Event cameras have been successfully applied to visual place recognition
(VPR) tasks by using deep artificial neural networks (ANNs) in recent years.
However, previously proposed deep ANN architectures are often unable to harness
the abundant temporal information presented in event streams. In contrast, deep
spiking networks exhibit more intricate spatiotemporal dynamics and are
inherently well-suited to process sparse asynchronous event streams.
Unfortunately, directly inputting temporal-dense event volumes into the spiking
network introduces excessive time steps, resulting in prohibitively high
training costs for large-scale VPR tasks. To address the aforementioned issues,
we propose a novel deep spiking network architecture called Spike-EVPR for
event-based VPR tasks. First, we introduce two novel event representations
tailored for SNN to fully exploit the spatio-temporal information from the
event streams, and reduce the video memory occupation during training as much
as possible. Then, to exploit the full potential of these two representations,
we construct a Bifurcated Spike Residual Encoder (BSR-Encoder) with powerful
representational capabilities to better extract the high-level features from
the two event representations. Next, we introduce a Shared & Specific
Descriptor Extractor (SSD-Extractor). This module is designed to extract
features shared between the two representations and features specific to each.
Finally, we propose a Cross-Descriptor Aggregation Module (CDA-Module) that
fuses the above three features to generate a refined, robust global descriptor
of the scene. Our experimental results indicate the superior performance of our
Spike-EVPR compared to several existing EVPR pipelines on Brisbane-Event-VPR
and DDD20 datasets, with the average Recall@1 increased by 7.61% on Brisbane
and 13.20% on DDD20.

提出了一种称为 Spike-EVPR 的新型深度脉冲神经网络架构，用于基于事件的视觉位置识别任务，通过引入两种新的事件表征并构建具有强大表征能力的 Bifurcated Spike Residual Encoder 和 Shared & Specific Descriptor Extractor 模块，以及采用 Cross-Descriptor Aggregation Module 进行特征融合，显著提高了性能。

Spike-EVPR: 基于深度脉冲残差网络与交叉表征聚合的事件驱动视觉地点识别

Spike-EVPR: Deep Spiking Residual Network with Cross-Representation  Aggregation for Event-Based Visual Place Recognition

Spiking neural networks are a type of artificial neural networks in which
communication between neurons is only made of events, also called spikes. This
property allows neural networks to make asynchronous and sparse computations
and therefore to drastically decrease energy consumption when run on
specialized hardware. However, training such networks is known to be difficult,
mainly due to the non-differentiability of the spike activation, which prevents
the use of classical backpropagation. This is because state-of-the-art spiking
neural networks are usually derived from biologically-inspired neuron models,
to which are applied machine learning methods for training. Nowadays, research
about spiking neural networks focuses on the design of training algorithms
whose goal is to obtain networks that compete with their non-spiking version on
specific tasks. In this paper, we attempt the symmetrical approach: we modify
the dynamics of a well-known, easily trainable type of recurrent neural network
to make it event-based. This new RNN cell, called the Spiking Recurrent Cell,
therefore communicates using events, i.e. spikes, while being completely
differentiable. Vanilla backpropagation can thus be used to train any network
made of such RNN cell. We show that this new network can achieve performance
comparable to other types of spiking networks in the MNIST benchmark and its
variants, the Fashion-MNIST and the Neuromorphic-MNIST. Moreover, we show that
this new cell makes the training of deep spiking networks achievable.

本文中，我们尝试一种对称方法：我们通过修改一种易于训练的循环神经网络的动力学来使其基于事件。这种新型 RNN 单元，称为 Spiking Recurrent Cell，因此使用事件即 spike 进行通信，同时可完全区分化。因此，可以使用基本反向传播来训练任何由该 RNN 单元组成的网络。我们展示了这种新的网络在 MNIST 基准测试及其变体 Fashion-MNIST 和 Neuromorphic-MNIST 中可以达到与其他类型的脉冲网络可比较的性能，并且这个新单元使得深度脉冲网络的训练成为可能。

利用传统循环神经网络进行基于脉冲的计算

Spike-based computation using classical recurrent neural networks

Nowadays deep learning is dominating the field of machine learning with
state-of-the-art performance in various application areas. Recently, spiking
neural networks (SNNs) have been attracting a great deal of attention, notably
owning to their power efficiency, which can potentially allow us to implement a
low-power deep learning engine suitable for real-time/mobile applications.
However, implementing SNN-based deep learning remains challenging, especially
gradient-based training of SNNs by error backpropagation. We cannot simply
propagate errors through SNNs in conventional way because of the property of
SNNs that process discrete data in the form of a series. Consequently, most of
the previous studies employ a workaround technique, which first trains a
conventional weighted-sum deep neural network and then maps the learning
weights to the SNN under training, instead of training SNN parameters directly.
In order to eliminate this workaround, recently proposed is a new class of SNN
named deep spiking networks (DSNs), which can be trained directly (without a
mapping from conventional deep networks) by error backpropagation with
stochastic gradient descent. In this paper, we show that the initialization of
the membrane potential on the backward path is an important step in DSN
training, through diverse experiments performed under various conditions.
Furthermore, we propose a simple and efficient method that can improve DSN
training by controlling the initial membrane potential on the backward path. In
our experiments, adopting the proposed approach allowed us to boost the
performance of DSN training in terms of converging time and accuracy.

本文介绍了一种新型的深度脉冲神经网络（DSN），它可以直接进行参数训练，不需要先用传统深度神经网络的训练结果来初始化；研究表明，在 DSN 中控制反向路径的膜电位初始值是非常重要的，同时本文提出了一种简单而有效的方法可以加快 DSN 的收敛时间并提高准确性。