Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spiking Neural Networks with Improved Inherent Recurrence Dynamics for
  Sequential Learning   Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons,
can be operated in an event-driven manner and have internal states to retain
information over time, providing opportunities for energy-efficient
neuromorphic computing, especially on edge devices. Note, however, many
representative works on SNNs do not fully demonstrate the usefulness of their
inherent recurrence (membrane potentials retaining information about the past)
for sequential learning. Most of the works train SNNs to recognize static
images by artificially expanded input representation in time through rate
coding. We show that SNNs can be trained for sequential tasks and propose
modifications to a network of LIF neurons that enable internal states to learn
long sequences and make their inherent recurrence resilient to the vanishing
gradient problem. We then develop a training scheme to train the proposed SNNs
with improved inherent recurrence dynamics. Our training scheme allows spiking
neurons to produce multi-bit outputs (as opposed to binary spikes) which help
mitigate the mismatch between a derivative of spiking neurons' activation
function and a surrogate derivative used to overcome spiking neurons'
non-differentiability. Our experimental results indicate that the proposed SNN
architecture on TIMIT and LibriSpeech 100h dataset yields accuracy comparable
to that of LSTMs (within 1.10% and 0.36%, respectively), but with 2x fewer
parameters than LSTMs. The sparse SNN outputs also lead to 10.13x and 11.14x
savings in multiplication operations compared to GRUs, which is generally
con-sidered as a lightweight alternative to LSTMs, on TIMIT and LibriSpeech
100h datasets, respectively.

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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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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: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg