Synaptic Learning with Augmented Spikes
- URL: http://arxiv.org/abs/2005.04820v1
- Date: Mon, 11 May 2020 01:00:23 GMT
- Title: Synaptic Learning with Augmented Spikes
- Authors: Qiang Yu, Shiming Song, Chenxiang Ma, Linqiang Pan, Kay Chen Tan
- Abstract summary: With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability.
We introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies.
New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes.
- Score: 14.76595318993715
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traditional neuron models use analog values for information representation
and computation, while all-or-nothing spikes are employed in the spiking ones.
With a more brain-like processing paradigm, spiking neurons are more promising
for improvements on efficiency and computational capability. They extend the
computation of traditional neurons with an additional dimension of time carried
by all-or-nothing spikes. Could one benefit from both the accuracy of analog
values and the time-processing capability of spikes? In this paper, we
introduce a concept of augmented spikes to carry complementary information with
spike coefficients in addition to spike latencies. New augmented spiking neuron
model and synaptic learning rules are proposed to process and learn patterns of
augmented spikes. We provide systematic insight into the properties and
characteristics of our methods, including classification of augmented spike
patterns, learning capacity, construction of causality, feature detection,
robustness and applicability to practical tasks such as acoustic and visual
pattern recognition. The remarkable results highlight the effectiveness and
potential merits of our methods. Importantly, our augmented approaches are
versatile and can be easily generalized to other spike-based systems,
contributing to a potential development for them including neuromorphic
computing.
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