LIF Neurons

LIF: Leaky Integrate-and-Fire

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12 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT
Oct 12, 2026Kaixiang He, Yujie WuLIF Neurons Spiking Neural Networks
Department of Computing, The Hong Kong Polytechnic University
Parallel Time-Aligned Spiking Self-Attention for Consistent Integer-Valued Training and Spike-Driven Inference
Oct 2, 2026Peng Xue, Wei Fang, Kaiwei Che +4Spiking Neural Networks Spiking Transformers
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China · Pengcheng Laboratory, China · University of Chinese Academy of Sciences, China +2
Petri Net Description of Biological Neural Circuits for Fast Hardware Prototyping
Aug 20, 2026Carlo daCunha, Rodrigo Pena, Marcos TurquetiSpiking Neural Networks Neuromorphic Computing
Helen and John C. Hartmann Department of Electrical Engineering, New Jersey Institute of Technology, 323 Martin Luther King Jr. Blvd., Newark, 07102, NJ, USA. · School of Applied Engineering and Technology, New Jersey Institute of Technology, 323 Martin Luther King Jr. Blvd., Newark, 07102, NJ, USA. · Department of Biological Sciences, Florida Atlantic University, 5353 Parkside Drive, Jupiter, 33458, FL, USA. +1
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
Aug 9, 2026Prasanna Date, Kevin Zhu, Shruti Kulkarni +12LIF Neurons Spiking Neural Networks
Oak Ridge National Laboratory, Oak Ridge, 37830, Tennessee, United States. · George Mason University, Fairfax, 22030, Virginia, United States. · University of Tennessee, Knoxville, Knoxville, 37996, Tennessee, United States. +1
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
Jul 16, 2026Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi +1Spiking Neural Networks LIF Neurons
Research Center for Mathematical Engineering, Chiba Institute of Technology, Narashino, Japan · International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan · NEC Corporation, Kawasaki, Japan
An Open-Source LFSR-Based Stochastic Leaky Integrate-and-Fire Neuron in SkyWater 130 nm: Design, Stochastic Characterisation, and Rate Coding
Jun 22, 2026Poornima Kumaresan, Santhosh SivasubramaniLIF Neurons Spiking Neural Networks
Intrinsic Lab, Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (SeNSE), Indian Institute of Technology Delhi, New Delhi 110016, India
Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
Jun 2, 2026Carlo Wenig, Raoul-Martin Memmesheimer, Christian KlosLIF Neurons Spiking Neural Networks
University of Bonn · University of Tübingen · University of Illinois Urbana-Champaign
Signed Spiking Neuron Enabled by an Orthogonal-Easy-Axis Magnetic Tunnel Junction
Jun 2, 2026Huannan Zheng, Jingli Liu, Kezhou YangLIF Neurons Spiking Neural Networks
Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 51000, China
Leveraging Non-Equilibrium ECRAM Dynamics for Short-Term Plasticity in Neuromorphic Circuits
May 11, 2026Alex Currie, Sean Borkholder, Nithil Harris Manimaran +4LIF Neurons Spiking Neural Networks
RAMLab, Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester NY, USA · Iontronic and Nanoelectronics Lab, School of Physics and Astronomy, Rochester Institute of Technology, Rochester NY, USA · Brain Lab, Department of Computer Engineering, Rochester Institute of Technology, Rochester NY, USA
General aspects of internal noise in spiking neural networks
Apr 15, 2026I. D. Kolesnikov, D. A. Maksimov, V. M. Moskvitin +1LIF Neurons Spiking Neural Networks
Saratov State University, Astrakhanskaya str. 83, Saratov 410012, Russia
A 0.5-V Linear Neuromorphic Voltage-to-Spike Encoder Using a Bulk-Driven Transconductor
Apr 10, 2026Meysam Akbari, Erika Covi, Kea-Tiong TangLIF Neurons Spike-Based Signal Encoding
ZIAM & CogniGron, University of Groningen, 9747 AG Groningen, The Netherlands · Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
SRAM-Based Compute-in-Memory Accelerator for Linear-decay Spiking Neural Networks
Mar 13, 2026Hongyang Shang, Shuai Dong, Yahan Yang +3LIF Neurons Spiking Neural Networks
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

Oct 12, 2026J/K move · Enter open · S save

FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT

Kaixiang He, Yujie Wu

Department of Computing, The Hong Kong Polytechnic University

LIF Neurons Spiking Neural Networks Backpropagation

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Although spiking neural networks (SNNs) show promise in energy-efficient computing through event-driven computation and stateful temporal processing, their recurrent membrane dynamics render their training inherently sequential and inefficient for long sequences. Existing methods achieve full-sequence parallelism in SNNs through two main strategies: redesigning or approximating spiking dynamics to avoid reset-coupled temporal dependencies, or relying on iterative relaxed fixed-point solvers that suffer from excessive memory costs and iteration-dependent backward overhead. To address these limitations, we present FidLIF, a novel framework that reconciles the incremental update and hard-reset behavior of standard spiking LIF neurons with parallel forward propagation and BPTT-equivalent backward propagation. FidLIF iteratively recovers the full-sequence membrane-potential trajectory in parallel. By exploiting the lower bidiagonal structure induced by the local recurrence of LIF neurons, FidLIF formulates each forward trajectory correction and backward adjoint propagation as associative scans with an

O(\log T)

temporal depth. This formulation also supports input-dependent neuronal parameters and allows the same neuron to perform full-sequence parallel training and incremental step-by-step inference at deployment. In our numerical convergence evaluation, FidLIF achieves a mean absolute error below

10^{-7}

relative to the matched serial relaxed LIF dynamics, while yielding gradients numerically equivalent to exact BPTT. Our Triton implementation delivers favorable long-sequence latency and memory scaling, while achieving competitive performance on four sequential classification benchmarks. By reconciling parallel training with step-by-step execution, FidLIF enables high-fidelity spiking dynamics to benefit from parallel hardware without compromising incremental inference at deployment.