Learning Precise Spike Timings with Eligibility Traces
- URL: http://arxiv.org/abs/2006.09988v1
- Date: Fri, 8 May 2020 09:19:59 GMT
- Title: Learning Precise Spike Timings with Eligibility Traces
- Authors: Manuel Traub, Martin V. Butz, R. Harald Baayen, Sebastian Otte
- Abstract summary: We show that STDP-aware synaptic gradients naturally emerge within the eligibility equations of e-prop.
We also present a simple extension of the LIF model that provides similar gradients.
In a simple experiment we demonstrate that the STDP-aware LIF neurons can learn precise spike timings from an e-prop-based gradient signal.
- Score: 1.3190581566723916
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent research in the field of spiking neural networks (SNNs) has shown that
recurrent variants of SNNs, namely long short-term SNNs (LSNNs), can be trained
via error gradients just as effective as LSTMs. The underlying learning method
(e-prop) is based on a formalization of eligibility traces applied to leaky
integrate and fire (LIF) neurons. Here, we show that the proposed approach
cannot fully unfold spike timing dependent plasticity (STDP). As a consequence,
this limits in principle the inherent advantage of SNNs, that is, the potential
to develop codes that rely on precise relative spike timings. We show that
STDP-aware synaptic gradients naturally emerge within the eligibility equations
of e-prop when derived for a slightly more complex spiking neuron model, here
at the example of the Izhikevich model. We also present a simple extension of
the LIF model that provides similar gradients. In a simple experiment we
demonstrate that the STDP-aware LIF neurons can learn precise spike timings
from an e-prop-based gradient signal.
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