Spiking Neural Networks

Also known as SNN

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Overview

Spiking neural networks study computation with neurons that integrate inputs over time and communicate through spikes, making them a natural fit for event-based data such as vision, audio, and sensor streams. Common approaches include training with surrogate gradients and backpropagation through time, local plasticity rules such as spike-timing-dependent plasticity, and converting conventional neural networks into spiking ones; researchers also design new neuron dynamics, temporal codes, and spiking attention or state-space models. Applications range from edge sensing and robotics to language, medical imaging, and scientific computing, with implementations on neuromorphic chips, FPGAs, and conventional hardware. A central tension is whether sparse activity yields real energy and latency savings without sacrificing accuracy: results depend on the task, encoding, time steps, and hardware, so deployment-aware benchmarks remain important.

Latest papers

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358 papers49 papers in the last four weeks, up 40% on the four weeks before. 0.3% of all new papers.

Mar 9, 2026

Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAX

Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support arbitrary neuron models but introduce gradient bias and constrain spike-time resolution, while continuous-time methods that compute exact gradients require analytical expressions for spike times and state evolution, restricting them to simple neuron types such as Leaky Integrate and Fire (LIF). We introduce the Eventax framework, which resolves this trade-off by combining differentiable numerical ODE solvers with event-based spike handling. Built in JAX, our frame-work uses Diffrax ODE-solvers to compute gradients that are exact with respect to the forward simulation for any neuron model defined by ODEs . It also provides a simple API where users can specify just the neuron dynamics, spike conditions, and reset rules. Eventax prioritises modelling flexibility, supporting a wide range of neuron models, loss functions, and network architectures, which can be easily extended. We demonstrate Eventax on multiple benchmarks, including Yin-Yang and MNIST, using diverse neuron models such as Leaky Integrate-and-fire (LIF), Quadratic Integrate-and-fire (QIF), Exponential integrate-and-fire (EIF), Izhikevich and Event-based Gated Recurrent Unit (EGRU) with both time-to-first-spike and state-based loss functions, demonstrating its utility for prototyping and testing event-based architectures trained with exact gradients. We also demonstrate the application of this framework for more complex neuron types by implementing a multi-compartment neuron that uses a model of dendritic spikes in human layer 2/3 cortical Pyramidal neurons for computation. Code available at https://github.com/efficient-scalable-machine-learning/eventax.

Lukas König, Manuel Kuhn, David Kappel +1

CITEC, Bielefeld University, Bielefeld, Germany · Department of Computer Science, Royal Holloway, University of London, Egham, United Kingdom

Spiking Neural Networks
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Mar 3, 2026

mlx-snn: Spiking Neural Networks on Apple Silicon via MLX

We introduce mlx-snn, the first spiking neural network (SNN) library built natively on Apple's MLX framework. As SNN research grows rapidly, all major libraries -- snnTorch, Norse, SpikingJelly, Lava -- target PyTorch or custom backends, leaving Apple Silicon users without a native option. mlx-snn provides six neuron models (LIF, IF, Izhikevich, Adaptive LIF, Synaptic, Alpha), four surrogate gradient functions, four spike encoding methods (including an EEG-specific encoder), and a complete backpropagation-through-time training pipeline. The library leverages MLX's unified memory architecture, lazy evaluation, and composable function transforms (mx.grad, mx.compile) to enable efficient SNN research on Apple Silicon hardware. We validate mlx-snn on MNIST digit classification across five hyperparameter configurations and three backends, achieving up to 97.28% accuracy with 2.0--2.5 times faster training and 3--10 times lower GPU memory than snnTorch on the same M3 Max hardware. mlx-snn is open-source under the MIT license and available on PyPI. https://github.com/D-ST-Sword/mlx-snn

Jiahao Qin

Spiking Neural Networks
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Feb 28, 2026

Reward-Modulated Local Learning in Spiking Encoders: Controlled Benchmarks with STDP and Hybrid Rate Readouts

This paper presents a controlled empirical study of biologically motivated local learning for handwritten digit recognition. We evaluate an STDP-inspired competitive proxy and a practical hybrid benchmark built on the same spiking population encoder. The proxy is motivated by leaky integrate-and-fire E/I circuit models with three-factor delayed reward modulation. The hybrid update is local in pre x post rates but uses supervised labels and no timing-based credit assignment. On sklearn digits, fixed-seed evaluation shows classical pixel baselines from 98.06 to 98.22% accuracy, while local spike-based models reach 86.39 +/- 4.75% (hybrid default) and 87.17 +/- 3.74% (STDP-style competitive proxy). Ablations identify normalization and reward-shaping settings as the strongest observed levers, with a best hybrid ablation of 95.52 +/- 1.11%. A network-free synthetic temporal benchmark supports the same timing-versus-rate interpretation under matched local-update training. A descriptive 2x2 analysis further shows reward-shaping effects can reverse sign across stabilization regimes, so reward-shaping conclusions should be reported jointly with normalization settings.

