Improving the efficiency of current neural networks and modeling them in
biological neural systems have become popular research directions in recent
years. Pulse-coupled neural network (PCNN) is a well applicated model for
imitating the computation characteristics of the human brain in computer vision
and neural network fields. However, differences between the PCNN and biological
neural systems remain: limited neural connection, high computational cost, and
lack of stochastic property. In this study, random-coupled neural network
(RCNN) is proposed. It overcomes these difficulties in PCNN's neuromorphic
computing via a random inactivation process. This process randomly closes some
neural connections in the RCNN model, realized by the random inactivation
weight matrix of link input. This releases the computational burden of PCNN,
making it affordable to achieve vast neural connections. Furthermore, the image
and video processing mechanisms of RCNN are researched. It encodes constant
stimuli as periodic spike trains and periodic stimuli as chaotic spike trains,
the same as biological neural information encoding characteristics. Finally,
the RCNN is applicated to image segmentation, fusion, and pulse shape
discrimination subtasks. It is demonstrated to be robust, efficient, and highly
anti-noised, with outstanding performance in all applications mentioned above.

提出了随机耦合神经网络（RCNN）模型，通过随机失活过程克服了脉冲耦合神经网络（PCNN）的困难，实现了海量神经连接，并在图像分割等应用中展现了鲁棒、高效、抗噪声的出色性能。

随机耦合神经网络

Random-coupled Neural Network

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。