Spiking Neural Networks (SNNs) capture the information processing mechanism
of the brain by taking advantage of spiking neurons, such as the Leaky
Integrate-and-Fire (LIF) model neuron, which incorporates temporal dynamics and
transmits information via discrete and asynchronous spikes. However, the
simplified biological properties of LIF ignore the neuronal coupling and
dendritic structure of real neurons, which limits the spatio-temporal dynamics
of neurons and thus reduce the expressive power of the resulting SNNs. In this
work, we leverage a more biologically plausible neural model with complex
dynamics, i.e., a pulse-coupled neural network (PCNN), to improve the
expressiveness and recognition performance of SNNs for vision tasks. The PCNN
is a type of cortical model capable of emulating the complex neuronal
activities in the primary visual cortex. We construct deep pulse-coupled neural
networks (DPCNNs) by replacing commonly used LIF neurons in SNNs with PCNN
neurons. The intra-coupling in existing PCNN models limits the coupling between
neurons only within channels. To address this limitation, we propose
inter-channel coupling, which allows neurons in different feature maps to
interact with each other. Experimental results show that inter-channel coupling
can efficiently boost performance with fewer neurons, synapses, and less
training time compared to widening the networks. For instance, compared to the
LIF-based SNN with wide VGG9, DPCNN with VGG9 uses only 50%, 53%, and 73% of
neurons, synapses, and training time, respectively. Furthermore, we propose
receptive field and time dependent batch normalization (RFTD-BN) to speed up
the convergence and performance of DPCNNs.

通过使用脉冲耦合神经网络（PCNN）取代常用的漏积分 - 发放神经元（LIF）模型神经元，我们构建了深度脉冲耦合神经网络（DPCNN），进而改善了 SNNs 在视觉任务中的表达能力和识别性能。同时，我们还提出了接受域和时间依赖批归一化（RFTD-BN）来加速 DPCNNs 的收敛和性能。

深脉冲耦合神经网络

Deep Pulse-Coupled Neural Networks

Spiking Neural Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency.
Throughout existing SNNs, the leaky integrate-and-fire (LIF) model is commonly
adopted to formulate the spiking neuron and evolves into numerous variants with
different biological features. However, most LIF-based neurons support only
single biological feature in different neuronal behaviors, limiting their
expressiveness and neuronal dynamic diversity. In this paper, we propose GLIF,
a unified spiking neuron, to fuse different bio-features in different neuronal
behaviors, enlarging the representation space of spiking neurons. In GLIF,
gating factors, which are exploited to determine the proportion of the fused
bio-features, are learnable during training. Combining all learnable
membrane-related parameters, our method can make spiking neurons different and
constantly changing, thus increasing the heterogeneity and adaptivity of
spiking neurons. Extensive experiments on a variety of datasets demonstrate
that our method obtains superior performance compared with other SNNs by simply
changing their neuronal formulations to GLIF. In particular, we train a spiking
ResNet-19 with GLIF and achieve $77.35\%$ top-1 accuracy with six time steps on
CIFAR-100, which has advanced the state-of-the-art. Codes are available at
https://github.com/Ikarosy/Gated-LIF.

本文提出了一个统一脉冲神经元 GLIF，以融合不同的生物特征在不同的神经行为中，从而扩大脉冲神经元的表示空间，增加其异质性和适应性，并通过各种数据集的广泛实验证明了其优越性能。