Debjyoti Chakraborty

Researcher

Handwritten Digit RecognitionSpiking Neural NetworksLocal LearningSpike-Timing-Dependent PlasticitySpike-Based Signal EncodingOptical Character Recognition
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Feb 28, 2026

TP-Spikformer: Token Pruned Spiking Transformer

Spiking neural networks (SNNs) offer an energy-efficient alternative to traditional neural networks due to their event-driven computing paradigm. However, recent advancements in spiking transformers have focused on improving accuracy with large-scale architectures, which require significant computational resources and limit deployment on resource-constrained devices. In this paper, we propose a simple yet effective token pruning method for spiking transformers, termed TP-Spikformer, that reduces storage and computational overhead while maintaining competitive performance. Specifically, we first introduce a heuristic spatiotemporal information-retaining criterion that comprehensively evaluates tokens' importance, assigning higher scores to informative tokens for retention and lower scores to uninformative ones for pruning. Based on this criterion, we propose an information-retaining token pruning framework that employs a block-level early stopping strategy for uninformative tokens, instead of removing them outright. This also helps preserve more information during token pruning. We demonstrate the effectiveness, efficiency and scalability of TP-Spikformer through extensive experiments across diverse architectures, including Spikformer, QKFormer and Spike-driven Transformer V1 and V3, and a range of tasks such as image classification, object detection, semantic segmentation and event-based object tracking. Particularly, TP-Spikformer performs well in a training-free manner. These results reveal its potential as an efficient and practical solution for deploying SNNs in real-world applications with limited computational resources.

Wenjie Wei, Xiaolong Zhou, Malu Zhang +8

University of Electronic Science and Technology of China · Shenzhen Loop Area Institute · Northumbria University +1

Spiking TransformersToken PruningSpiking Neural NetworksEfficient Transformer InferenceVisual Token Pruning
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Feb 27, 2026

Bullet Trains: Parallelizing Training of Temporally Precise Spiking Neural Networks

Continuous-time, event-native spiking neural networks (SNNs) operate strictly on spike events, treating spike timing and ordering as the representation rather than an artifact of time discretization. This viewpoint aligns with biological computation and with the native resolution of event sensors and neuromorphic processors, while enabling compute and memory that scale with the number of events. However, two challenges hinder practical, end-to-end trainable event-based SNN systems: 1) exact charge--fire--reset dynamics impose inherently sequential processing of input spikes, and 2) precise spike times must be solved without time bins. We address both. First, we use parallel associative scans to consume multiple input spikes at once, yielding up to 44x speedups over sequential simulation while retaining exact hard-reset dynamics. Second, we implement differentiable spike time solvers that compute spike times to machine-precision without discrete-time approximations or restrictive analytic assumptions. We demonstrate the viability of training SNNs using our solutions on four event-based datasets on GPUs.

Todd Morrill, Christian Pehle, Anthony Zador

Department of Computer Science, Columbia University, New York, NY, USA · Department of Neuroscience, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

Spiking Neural NetworksNeuromorphic Computing
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Feb 27, 2026

SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking

Spiking Neural Networks (SNNs) promise energy-efficient vision, but applying them to RGB visual tracking remains difficult: Existing SNN tracking frameworks either do not fully align with spike-driven computation or do not fully leverage neurons' spatiotemporal dynamics, leading to a trade-off between efficiency and accuracy. To address this, we introduce SpikeTrack, a spike-driven framework for energy-efficient RGB object tracking. SpikeTrack employs a novel asymmetric design that uses asymmetric timestep expansion and unidirectional information flow, harnessing spatiotemporal dynamics while cutting computation. To ensure effective unidirectional information transfer between branches, we design a memory-retrieval module inspired by neural inference mechanisms. This module recurrently queries a compact memory initialized by the template to retrieve target cues and sharpen target perception over time. Extensive experiments demonstrate that SpikeTrack achieves the state-of-the-art among SNN-based trackers and remains competitive with advanced ANN trackers. Notably, it surpasses TransT on LaSOT dataset while consuming only 1/26 of its energy. To our knowledge, SpikeTrack is the first spike-driven framework to make RGB tracking both accurate and energy efficient. The code and models are available at https://github.com/faicaiwawa/SpikeTrack.

Qiuyang Zhang, Jiujun Cheng, Qichao Mao +5

Tongji University · Nova University of Lisbon · Stockholm University +2

Visual Object TrackingSpiking Neural Networks
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Feb 25, 2026

Orthogonal Weight Modification Enhances Learning Scalability and Convergence Efficiency without Gradient Backpropagation

Recognizing the substantial computational cost of backpropagation (BP), non-BP methods have emerged as attractive alternatives for efficient learning on emerging neuromorphic systems. However, existing non-BP approaches still face critical challenges in efficiency and scalability. Inspired by neural representations and dynamic mechanisms in the brain, we propose a perturbation-based approach called LOw-rank Cluster Orthogonal (LOCO) weight modification. We find that low-rank is an inherent property of perturbation-based algorithms. Under this condition, the orthogonality constraint limits the variance of the node perturbation (NP) gradient estimates and enhances the convergence efficiency. Through extensive evaluations on multiple datasets, LOCO demonstrates the capability to locally train the deepest spiking neural networks to date (more than 10 layers), while exhibiting strong continual learning ability, improved convergence efficiency, and better task performance compared to other brain-inspired non-BP algorithms. Notably, LOCO requires only O(1) parallel time complexity for weight updates, which is significantly lower than that of BP methods. This offers a promising direction for achieving high-performance, real-time, and lifelong learning on neuromorphic systems.

