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Found 2 papers, 0 papers with code
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern.
A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses
We summarize the implementation of an analog VLSI chip hosting a network of 32 integrate-and-fire (IF) neurons with spike-frequency adaptation and 2, 048 Hebbian plastic bistable spike-driven stochastic synapses endowed with a self-regulating mechanism which stops unnecessary synaptic changes.
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