SpiLiFormer: Enhancing Spiking Transformers with Lateral Inhibition

Spiking Neural Networks (SNNs) based on Transformers have garnered significant attention due to their superior performance and high energy efficiency. However, the spiking attention modules of most existing Transformer-based SNNs are adapted from those of analog Transformers, failing to fully address the issue of over-allocating attention to irrelevant contexts. To fix this fundamental yet overlooked issue, we propose a Lateral Inhibition-inspired Spiking Transformer (SpiLiFormer). It emulates the brain's lateral inhibition mechanism, guiding the model to enhance attention to relevant tokens while suppressing attention to irrelevant ones. Our model achieves state-of-the-art (SOTA) performance across multiple datasets, including CIFAR-10 (+0.45%), CIFAR-100 (+0.48%), CIFAR10-DVS (+2.70%), N-Caltech101 (+1.94%), and ImageNet-1K (+1.6%). Notably, on the ImageNet-1K dataset, SpiLiFormer (69.9M parameters, 4 time steps, 384 resolution) outperforms E-SpikeFormer (173.0M parameters, 8 time steps, 384 resolution), a SOTA spiking Transformer, by 0.46% using only 39% of the parameters and half the time steps. Our code and training checkpoints will be released upon acceptance.

A Recurrent Spiking Network with Hierarchical Intrinsic Excitability Modulation for Schema Learning

Schema, a form of structured knowledge that promotes transfer learning, is attracting growing attention in both neuroscience and artificial intelligence (AI). Current schema research in neural computation is largely constrained to a single behavioral paradigm and relies heavily on recurrent neural networks (RNNs) which lack the neural plausibility and biological interpretability. To address these limitations, this work first constructs a generalized behavioral paradigm framework for schema learning and introduces three novel cognitive tasks, thus supporting a comprehensive schema exploration. Second, we propose a new model using recurrent spiking neural networks with hierarchical intrinsic excitability modulation (HM-RSNNs). The top level of the model selects excitability properties for task-specific demands, while the bottom level fine-tunes these properties for intra-task problems. Finally, extensive visualization analyses of HM-RSNNs are conducted to showcase their computational advantages, track the intrinsic excitability evolution during schema learning, and examine neural coordination differences across tasks. Biologically inspired lesion studies further uncover task-specific distributions of intrinsic excitability within schemas. Experimental results show that HM-RSNNs significantly outperform RSNN baselines across all tasks and exceed RNNs in three novel cognitive tasks. Additionally, HM-RSNNs offer deeper insights into neural dynamics underlying schema learning.

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks

Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume that all machine learning models are of the same type, although it becomes more likely that different edge devices adopt different types of AI models, including both conventional analogue artificial neural networks (ANNs) and biologically more plausible spiking neural networks (SNNs). This diversity empowers the efficient handling of specific tasks and requirements, showcasing the adaptability and versatility of edge computing platforms. One main challenge of such heterogeneous FL system lies in effectively aggregating models from the local devices in a privacy-preserving manner. To address the above issue, this work benchmarks FL systems containing both convoluntional neural networks (CNNs) and SNNs by comparing various aggregation approaches, including federated CNNs, federated SNNs, federated CNNs for SNNs, federated SNNs for CNNs, and federated CNNs with SNN fusion. Experimental results demonstrate that the CNN-SNN fusion framework exhibits the best performance among the above settings on the MNIST dataset. Additionally, intriguing phenomena of competitive suppression are noted during the convergence process of multi-model FL.