Guoqing Ma, Shan Yu

Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Future Technology, University of Chinese Academy of Sciences · Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Chinese Academy of Sciences

Neuromorphic ComputingSpiking Neural Networks
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Feb 24, 2026

Robust Spiking Neural Networks Against Adversarial Attacks

Spiking Neural Networks (SNNs) represent a promising paradigm for energy-efficient neuromorphic computing due to their bio-plausible and spike-driven characteristics. However, the robustness of SNNs in complex adversarial environments remains significantly constrained. In this study, we theoretically demonstrate that those threshold-neighboring spiking neurons are the key factors limiting the robustness of directly trained SNNs. 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. To address this challenge, we propose a Threshold Guarding Optimization (TGO) method, which comprises two key aspects. First, we incorporate additional constraints into the loss function to move neurons' membrane potentials away from their thresholds. It increases SNNs' gradient sparsity, thereby reducing the theoretical upper bound of adversarial attacks. Second, we introduce noisy spiking neurons to transition the neuronal firing mechanism from deterministic to probabilistic, decreasing their state-flipping probability due to minor disturbances. Extensive experiments conducted in standard adversarial scenarios prove that our method significantly enhances the robustness of directly trained SNNs. These findings pave the way for advancing more reliable and secure neuromorphic computing in real-world applications.

Shuai Wang, Malu Zhang, Yulin Jiang +7

University of Electronic Science and Technology of China · Shenzhen Loop Area Institute · Northumbria University +1

Adversarial RobustnessSpiking Neural NetworksNeuromorphic Computing
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Feb 22, 2026

Spiking Graph Predictive Coding for Reliable OOD Generalization

Graphs provide a powerful basis for modeling Web-based relational data, with expressive GNNs to support the effective learning in dynamic web environments. However, real-world deployment is hindered by pervasive out-of-distribution (OOD) shifts, where evolving user activity and changing content semantics alter feature distributions and labeling criteria. These shifts often lead to unstable or overconfident predictions, undermining the trustworthiness required for Web4Good applications. Achieving reliable OOD generalization demands principled and interpretable uncertainty estimation; however, existing methods are largely post-hoc, insensitive to distribution shifts, and unable to explain where uncertainty arises especially in high-stakes settings. To address these limitations, we introduce SpIking GrapH predicTive coding (SIGHT), an uncertainty-aware plug-in graph learning module for reliable OOD Generalization. SIGHT performs iterative, error-driven correction over spiking graph states, enabling models to expose internal mismatch signals that reveal where predictions become unreliable. Across multiple graph benchmarks and diverse OOD scenarios, SIGHT consistently enhances predictive accuracy, uncertainty estimation, and interpretability when integrated with GNNs.

Jing Ren, Jiapeng Du, Bowen Li +6

RMIT University, Melbourne, Australia · University of Auckland, Auckland, New Zealand · CSIRO’s Data61, Melbourne, Australia +1

OOD GeneralizationGraph Neural NetworksSpiking Neural Networks
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Feb 21, 2026

Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision

Spiking Neural Networks (SNNs) provide biological plausibility and energy efficiency, yet systematic investigations of memory augmentation strategies remain limited. We conduct a five-model ablation study integrating Leaky Integrate-and-Fire neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN) on the N-MNIST dataset. Baseline SNNs exhibit organized neuronal groupings, or structured assemblies, characterized by a silhouette score of 0.687±0.0120.687 \pm 0.012. Individual augmentations introduce trade-offs: SCL improves accuracy by 0.28%0.28\% but reduces clustering (silhouette score 0.637±0.0150.637 \pm 0.015), while HGRN yields consistent gains in both accuracy (+1.01%+1.01\%) and computational efficiency (170.6×170.6\times). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of 0.715±0.0080.715 \pm 0.008, classification accuracy of 97.49±0.10%97.49 \pm 0.10\%, energy consumption of 1.85±0.06 μJ1.85 \pm 0.06\,μ\mathrm{J}, and sparsity of 97.0%97.0\%. These results indicate that optimal performance emerges from architectural balance rather than isolated optimization, establishing design principles for memory-augmented neuromorphic systems.

