Experimental results agree well with simulation and exhibit expected STD behavior: the transmitted spike train has negative autocorrelation and lower power spectral density at low frequencies which can remove redundancy in the input spike train, and the mean transmission probability is inversely proportional to the input spike rate which has been suggested as an automatic gain control mechanism in neural systems. The dynamic stochastic synapse could potentially be a powerful addition to existing deterministic VLSI spiking neural systems.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning
📈 Trend Setter — Memory
🧭 Keyword Pioneer — short-term depression
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — spiking neural network

Authors

Peng Xu , Timothy K. Horiuchi , Pamela A. Abshire

Topics

Artificial Intelligence > Core AI > Memory Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Optimization & Theory > Stochastic Processes Deep Learning > Architectures > Neural Networks Interdisciplinary > Science > Quantum Computing

Keywords

neuromorphic computing vlsi implementation neuromorphic hardware neuromorphic engineering short-term depression stochastic synapse automatic gain control spiking neural network spike train vlsi synapse
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