Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Precise Spike Timings with Eligibility Traces   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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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th