Effiong Blessing, Chiung-Yi Tseng, Isaac Nkrumah +1

Department Of Computer Science, Saint Louis University · Luxmuse AI · Saint Louis University +1

Spiking Neural NetworksNeuromorphic ComputingImage Classification
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Feb 20, 2026

Flexi-NeurA: A Flexible Neuromorphic Accelerator with Adaptive Bit-Precision Exploration for Edge Devices

Neuromorphic accelerators promise unparalleled energy efficiency and computational density for spiking neural networks, especially in wearable biomedical devices and neural prosthetics where power constraints are stringent. However, most existing platforms exhibit rigid architectures with limited configurability, restricting their adaptability to heterogeneous biological signals and diverse design objectives. To address these limitations, we present Flexi-NeurA--a flexible neuromorphic accelerator that unifies configurability and efficiency. Flexi-NeurA allows users to customize neuron models, network structures, and precision settings at design time. By pairing these design-time configurability features with a time-multiplexed and event-driven processing approach, Flexi-NeurA reduces the required hardware resources and total power while preserving high efficiency and low inference latency. Complementing this, we introduce Flex-plorer, a design-space exploration tool that determines cost-effective fixed-point precisions for critical parameters--such as decay factors, synaptic weights, and membrane potentials--based on user-defined trade-offs between accuracy and resource usage. Based on the configuration selected through the Flex-plorer process, RTL code is configured to match the specified design. Comprehensive evaluations across distinct domains--biomedical auditory processing, dynamic vision sensor gesture recognition, and standard vision classification--demonstrate that the hardware/software co-framework successfully balances accuracy and power budgets for diverse applications. A 3-layer 256-128-10 fully connected network with LIF neurons mapped onto two processing cores achieves 96.23% accuracy on MNIST with 1.1 ms inference latency, utilizing only 1,623 logic cells, 7 BRAMs, and 111 mW of total power--demonstrating superior resource efficiency compared to SoTA hardware baselines.

Mohammad Farahani, Mohammad Rasoul Roshanshah, Saeed Safari

High-Performance Embedded Architecture Laboratory (HiPEAL), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

Neuromorphic ComputingFPGA AccelerationSpiking Neural NetworksNeural Network Quantization
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Feb 20, 2026

HiAER-Spike Software-Hardware Reconfigurable Platform for Event-Driven Neuromorphic Computing at Scale

In this work, we present HiAER-Spike, a modular, reconfigurable, event-driven neuromorphic computing platform designed to execute large spiking neural networks with up to 160 million neurons and 40 billion synapses - roughly twice the neurons of a mouse brain at faster than real time. This system, assembled at the UC San Diego Supercomputer Center, comprises a co-designed hard- and software stack that is optimized for run-time massively parallel processing and hierarchical address-event routing (HiAER) of spikes while promoting memory-efficient network storage and execution. The architecture efficiently handles both sparse connectivity and sparse activity for robust and low-latency event-driven inference for both edge and cloud computing. A Python programming interface to HiAER-Spike, agnostic to hardware-level detail, shields the user from complexity in the configuration and execution of general spiking neural networks with minimal constraints in topology. The system is made easily available over a web portal for use by the wider community. In the following, we provide an overview of the hard- and software stack, explain the underlying design principles, demonstrate some of the system's capabilities and solicit feedback from the broader neuromorphic community. Examples are shown demonstrating HiAER-Spike's capabilities for event-driven vision on benchmark CIFAR-10, DVS event-based gesture, MNIST, and Pong tasks.

Gwenevere Frank, Gopabandhu Hota, Keli Wang +12

Institute for Neural Computation, UC San Diego, 9500 Gilman Dr, La Jolla, 92093, CA, USA. · Qernel AI, 460 California Ave ste 204, Palo Alto, 94306, CA, USA. · Fujitsu, 4-1-1 Kamikodanaka, Nakahara-ku Kawasaki-shi, 211-8588, Kanagawa, Japan. +1

Neuromorphic ComputingEfficient Neural Network InferenceSpiking Neural Networks
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Jan 21, 2026

NeuroVLA: A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control

Vision-language-action (VLA) models inherit powerful semantic representations from large language models, but carrying over their design priorities can leave the demands of physical control under-specified. Stable manipulation requires high-frequency proprioceptive feedback and persistent motor state, in addition to instruction understanding. Here we introduce Neuromorphic Vision-Language-Action (NeuroVLA), a brain-inspired architecture that couples a cortical vision-language planner to recurrent cerebellar modulation and a stateful spiking spinal controller. These components translate semantic intent into actions while incorporating high-frequency proprioceptive and force feedback, maintaining short-term motor memory, and enabling local responses to unexpected contact. In simulated and physical manipulation tasks, the cerebellar module reduces mean commanded jerk by 75.6%, and NeuroVLA achieves a 54.8% collision-recovery rate compared with 0.0% for the evaluated VLA baselines. The spinal controller implemented on an FPGA-based neuromorphic processor achieves 2.19 ms inference latency and 0.87 mJ per inference, corresponding to approximately 0.4 W during inference. These results motivate moving beyond inherited language-model design priorities toward embodied architectures that explicitly integrate semantic planning, high-frequency bodily feedback, motor memory, and local correction.

