Modality-Dependent Memory Mechanisms in Cross-Modal Neuromorphic Computing

2025-12-20

AI Summary

This work investigates the cross-modal generalization capability of memory-augmented spiking neural networks (SNNs) between visual and auditory modalities. To address the limited understanding of modality-specific memory effects in SNNs, we systematically evaluate three memory mechanisms—Hopfield networks, hierarchical gated recurrent networks (HGRNs), and supervised contrastive learning (SCL)—on the N-MNIST and SHD benchmarks. Our empirical analysis reveals strong modality specificity: cross-modal representation alignment is only 0.038, indicating poor transferability of learned memory structures across modalities. Motivated by this finding, we propose a parallel multimodal architecture that decouples modality-specific processing instead of enforcing unified representations, thereby enhancing modality adaptability. Experiments achieve 97.68% accuracy on visual tasks and 82.16% on auditory tasks, with energy efficiency improved by 603× over conventional artificial neural networks (ANNs). The core contribution lies in empirically establishing the modality-dependent nature of SNN memory mechanisms and validating the efficacy of a disentangled multimodal design.

Technology Category

• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchical Gated Recurrent Networks (HGRNs), and supervised contrastive learning (SCL) across visual (N-MNIST) and auditory (SHD) neuromorphic datasets. Our systematic evaluation of five architectures reveals striking modality-dependent performance patterns: Hopfield networks achieve 97.68% accuracy on visual tasks but only 76.15% on auditory tasks (21.53 point gap), revealing severe modality-specific specialization, while SCL demonstrates more balanced cross-modal performance (96.72% visual, 82.16% audio, 14.56 point gap). These findings establish that memory mechanisms exhibit task-specific benefits rather than universal applicability. Joint multi-modal training with HGRN achieves 94.41% visual and 79.37% audio accuracy (88.78% average), matching parallel HGRN performance through unified deployment. Quantitative engram analysis confirms weak cross-modal alignment (0.038 similarity), validating our parallel architecture design. Our work provides the first empirical evidence for modality-specific memory optimization in neuromorphic systems, achieving 603x energy efficiency over traditional neural networks.

Problem

• Investigates modality-dependent memory mechanisms in cross-modal neuromorphic computing.
• Evaluates memory-augmented SNNs across visual and auditory neuromorphic datasets.
• Establishes modality-specific memory optimization for energy-efficient neuromorphic systems.

Innovation

• Evaluated memory mechanisms across visual and auditory neuromorphic datasets
• Revealed modality-dependent performance patterns in spiking neural networks
• Achieved high energy efficiency through parallel architecture design