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Abstract:
We have designed and fabricated a VLSI synapse that can learn
a conditional probability or correlation between spike-based
inputs and feedback signals. The synapse is low power, compact,
provides nonvolatile weight storage, and can perform simultaneous
multiplication and adaptation. We can calibrate arrays of
synapses to ensure uniform adaptation characteristics. Finally,
adaptation in our synapse does not necessarily depend on the
signals used for computation. Consequently, our synapse can
implement learning rules that correlate past and present synaptic
activity. We provide analysis and experimental chip results
demonstrating the operation in learning and calibration mode, and
show how to use our synapse to implement various learning rules
in silicon.
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