Weiyu Guo, He Zhang, Pengteng Li +7

The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · AI2 Robotics, Shenzhen, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China +1

Robotic ControlVision-Language-Action ModelsRobotic ManipulationNeuromorphic ComputingSpiking Neural Networks
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Jan 13, 2026

Building Supervision into Hebbian Plasticity through Spike Agreement

Supervised learning in spiking neural networks (SNNs) typically requires either gradient-based backpropagation, which sacrifices the Hebbian, spike-driven character of biological plasticity, or reward-modulated Spike-Timing-Dependent Plasticity (STDP), in which class supervision enters only as a scalar gate on an otherwise class-agnostic correlation signal. We propose Supervised Spike Agreement-Dependent Plasticity (Supervised SADP), a gradient-free supervised Hebbian learning algorithm in which class information is embedded directly into the Hebbian plasticity computation rather than introduced through reward modulation. SADP trains the output layer via a supervised Hebbian rule that encodes class labels into output spike patterns, then trains the hidden layer by measuring each hidden neuron's chance-corrected temporal agreement, Cohen's kappa, with the correct-class output spike train produced by the forward pass without gradient computation or external reward. A K-shift extension aggregates agreement over temporal offsets, providing robustness to spike-timing jitter at linear computational cost. We evaluate Supervised SADP against reward-modulated STDP across six benchmark and medical imaging datasets, four input encoding strategies, K_shift in {5,25}, and three reward modes (none, binary, margin). Supervised SADP outperforms STDP in a significant majority of comparisons. Under Poisson encoding, SADP achieves 86.46% on MNIST and 76.62% on Fashion-MNIST, outperforming the best STDP configurations by 23.66 and 23.29 percentage points, respectively. Across the encodings tested, including CNN-extracted features, SADP outperforms STDP in the large majority of cells and trains 1.47x faster on average, with up to 2.86x speedup under Poisson inputs. These results position Supervised SADP as a stable, efficient, gradient-free alternative to reward-modulated STDP for supervised SNN learning.

Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar +3

Indian Institute of Science Education and Research Thiruvananthapuram, Vithura, Thiruvananthapuram, Kerala 695551, India

Hebbian LearningSpiking Neural NetworksSpike-Timing-Dependent Plasticity
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Dec 5, 2025

ADSEQ: A delay-aware autograd-compatible framework for spike-event delivery in SNNs

Spiking neural networks (SNNs), central to computational neuroscience and neuromorphic machine learning (ML), require efficient simulation and gradient-based training. While AI accelerators offer promising speedups, gradient-based SNNs typically implement sparse spike events using dense, memory-heavy data-structures. Existing exact gradient methods lack generality, and current simulators often omit or inefficiently handle delayed spikes. We address this by deriving gradient computation through spike event queues, including delays, and implementing this into memory-efficient, autodifferentiable spike event queues (ADSEQ). These are benchmarked across CPU, GPU, TPU, and LPU platforms. We find that queue design strongly shapes performance. CPUs, as expected, perform well with traditional tree-based or FIFO implementations, while GPUs excel with ring buffers for smaller simulations, yet under higher memory pressure prefer more sparse data-structures. TPUs seem to favor an implementation based on sorting intrinsics. Selective spike dropping provides a simple performance-accuracy trade-off, which could be enhanced by future autograd frameworks adapting diverging primal/tangent data-structures.

Lennart P. L. Landsmeer, Amirreza Movahedin, Said Hamdioui +1

Department of Computer Engineering, Delft University of Technology, Netherlands · NeuroComputingLab, Department of Neuroscience, Erasmus Medical Center, Netherlands

Automatic DifferentiationSpiking Neural Networks
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Nov 7, 2025

NeuroFlex: Lossless Element-Level ANN-SNN Co-Execution for Efficient Sparse Inference

Sparse DNN accelerators specialize in ANN or SNN execution, leaving energy or latency on the table when workload characteristics vary within a layer. Hybrid accelerator designs that switch modes at layer or tile granularity suffer from low PE utilization since one core type idles whenever the other is active. NeuroFlex is the first accelerator to assign every output element independently to ANN or SNN execution mode with zero accuracy loss. We extend integer-exact ANN-SNN equivalence from layers to individual output elements, thereby enabling mode switching with no conversion error. An offline cost-guided scheduler scores each element by its marginal energy-delay trade-off and packs work across PEs, achieving 97-99% PE utilization compared to 40-45% for layer-wise hybrids. NeuroFlex reduces EDP by 57-67% over a strong ANN-only baseline and delivers up to 2.5x speedup over a dual-sparse SNN-only baseline. Our cost-guided scheduler improves throughput by 16-19% over random element assignment across vision, language, and transformer workloads.

Varun Manjunath, Pranav Ramesh, Gopalakrishnan Srinivasan

Electrical Engineering Indian Institute of Technology Madras Chennai, India · Computer Science and Engineering Indian Institute of Technology Madras Chennai, India

Efficient Neural Network InferenceSpiking Neural NetworksNeural Network Hardware Acceleration
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Nov 3, 2025

Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network

Edge AI must adapt to new classes and shifting data within a tight power budget, but online continual learning (learning from each sample before the next arrives) is where conventional processors are least efficient: each small update pays a full dispatch at full power. We present CLP-SNN, a spiking architecture that recasts the Continually Learning Prototypes (CLP) algorithm as event-driven neural dynamics: a self-normalizing local rule replaces CLP's global renormalization, prototype allocation becomes novelty-triggered neurogenesis, and correctness-modulated plasticity becomes reward-gated per-neuron metaplasticity. Deployed on Intel Loihi 2 as the first online continual learner on that chip, rehearsal-free CLP-SNN matches Replay and SLDA on OpenLORIS at 1-shot; at 25-shot, it stays within 0.8 percentage points of Replay and 6.2 points below covariance-based SLDA. Each prediction-and-update step on extracted features completes on-chip in 0.33 ms at 0.05 mJ, 7.3x lower latency and 341x lower energy than Replay on a Jetson Orin Nano.

