Aug 2026· International Conference on Computer Vision and Information Technology· Vol 14321, pp. 1432108 - 1432108-10· 0 citations· 31 references
Engineering
TL;DR
Simulation results show that incorporating astrocytic modulation consistently enhances classification performance in leaky integrate-and-fire (LIF) networks, including under noisy conditions, and suggest that augmenting simplified astrocytic dynamics can improve robustness and computational capability in SNNs, while also increasing their biological plausibility.
Abstract
Spiking neural networks (SNNs) have attracted growing interest due to their event-driven computation and close correspondence with biological neuronal signaling. Despite this, most SNN models focus exclusively on neuronal dynamics, overlooking the role of astrocytes—a major class of glial cells now recognized as active modulators of synaptic transmission, network excitability, and learning. In this work, an astrocyte-augmented SNN architecture is proposed by introducing a hidden layer of astrocytes that interact with excitatory neurons through gliotransmitter-mediated currents. The astrocytic dynamics are modeled using a simplified calcium-based framework derived from previous works, enabling efficient integration into spiking networks. Simulation results on an image classification task show that incorporating astrocytic modulation consistently enhances classification performance in leaky integrate-and-fire (LIF) networks, including under noisy conditions. These results suggest that augmenting simplified astrocytic dynamics can improve robustness and computational capability in SNNs, while also increasing their biological plausibility.
This work proposes the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model, and introduces Spik-NeRF (Spiking Neural Radi-ance Fields with Ternary Spike), which achieves rendering performance comparable to ANN-based NeRF models.
Gang Wan, Qinlong Lan, Zihan Li et al.· Neural Information Processin...· 1 citation
This work proposes a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms, and develops the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics.
Yajie Zhai, Yanmei Kang, Meng Li et al.· 0 citations
A Spiking Neural Network (SNN) is a kind of brain-inspired and event-driven network, which is becoming a promising energy-efficient alternative to Artificial Neural Networks (ANNs). In recent years, SNN methods have been successfully applied in the fields of electromagnetic signal processing and image signal processing, particularly in application scenarios that require low energy consumption. However, the performance of SNNs by direct training is far from satisfactory. In this paper, we study a novel learning method named SAD-SNN (Spatial-Activation Distillation for Spiking Neural Networks), which utilizes the ANN model to guide the SNN model learning. Unlike prior works that rely on element-wise feature alignment approaches, SAD-SNN aligns spatial-activation maps at different resolutions of the teacher and student networks. Specifically, we introduce a direct alignment approach, which defines a spatial-activation loss and normalizes the representation vectors of ANN and SNN, to alleviate the unexpected precision loss. This enables the knowledge of teacher ANNs to be effectively transferred to train student SNNs. On three image classification datasets, our proposed SAD-SNN outperforms other SNN training methods no matter whether homogeneous or heterogeneous teacher ANNs are used. Furthermore, we apply SAD-SNN to the electromagnetic signal detection task, demonstrating strong generalization ability and superior performance. In conclusion, the experimental results on various tasks and SNN architectures demonstrate that our method is a general and effective solution that significantly improves the learning of student SNNs with only two time steps.
Chongxiao Qu, Qian Zhang, Chenxiao Dou et al.· Italian National Conference...· 0 citations
This work proposes Burst Spiking Neural Networks (BuSNNs), built upon Burst-enhanced Spiking Neurons and a Dynamic Weight Constraint (DWC) mechanism, which mitigates perturbation-induced transitions in activation states and thereby enhances robustness.
Jiahong Zhang, Sijun Shen, Man Yao et al.· 0 citations
Spiking Neural Network (SNN) is a promising field for modelling neuronal activity in the brain, with applications in healthcare, agriculture, finance, manufacturing and others. However, traditional SNNs often suffer from static synaptic weights, limiting their adaptability to the changes in data. Hence, to enhance performance and more accurately model the biological process of the brain, a novel SNN model, Dynamic Gaussian-Weighted Evolving Spiking Neural Network (DyGENN) is developed and applied for the detection of Parkinson’s disease and epileptic seizure. DyGENN incorporates a time-varying synaptic weight function modeled by a Gaussian distribution. The output layer evolves dynamically by either adding new output neurons, updating parameters, or skipping sample learning based on the least error margin calculated from postsynaptic firing time and the desired class labels. The suggested model is evaluated on two different imbalanced datasets: the UCI Oxford Parkinson’s Disease Detection and the Bonn Epileptic Seizure dataset using percentage splitting and stratified K-fold cross validation. Compared to popular machine learning (Support Vector Machine (SVM) and Random Forest (RF)), deep learning (Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)) and state-of-the-art SNN frameworks (Norse, SpikingJelly, and a custom PyTorch-based model), DyGENN achieves superior performance: 97.44% accuracy, 87.5% sensitivity, 100% specificity, and geometric mean (G-Mean) of 0.9354 for Parkinson’s disease detection; 97.98% accuracy, 95.83% sensitivity, 98.67% specificity, and G-Mean of 0.972 for epileptic seizure detection. The proposed model outperforms existing models in both scenarios, suggesting its potential for broader application to other neurological conditions characterized by structured biomarker data.
Priya Das, S. Nanda, P. K. Sahoo et al.· Discover Computing· 0 citations
Understanding neuronal topology—how neurons are connected—is essential for uncovering neural computation principles and functional organization. However, accurately reconstructing such connectivity remains challenging due to the indirect nature of neural recordings and the complexity of network dynamics. As a first step towards this problem, a growing body of work has explored inferring monosynaptic connectivity directly from spike data. Among these, convolutional neural networks have shown promise when applied to spike-train cross-correlograms. Nevertheless, their ability to generalize across realistic experimental variability and the internal features that drive their predictions remain poorly understood. In this paper, we present a systematic benchmarking and diagnostic study of neural-network-based synaptic inference using simulations across a broad range of biophysical regimes. We show that connectivity classification and synaptic weight estimation, though often combined, rely on distinct internal representations and exhibit markedly different generalization behavior: robust connectivity models emphasize global structure in spike-train correlations, whereas weight estimation models are more sensitive to local signal amplitude and generalize less predictably. Importantly, we find that training on pooled, biologically grounded simulation data substantially improves robustness across parameter perturbations, outperforming models trained under narrow conditions. We further validate these findings in both simulated network data and an in vitro dataset from high‑density microelectrode array recordings with patch‑clamp‑verified ground‑truth connections. Models trained on diverse simulated circuits generalize effectively to novel network architectures and the experimental dataset. Together, these results demonstrate that incorporating biologically realistic diversity during training is critical for developing reliable machine-learning tools for large-scale synaptic inference from neural recordings.