PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks

• URL: http://arxiv.org/abs/2211.15386v1
• Date: Thu, 24 Nov 2022 09:56:02 GMT
• Title: PC-SNN: Supervised Learning with Local Hebbian Synaptic Plasticity based on Predictive Coding in Spiking Neural Networks
• Authors: Mengting Lan, Xiaogang Xiong, Zixuan Jiang, Yunjiang Lou
• Abstract summary: We propose a novel learning algorithm inspired by predictive coding theory. We show that it can perform supervised learning fully autonomously and successfully as the backprop. This method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs.
• Score: 1.6172800007896282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deemed as the third generation of neural networks, the event-driven Spiking Neural Networks(SNNs) combined with bio-plausible local learning rules make it promising to build low-power, neuromorphic hardware for SNNs. However, because of the non-linearity and discrete property of spiking neural networks, the training of SNN remains difficult and is still under discussion. Originating from gradient descent, backprop has achieved stunning success in multi-layer SNNs. Nevertheless, it is assumed to lack biological plausibility, while consuming relatively high computational resources. In this paper, we propose a novel learning algorithm inspired by predictive coding theory and show that it can perform supervised learning fully autonomously and successfully as the backprop, utilizing only local Hebbian plasticity. Furthermore, this method achieves a favorable performance compared to the state-of-the-art multi-layer SNNs: test accuracy of 99.25% for the Caltech Face/Motorbike dataset, 84.25% for the ETH-80 dataset, 98.1% for the MNIST dataset and 98.5% for the neuromorphic dataset: N-MNIST. Furthermore, our work provides a new perspective on how supervised learning algorithms are directly implemented in spiking neural circuitry, which may give some new insights into neuromorphological calculation in neuroscience.

Related papers

• Scalable Mechanistic Neural Networks [52.28945097811129]
  We propose an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources.
  arXiv  Detail & Related papers  (2024-10-08T14:27:28Z)
• BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation [20.34272550256856]
  Spiking neural networks (SNNs) mimic biological neural system to convey information via discrete spikes. Our work achieves state-of-the-art performance for training SNNs on both static and neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-07-12T08:17:24Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Training Spiking Neural Networks with Local Tandem Learning [96.32026780517097]
  Spiking neural networks (SNNs) are shown to be more biologically plausible and energy efficient than their predecessors. In this paper, we put forward a generalized learning rule, termed Local Tandem Learning (LTL) We demonstrate rapid network convergence within five training epochs on the CIFAR-10 dataset while having low computational complexity.
  arXiv  Detail & Related papers  (2022-10-10T10:05:00Z)
• Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks [7.840247953745616]
  Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. This paper establishes a precise mathematical mapping between the biological parameters of the Linear Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep Neural Networks (DNNs)
  arXiv  Detail & Related papers  (2022-05-31T17:02:26Z)
• Advancing Residual Learning towards Powerful Deep Spiking Neural Networks [16.559670769601038]
  Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks. MS-ResNet is able to significantly extend the depth of directly trained SNNs. MS-ResNet 104 achieves 76.02% accuracy on ImageNet, the first time in the domain of directly trained SNNs.
  arXiv  Detail & Related papers  (2021-12-15T05:47:21Z)
• BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks [0.0]
  Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems. To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles. We propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop.
  arXiv  Detail & Related papers  (2021-10-27T00:07:25Z)
• Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity [87.32169414230822]
  Recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) In this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the Synaptic Plasticity paradigm that poses the essential learning theory in biological neurons. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs.
  arXiv  Detail & Related papers  (2020-08-21T19:03:23Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)
• Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction [3.9898522485253256]
  Spiking neural networks (SNNs) can be used in low-power and embedded systems. temporal coding in layers of convolutional neural networks and other types of SNNs has yet to be studied. We present a new deep spiking architecture to tackle real-world problems.
  arXiv  Detail & Related papers  (2020-03-27T11:58:51Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.