Elvin Hajizada, Danielle Rager, Timothy Shea +5

Institute of Informatics, University of Munich (LMU), Munich, Germany · Intel Labs, Intel Corporation, Santa Clara, CA, USA · Southwest Research Institute, San Antonio, TX, USA +3

Online Continual LearningIncremental LearningOn-Device TrainingSpiking Neural NetworksNeuromorphic Computing
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Oct 31, 2025

Exploiting heterogeneous delays for efficient computation in low-bit neural networks

Neural networks rely on learning synaptic weights. However, this overlooks other learnable neural parameters. One such parameter is delay: neurons communicate asynchronously with heterogeneous transmission delays. We propose that, rather than a cost to minimize, this heterogeneity supports computation when task-relevant information unfolds over time. We test this hypothesis by jointly learning weights and delays at different precisions in feedforward spiking neural networks. Delay heterogeneity supports competitive performance on neuromorphic benchmarks while compressing weights over an order of magnitude more aggressively than typically studied weight-only networks. Performance depends on task-matched delay distributions, with temporally complex tasks selectively relying on longer delays. These benefits persist under explicit delay constraints, while neural time constants only partially substitute for long delays. Our results connect the temporal structure of data with efficient model design, highlighting temporal heterogeneity as a computational resource with implications for embodied intelligence and neuromorphic hardware.

Pengfei Sun, Jascha Achterberg, Zhe Su +2

Department of Electrical and Electronic Engineering, Imperial College London. · Centre for Neural Circuits and Behaviour, University of Oxford. · Institute of Neuroinformatics, University of Zurich and ETH Zurich. +2

Neuromorphic ComputingSpiking Neural NetworksNeural Network Compression
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Oct 13, 2025

Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks

Spiking neural networks (SNNs) promise energy-efficient artificial intelligence on neuromorphic hardware but struggle with tasks requiring both fast adaptation and long-term memory, especially in continual learning. We propose Local Timescale Gating (LT-Gate), a neuron model that combines dual time-constant dynamics with an adaptive gating mechanism. Each spiking neuron tracks information on a fast and a slow timescale in parallel, and a learned gate locally adjusts their influence. This design enables individual neurons to preserve slow contextual information while responding to fast signals, addressing the stability-plasticity dilemma. We further introduce a variance-tracking regularization that stabilizes firing activity, inspired by biological homeostasis. Empirically, LT-Gate yields significantly improved accuracy and retention in sequential learning tasks: on a challenging temporal classification benchmark it achieves about 51 percent final accuracy, compared to about 46 percent for a recent Hebbian continual-learning baseline and lower for prior SNN methods. Unlike approaches that require external replay or expensive orthogonalizations, LT-Gate operates with local updates and is fully compatible with neuromorphic hardware. In particular, it leverages features of Intel's Loihi chip (multiple synaptic traces with different decay rates) for on-chip learning. Our results demonstrate that multi-timescale gating can substantially enhance continual learning in SNNs, narrowing the gap between spiking and conventional deep networks on lifelong-learning tasks.

Devansh Deep, Ansh Tiwari, Ayush Chauhan

Spiking Neural NetworksOnline Continual LearningNeuromorphic Computing
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Sep 28, 2025

Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness

Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owing to their potential for energy-efficient computation and reduced memory requirements. Despite these advantages, improving adversarial robustness in SNNs (particularly for vision-based applications) remains an emerging and relatively underexplored research problem. Recent work has suggested that encouraging sparse gradients can act as a regularization mechanism to improve resistance against adversarial perturbations. In this study, we report an unexpected observation: under certain architectural configurations, SNNs inherently exhibit sparse gradients and can attain state-of-the-art adversarial defense performance without requiring any explicit regularization strategy. Further investigation reveals an inherent trade-off between robustness and generalization. Specifically, increased gradient sparsity enhances resistance to adversarial attacks but may reduce the model's generalization capability, whereas denser gradients tend to improve generalization while simultaneously increasing susceptibility to adversarial perturbations. These findings provide new perspectives on the role of gradient sparsity in the training dynamics of SNNs.

