General Self-Prediction Enhancement for Spiking Neurons

• URL: http://arxiv.org/abs/2601.21823v1
• Date: Thu, 29 Jan 2026 15:08:48 GMT
• Title: General Self-Prediction Enhancement for Spiking Neurons
• Authors: Zihan Huang, Zijie Xu, Yihan Huang, Shanshan Jia, Tong Bu, Yiting Dong, Wenxuan Liu, Jianhao Ding, Zhaofei Yu, Tiejun Huang,
• Abstract summary: Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
• Score: 71.01912385372577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility. Crucially, mainstream SNNs ignore predictive coding, a core cortical mechanism where the brain predicts inputs and encodes errors for efficient perception. Inspired by this, we propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential. This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity. Experiments show consistent performance gains across diverse architectures, neuron types, time steps, and tasks demonstrating broad applicability for enhancing SNNs.

Related papers

• Robust Spiking Neural Networks Against Adversarial Attacks [49.08210314590693]
  Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing.<n>In this study, we theoretically demonstrate that threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs.<n>We find that these neurons set the upper limits for the maximum potential strength of adversarial attacks and are prone to state-flipping under minor disturbances.
  arXiv  Detail & Related papers  (2026-02-24T05:06:12Z)
• Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition [52.59263087086756]
  Training deep neural networks (SNNs) has critically depended on explicit normalization schemes, such as batch normalization.<n>We propose a normalization-free learning framework that incorporates lateral inhibition inspired by cortical circuits.<n>We show that our framework enables stable training of deep SNNs with biological realism and achieves competitive performance without resorting to explicit normalizations.
  arXiv  Detail & Related papers  (2025-09-27T11:11:30Z)
• A Brain-Inspired Gating Mechanism Unlocks Robust Computation in Spiking Neural Networks [5.647576619206974]
  We introduce the Dynamic Gated Neuron(DGN), a novel spiking unit in which membrane conductance evolves in response to neuronal activity.<n>Our results highlight, for the first time, a biologically plausible dynamic gating as a key mechanism for robust spike-based computation.
  arXiv  Detail & Related papers  (2025-09-03T13:00:49Z)
• Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance [0.0]
  This study introduces a novel approach by replacing the traditional perceptron model with a biologically inspired probabilistic meta neuron model.<n>As a second key contribution, we present a new biologically inspired classification framework that uniquely integrates SNNs with Le-Ziv plasticity (LZC)<n>We consider learning algorithms such as backpropagation, spike-timing aspect-dependent plasticity (STDP), and the Tempotron learning rule.
  arXiv  Detail & Related papers  (2025-08-08T09:14:49Z)
• Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training [63.3991315762955]
  Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation.<n>Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics.<n>We propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics.
  arXiv  Detail & Related papers  (2025-07-22T18:20:56Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence [51.6943465041708]
  Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency. We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
  arXiv  Detail & Related papers  (2023-10-25T13:15:17Z)
• 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)
• Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles [13.521272923545409]
  We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity) We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity.
  arXiv  Detail & Related papers  (2023-02-22T19:48:02Z)
• 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)

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.