2008
NeurIPS
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.
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- Artificial Intelligence and Interdisciplinary and Mathematics & Optimization
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- Neural Networks
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Hot Topic Early Bird
- spiking neural network
Authors
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
Mathematics & Optimization > Probability > Stochastic Processes
Interdisciplinary > Science > Neuroscience
Artificial Intelligence > Core AI > Neural Networks