Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Backpropagation with Biologically Plausible Spatio-Temporal Adjustment
  For Training Deep Spiking Neural Networks   The spiking neural network (SNN) mimics the information processing operation
in the human brain, represents and transmits information in spike trains
containing wealthy spatial and temporal information, and shows superior
performance on many cognitive tasks. In addition, the event-driven information
processing enables the energy-efficient implementation on neuromorphic chips.
The success of deep learning is inseparable from backpropagation. Due to the
discrete information transmission, directly applying the backpropagation to the
training of the SNN still has a performance gap compared with the traditional
deep neural networks. Also, a large simulation time is required to achieve
better performance, which results in high latency. To address the problems, we
propose a biological plausible spatial adjustment, which rethinks the
relationship between membrane potential and spikes and realizes a reasonable
adjustment of gradients to different time steps. And it precisely controls the
backpropagation of the error along the spatial dimension. Secondly, we propose
a biologically plausible temporal adjustment making the error propagate across
the spikes in the temporal dimension, which overcomes the problem of the
temporal dependency within a single spike period of the traditional spiking
neurons. We have verified our algorithm on several datasets, and the
experimental results have shown that our algorithm greatly reduces the network
latency and energy consumption while also improving network performance. We
have achieved state-of-the-art performance on the neuromorphic datasets
N-MNIST, DVS-Gesture, and DVS-CIFAR10. For the static datasets MNIST and
CIFAR10, we have surpassed most of the traditional SNN backpropagation training
algorithm and achieved relatively superior performance.

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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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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: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: stat