A Time Encoding approach to training Spiking Neural Networks

• URL: http://arxiv.org/abs/2110.06735v1
• Date: Wed, 13 Oct 2021 14:07:11 GMT
• Title: A Time Encoding approach to training Spiking Neural Networks
• Authors: Karen Adam
• Abstract summary: Spiking Neural Networks (SNNs) have been gaining in popularity. In this paper, we provide an extra tool to help us understand and train SNNs by using theory from the field of time encoding.
• Score: 3.655021726150368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Spiking Neural Networks (SNNs) have been gaining in popularity, it seems that the algorithms used to train them are not powerful enough to solve the same tasks as those tackled by classical Artificial Neural Networks (ANNs). In this paper, we provide an extra tool to help us understand and train SNNs by using theory from the field of time encoding. Time encoding machines (TEMs) can be used to model integrate-and-fire neurons and have well-understood reconstruction properties. We will see how one can take inspiration from the field of TEMs to interpret the spike times of SNNs as constraints on the SNNs' weight matrices. More specifically, we study how to train one-layer SNNs by solving a set of linear constraints, and how to train two-layer SNNs by leveraging the all-or-none and asynchronous properties of the spikes emitted by SNNs. These properties of spikes result in an alternative to backpropagation which is not possible in the case of simultaneous and graded activations as in classical ANNs.

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)
• LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding [55.64533786293656]
  We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks. The study paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms.
  arXiv  Detail & Related papers  (2023-10-23T14:26:16Z)
• High-performance deep spiking neural networks with 0.3 spikes per neuron [9.01407445068455]
  It is hard to train biologically-inspired spiking neural networks (SNNs) than artificial neural networks (ANNs) We show that training deep SNN models achieves the exact same performance as that of ANNs. Our SNN accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation.
  arXiv  Detail & Related papers  (2023-06-14T21:01:35Z)
• 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 High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Deep Learning in Spiking Phasor Neural Networks [0.6767885381740952]
  Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware. In this paper, we introduce Spiking Phasor Neural Networks (SPNNs) SPNNs are based on complex-valued Deep Neural Networks (DNNs), representing phases by spike times.
  arXiv  Detail & Related papers  (2022-04-01T15:06:15Z)
• Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks [0.0]
  Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) We propose a novel strategic pipeline that transfers the weights to the target SNN by combining threshold balance and soft-reset mechanisms. Our method is promising to get implanted onto embedded platforms with better support of SNNs with limited energy and memory.
  arXiv  Detail & Related papers  (2021-02-28T12:04:22Z)
• Long Short-Term Memory Spiking Networks and Their Applications [10.071615423169902]
  We present a novel framework for training recurrent spiking neural networks (SNNs) We show that LSTM spiking networks learn the timing of the spikes and temporal dependencies. We also develop a methodology for error backpropagation within LSTM-based SNNs.
  arXiv  Detail & Related papers  (2020-07-09T13:22:27Z)
• 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)
• Training Deep Spiking Neural Networks [0.0]
  Brain-inspired spiking neural networks (SNNs) with neuromorphic hardware may offer orders of magnitude higher energy efficiency. We show that is is possible to train SNN with ResNet50 architecture on CIFAR100 and Imagenette object recognition datasets. The trained SNN falls behind in accuracy compared to analogous ANN but requires several orders of magnitude less inference time steps.
  arXiv  Detail & Related papers  (2020-06-08T09:47:05Z)
• You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy [51.861168222799186]
  Spiking Neural Networks (SNNs) are a type of neuromorphic, or brain-inspired network. SNNs are sparse, accessing very few weights, and typically only use addition operations instead of the more power-intensive multiply-and-accumulate operations. In this work, we aim to overcome the limitations of TTFS-encoded neuromorphic systems.
  arXiv  Detail & Related papers  (2020-06-03T15:55:53Z)

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.