Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback

• URL: http://arxiv.org/abs/2508.06292v1
• Date: Fri, 08 Aug 2025 13:12:13 GMT
• Title: Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback
• Authors: Sanja Karilanova, Subhrakanti Dey, Ayça Özçelikkale,
• Abstract summary: A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs)<n>We propose a novel multiple-output spiking neuron model that combines a linear, general SSM state transition with a non-linear feedback mechanism through reset.<n>Our results illustrate how the proposed reset mechanism can overcome instability and enable learning even when the linear part of neuron dynamics is unstable.
• Score: 3.2443914909457594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs). SNNs are biologically inspired neural networks which utilize stateful neurons, and provide low-bit data processing by encoding and decoding information using spikes. Similar to SNNs, deep state-space models (SSMs) utilize stateful building blocks. However, deep SSMs, which recently achieved competitive performance in various temporal modeling tasks, are typically designed with high-precision activation functions and no reset mechanisms. To bridge the gains offered by SNNs and the recent deep SSM models, we propose a novel multiple-output spiking neuron model that combines a linear, general SSM state transition with a non-linear feedback mechanism through reset. Compared to the existing neuron models for SNNs, our proposed model clearly conceptualizes the differences between the spiking function, the reset condition and the reset action. The experimental results on various tasks, i.e., a keyword spotting task, an event-based vision task and a sequential pattern recognition task, show that our proposed model achieves performance comparable to existing benchmarks in the SNN literature. Our results illustrate how the proposed reset mechanism can overcome instability and enable learning even when the linear part of neuron dynamics is unstable, allowing us to go beyond the strictly enforced stability of linear dynamics in recent deep SSM models.

Related papers

• Delays in Spiking Neural Networks: A State Space Model Approach [2.309307613420651]
  Spiking neural networks (SNNs) are biologically inspired, event-driven models suitable for processing temporal data.<n>We propose a general framework for incorporating delays into SNNs through additional state variables.<n>We show that the proposed mechanism matches the performance of existing delay-based SNNs while remaining computationally efficient.
  arXiv  Detail & Related papers  (2025-12-01T17:26:21Z)
• Learning Neuron Dynamics within Deep Spiking Neural Networks [0.09558392439655011]
  Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs)<n>Deep SNNs remain limited by their reliance on simple neuron models, such as the Leaky Integrate-and-Fire (LIF) model.<n>LNMs are a general, parametric formulation for non-linear integrate-and-fire dynamics that learn neuron dynamics during training.
  arXiv  Detail & Related papers  (2025-10-07T18:16:57Z)
• Channel-wise Parallelizable Spiking Neuron with Multiplication-free Dynamics and Large Temporal Receptive Fields [32.349167886062105]
  Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their sophisticated neuronal dynamics and sparse binary activations (spikes) inspired by the biological neural system.<n>Traditional neuron models use iterative step-by-step dynamics, resulting in serial computation and slow training speed of SNNs.<n>Recent parallelizable spiking neuron models have been proposed to fully utilize the massive parallel computing ability of graphics processing units to accelerate the training of SNNs.
  arXiv  Detail & Related papers  (2025-01-24T13:44:08Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval.<n>A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed.<n>The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• TMN: A Lightweight Neuron Model for Efficient Nonlinear Spike Representation [7.524721345903027]
  Spike trains serve as the primary medium for information transmission in Spiking Neural Networks.<n>Existing encoding schemes based on spike counts or timing often face severe limitations under low-timestep constraints.<n>We propose the Ternary Momentum Neuron (TMN), a novel neuron model featuring two key innovations.
  arXiv  Detail & Related papers  (2024-08-30T12:39:25Z)
• P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks [1.9775291915550175]
  Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures.<n>We develop a scalable probabilistic spiking learning framework for long-range dependency tasks.<n>Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks.
  arXiv  Detail & Related papers  (2024-06-05T04:23:11Z)
• Learning Long Sequences in Spiking Neural Networks [0.0]
  Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations. Recent interest in efficient alternatives to Transformers has given rise to state-of-the-art recurrent architectures named state space models (SSMs)
  arXiv  Detail & Related papers  (2023-12-14T13:30:27Z)
• 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)
• Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State [66.2457134675891]
  Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks. We propose a novel training method that does not rely on the exact reverse of the forward computation.
  arXiv  Detail & Related papers  (2021-09-29T07:46:54Z)
• BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons [8.956708722109415]
  Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. We propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN) For the MNIST, FashionMNIST, and N-MNIST datasets, our model has achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T08:38:31Z)
• 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)
• 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.