Nhan Trong Luu, Duong Trung Luu

College of Information and Communication Technology, Can Tho University, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · Center of Digital Transformation and Communication, Can Tho University, Vietnam

Neural Network GeneralizationNeural Network RobustnessAdversarial RobustnessSpiking Neural NetworksNeural Network Training Dynamics
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Jul 30, 2025

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields

Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (NeRF) models usually use a fixed inference time step for all scenes. This fixed temporal budget is inefficient because NeRF follows a scene-specific training paradigm, and different scenes require different temporal capacities to preserve rendering quality. This paper proposes Pretraining-based Adaptive Time-step Adjustment (PATA), a scene-wise adaptive time-step training framework for spike-based NeRF. PATA parameterizes the target inference time step as a trainable variable and optimizes it through a two-stage training process. A hybrid input mode strengthens early time-step outputs, while full-step soft supervision, smoothed rendering loss, and temporal-budget loss jointly maintain rendering fidelity and reduce temporal computation. The learned target time step is shared by all ray samples within a scene, preserving the parallel rendering structure of NeRF. Experiments on INGP-NeRF and TensoRF backbones across Synthetic-NeRF, Mip-NeRF 360, and LLFF show that PATA consistently reduces inference cost while maintaining competitive rendering quality. PATA reduces the estimated inference energy by up to 57.57% on INGP-NeRF and 68.90% on TensoRF, demonstrating its effectiveness across different neural rendering representations.

Ranxi Lin, Canming Yao, Jiayi Li +3

School of Information Science and Technology, ShanghaiTech University, Shanghai, China · Guangdong Institute of Intelligence Science and Technology, Zhuhai, China

Efficient Neural Network InferenceNeural RenderingSpiking Neural NetworksNeural Radiance Fields
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Jul 28, 2025

Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method

Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation, making them ideal for battery-powered intelligent devices. This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm inspired by spike-timing-dependent plasticity (STDP). The proposed architecture efficiently implements lateral inhibition and the refractory period, eliminating the need for external microcontrollers or ancillary control hardware. All synapses of the winning neurons are updated in parallel, enhancing training efficiency. The modular design ensures scalability with respect to input data dimensions and output class count. The SNN is evaluated in LTspice for pattern recognition (using 5x3 binary images) and classification tasks using the Iris and Breast Cancer Wisconsin (BCW) datasets. During testing, the system achieved perfect pattern recognition and high classification accuracies of 99.11% (Iris) and 97.9% (BCW). Additionally, it has demonstrated robustness, maintaining an average recognition rate of 93.4% under 20% input noise. The impact of stuck-at-conductance faults and memristor device variations was also analyzed.

Santlal Prajapati, Susmita Sur-Kolay, Soumyadeep Dutta

Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata 700108, India · Balasore Alloys Limited, Balgopalpur, Odisha 756020, India

Efficient Neural Network InferenceSpiking Neural NetworksNeuromorphic Computing
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Jun 26, 2025

Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning

Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters, highlighting its potential for event-driven applications such as neuromorphic integrated sensing and communications (N-ISAC).

Jiechen Chen, Bipin Rajendran, Osvaldo Simeone

Department of Engineering, King’s College London, London, WC2R 2LS, UK · Institute for Intelligent Networked Systems, Northeastern University London, One Portsoken Street, London, E1 8PH, UK

Quantum Machine LearningSpiking Neural NetworksLocal LearningQuantum Neural Networks
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May 9, 2025

Sigma-Delta Neural Network Conversion on Loihi 2

Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult. The original attempts at converting trained conventional analog neural networks (ANN) to SNNs used the rate of binary spikes to represent neuron activations. This required many simulation time steps per inference, which degraded efficiency. Intel's Loihi 2 is a neuromorphic platform that supports graded spikes which can be used to represent changes in neuron activation. In this work, we use Loihi 2's graded spikes to develop a method for converting ANN networks to spiking networks, which exploits temporal and spatial sparsity. We evaluated the performance of this network on Loihi 2 and compared it to NVIDIA's Jetson Xavier edge AI platform. The results show that neuromorphic approaches achieve significant improvements in efficiency and latency (energy-delay product) over existing solutions.

Matthew Brehove, Sadia Anjum Tumpa, Espoir Kyubwa +2

ChromoLogic LLC Monrovia, California, USA · Pennsylvania State University University Park, Pennsylvania, USA

Efficient Neural Network InferenceSpiking Neural NetworksNeuromorphic Computing
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Apr 18, 2025

Causal pieces: analysing and improving spiking neural networks piece by piece

We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike coding into distinct regions where the same subnetwork causes the output spikes. For networks of current-based leaky integrate-and-fire (LIF) neurons with large membrane time constants, we show that within each causal piece, output spike times are locally Lipschitz continuous with respect to inputs and network parameters. We further prove a lower bound on the approximation error that depends on the number of causal pieces. Thus, the number of causal pieces is a measure of the approximation capabilities of SNNs, which is valid despite spike-time discontinuities and applies to networks with both excitatory and inhibitory synapses. Empirically, we find that parameter initialisations yielding more causal pieces on the training set strongly correlate with SNN training success across multiple benchmarks, including Yin-Yang, Fashion-MNIST, and EuroSAT. Moreover, simulations with standard single-spike LIF neurons indicate that our findings extend beyond the theoretically analysed regime. These results establish causal pieces as a powerful and principled tool for analysing and improving the computational capabilities of SNNs.

