Coincidence detection is all you need.

Published: 01 Jun 2023, Last Modified: 09 Jun 2023DAI2023 PosterReaders: Everyone
Abstract: This paper demonstrates that the performance of coincidence detection - a classic neuromorphic signal processing method found in Rosenblatt's perceptrons with distributed transmission times, can be competitive to a state-of-the-art deep learning method for pattern recognition. Hence, we cannot remain comfortably numb to the prevailing dogma that efficient matrix-vector operations is all we need; but should enquire with greater vigour if more advanced continual learning methods (running on spiking neural network hardware with neuromodulatory mechanisms at multiple timescales) can beat the accuracy of task-specific deep learning methods. With regards to deployability, coincidence detection is an interpretable shallow learning method and its applications provide a commercial use-case for neuromorphic hardware such as Intel Loihi.
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