Data & Analytics

Differential equation analysis of SNN dynamics. Translates discrete spiking models into continuous ODE/PDE formulations for stability analysis, bifurcation study, and dynamical systems characterization. Activation: SNN differential equations, spiking dynamics analysis, ODE neuron model, bifurcation SNN, continuous-time spiking, dynamical systems neuroscience

SpikeProphecy: First large-scale benchmark for causal, autoregressive neural population spike-count forecasting. Introduces population metric decomposition (temporal fidelity, spatial pattern accuracy, magnitude-invariant alignment) on 105 Neuropixels sessions (~89,800 neurons). arXiv:2605.12992

脉冲时序训练和自发强化神经元集群。研究STDP如何形成共享刺激偏好的强耦合神经元集群，自发动力学期间的脉冲相关性主动强化连接。适用于计算神经科学、STDP学习、神经编码研究。触发词：神经元集群、STDP、脉冲时序、神经编码、自发动力学、neuronal assembly、spike timing、STDP、noise correlation。

Spike Agreement Dependent Plasticity (SADP) - biologically inspired learning rule for SNNs using population-level correlation metrics instead of precise spike timing. Activation triggers: spike agreement, synaptic plasticity, SNN learning, bio-inspired learning, population correlation, neuromorphic learning.

Learning Alzheimer's disease biophysical signatures via EEG and Spiking Neural Networks. Combines biophysical modeling of neurodegeneration with SNN-based biomarker extraction from EEG signals. Simulates AD pathology effects on neural circuits and learns discriminative signatures. Activation: Alzheimer, EEG biomarker, spiking neural network, neurodegeneration, AD detection, biophysical simulation.

Spiking Neural Networks with Astrocyte-Like Units - incorporating glial cell dynamics for improved learning, achieving optimal performance at 2:1 astrocyte-to-neuron ratio matching biological estimates. Activation triggers: astrocyte, glial cells, tripartite synapse, SNN learning, liquid state machine, biological realism.

**arXiv ID:** 2404.07941 **Authors:** Dong Chen, Shuai Zheng, Muhao Xu, Zhenfeng Zhu, Yao Zhao **Published:** 2024-03-11T05:19:43Z **Abstract:** In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial. Spiking Neural Networks (SNNs), known as their temporal dynamics and low-power characteristic, offer an efficient solution for temporal processing in DGRL task. However, owing to the spike...

**arXiv ID:** 2209.09626 **Authors:** Malyaban Bal, Abhronil Sengupta **Published:** 2022-09-14T20:01:22Z **Abstract:** Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative to conventional learning frameworks such as backpropagation. The effectiveness of EP stems from the fact that it relies only on local computations and requires solely one kind of computational unit during both of its training phases, thereby enabling greater applicability in domains such as bio-ins...

ANN-SNN conversion with membrane potential alignment.

**arXiv ID:** 1707.08167 **Authors:** El Mahdi El Mhamdi, Rachid Guerraoui, Sebastien Rouault **Published:** 2017-07-25T19:22:55Z **Abstract:** With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural networ...

**arXiv ID:** 2407.00641 **Authors:** Rachmad Vidya Wicaksana Putra, Muhammad Shafique **Published:** 2024-06-30T09:51:58Z **Abstract:** Intelligent mobile agents (e.g., UGVs and UAVs) typically demand low power/energy consumption when solving their machine learning (ML)-based tasks, since they are usually powered by portable batteries with limited capacity. A potential solution is employing neuromorphic computing with Spiking Neural Networks (SNNs), which leverages event-based computation to...

Multi-Timescale Conductance (MTC) Spiking Networks methodology — sparse, gradient-trainable framework with rich firing dynamics. Derives differentiable conductance-based neurons with fast/slow/ultra-slow timescales, enabling direct BPTT without surrogate gradients. Benchmark: Mackey-Glass chaotic time series forecasting. Activation: MTC-SNN, conductance spiking, BPTT spiking, multi-timescale neuron, Mackey-Glass forecasting, I-V curve shaping, 多时间尺度电导脉冲网络.

Self-organizing memristive networks generating neuronal population spiking dynamics with intrinsic neuro-synaptic resonance

GTaS Generative Spike Train Model

Inter-areal predictive coding for gradient-free continual learning in spiking neural networks. Brain-inspired learning rule using feedback connections to transmit prediction errors without backpropagation. Keywords: gradient-free learning, continual learning, predictive coding, inter-areal, SNN, catastrophic forgetting, bio-inspired.

Analysis of internal noise in spiking neural networks: how intrinsic noise sources affect SNN dynamics, reliability, and computation. Covers stochastic spiking, channel noise, and noise-driven dynamics. Activation: spiking neural networks, internal noise, stochastic spiking, SNN reliability, channel noise, neural noise, snn dynamics

Internal noise analysis in Spiking Neural Networks. Covers noise sources (channel, synaptic, threshold), propagation mechanisms, and effects on SNN dynamics. Distinguishes additive vs multiplicative noise regimes and their impacts on computation. Activation: SNN, internal noise, spiking neural networks, noise analysis, neuromorphic

Functional ensembles as units of computation in deep spiking networks. First-order functionally-connected (1FC) groups based on pairwise correlations, aggregate cofiring predicts downstream responses, ReLU-like input-output relationship with ensemble-size scaling, rare high-coordination events encode information. Activation: functional ensemble, SNN computation, functional connectivity, 1FC group, ensemble cofiring, deep spiking network analysis.

Dynamic Gated Neuron (DGN) - Biologically plausible gating mechanism for Spiking Neural Networks via dynamic membrane conductance modulation. Enables selective input filtering and adaptive noise suppression. Activation triggers: dynamic gated neuron, DGN, SNN gating, conductance-based SNN, robust spiking neural network, biological gating.

深度连续局部学习方法论（DECOLLE）。在脉冲神经网络中实现局部突触可塑性规则，通过合成梯度实现端到端训练。适用于事件驱动视觉、神经形态计算、在线学习、脉冲神经网络研究。触发词：DECOLLE、脉冲神经网络、突触可塑性、局部学习、神经形态计算、在线学习、spiking neural network、synaptic plasticity、neuromorphic computing。

Congestion-Aware Dynamic Axonal Delay mechanism for Spiking Neural Networks. Decomposes delay into channel-wise static base delay + global activity-conditioned shift. Reduces delay parameters by ~50% while improving accuracy on temporal tasks. Source: arXiv:2605.01291 (Bai et al., May 2026).

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks. Replaces static per-synapse delays with input-dependent dynamic delays that adapt to network activity patterns, reducing delay parameters while improving temporal task performance. Activation: congestion-aware delay, dynamic axonal delay SNN, input-dependent delay, SNN temporal processing, adaptive delay learning.

Scalable neuromorphic computing via autonomous spiking dynamics in clockless (asynchronous) digital circuits implemented on FPGAs. Boolean spiking neurons with configurable excitatory/inhibitory weights, spike-encoded data processing pipeline. Bridges gap to analog neuromorphic systems without specialized hardware. Based on Oliveira Gomes & Rontani (arXiv: 2605.16114). Use when designing energy-efficient neuromorphic systems on FPGAs, exploring clockless asynchronous digital circuits for neur...

Circuit-level spiking neuron model for hardware robustness analysis. Studies how transistor-level variations affect SNN reliability on neuromorphic chips. Activation: circuit-level SNN, neuromorphic hardware reliability, transistor variation spiking, hardware spiking neuron, CMOS spiking, SNN fault tolerance