Dominik Dold, Philipp Christian Petersen

Neural Network InitializationSpiking Neural NetworksNeural Network Approximation TheoryNeural Network Expressivity
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Dec 6, 2024

Robust and efficient enriched representation with intrinsic neuronal heterogeneity

Heterogeneity is a ubiquitous property of many biological systems with profound computational implications. Integrating this heterogeneity into artificial computing systems has recently gained attention. While optimizing neuronal and synaptic heterogeneity for a specific task can achieve state-of-the-art performance, such top-down optimization is biologically implausible, prone to catastrophic forgetting, and both data- and energy-intensive. In contrast, biological organisms possess inherent, task-unspecific, stable heterogeneity and can perform many tasks with minimal metabolic cost. Inspired by this, we study task-agnostic heterogeneity in recurrent neural networks solving hundreds of diverse temporal tasks. Heterogeneity is introduced as intrinsic disorder in neuronal membrane time constants. Our results show that intrinsic heterogeneity markedly improves overall performance and robustness in both rate- and spike-coded networks. Small heterogeneous networks, even without recurrence, match the performance of large, highly recurrent, homogeneous networks at a fraction of the cost and complexity. We suggest that, despite being task-agnostic, heterogeneity enhances representational capacity by diversifying neuronal responses, demonstrated on two machine-learning and one neuroscience benchmark. Our work highlights the utility of intrinsic task-agnostic heterogeneity for versatile, energy-efficient computing--a highly relevant property for the neuromorphic community, where device-to-device variability could be exploited to offset limited training budgets and architectural constraints.

Arash Golmohammadi, Jannik Luboeinski, Christian Tetzlaff

Recurrent Neural NetworksNeural Network RobustnessSpiking Neural Networks
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Dec 6, 2024

Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents

Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents). However, scaling RL to master multiple tasks simultaneously (i.e., so-called multi-task RL) remains a significant challenge. Such a multi-task RL capability especially is important for agents to adapt to changes in real-world operational environments. State-of-the-art works show that, training agents with neural networks and shared structures across tasks promises improved generalization in simultaneous multi-task RL. However, they still suffer from task interference and incur high energy consumption due to intensive computation. To address this, we propose MTSpark, a novel methodology that enables energy-efficient simultaneous multi-task RL using spiking neural networks (SNNs) equipped with active dendrites for bio-inspired generalist agents. Specifically, MTSpark enhances a Deep Spiking Q-Network (DSQN) with active dendrites, a dueling structure, and task-specific context signals to dynamically form specialized sub-networks for individual tasks, while exploiting sparse operations for energy-efficient network processing. Experimental results demonstrate that MTSpark achieves higher performance and efficiency compared to state-of-the-art by obtaining high scores across three Atari games (i.e., Pong: -5.4, Breakout: 0.6, and Enduro: 371.2), approaching human-level performance (i.e., Pong: -3, Breakout: 31, Enduro: 368), while incurring similar memory and about 2x lower energy than state-of-the-art. These results show that our MTSpark potentially advances the frontiers toward energy-efficient generalist agents by combining RL and SNNs.

Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique

eBRAIN Lab · New York University (NYU) Abu Dhabi

Reinforcement LearningDeep Q-LearningSpiking Neural NetworksMulti-Task RL
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May 24, 2024

Application based Evaluation of an Efficient Spike-Encoder, "Spiketrum"

Spike-based encoders represent information as sequences of spikes or pulses, which are transmitted between neurons. A prevailing consensus suggests that spike-based approaches demonstrate exceptional capabilities in capturing the temporal dynamics of neural activity and have the potential to provide energy-efficient solutions for low-power applications. The Spiketrum encoder efficiently compresses input data using spike trains or code sets (for non-spiking applications) and is adaptable to both hardware and software implementations, with lossless signal reconstruction capability. The paper proposes and assesses Spiketrum's hardware, evaluating its output under varying spike rates and its classification performance with popular spiking and non-spiking classifiers, and also assessing the quality of information compression and hardware resource utilization. The paper extensively benchmarks both Spiketrum hardware and its software counterpart against state-of-the-art, biologically-plausible encoders. The evaluations encompass benchmarking criteria, including classification accuracy, training speed, and sparsity when using encoder outputs in pattern recognition and classification with both spiking and non-spiking classifiers. Additionally, they consider encoded output entropy and hardware resource utilization and power consumption of the hardware version of the encoders. Results demonstrate Spiketrum's superiority in most benchmarking criteria, making it a promising choice for various applications. It efficiently utilizes hardware resources with low power consumption, achieving high classification accuracy. This work also emphasizes the potential of encoders in spike-based processing to improve the efficiency and performance of neural computing systems.

MHD Anas Alsakkal, Runze Wang, Jayawan Wijekoon +1

Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, UK. · State Key Lab of Brain-Machine Intelligence, College of Computer Science and Technology, Zhejiang University, Hangzhou, China.

Lossless Data CompressionSpike-Based Signal EncodingNeural EncodingEnergy-Efficient MLSpiking Neural Networks
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