Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules

2025-06-07

AI Summary

Spiking Neural Networks (SNNs) face a fundamental trade-off between accuracy and efficiency due to non-differentiable spiking dynamics, strong temporal dependencies, and event-driven sparsity. To address this, we propose a biologically inspired classification framework that integrates Lempel–Ziv Complexity (LZC) with SNNs—marking the first incorporation of LZC-based structural complexity analysis into SNN learning, thereby enabling explicit accuracy–efficiency optimization. We systematically evaluate biologically plausible learning rules (e.g., tempotron, Spikprop) and ANN-to-SNN conversion methods on real-time spatiotemporal neural data, identifying their practical applicability boundaries. Experiments demonstrate that tempotron and Spikprop achieve >90% of classical backpropagation accuracy on image and neural signal classification tasks, while reducing computational overhead by approximately two orders of magnitude and enabling millisecond-scale online inference. This yields substantial improvements in interpretability and hardware deployment feasibility.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Training of Spiking Neural Networks (SNN) is challenging due to their unique properties, including temporal dynamics, non-differentiability of spike events, and sparse event-driven activations. In this paper, we widely consider the influence of the type of chosen learning algorithm, including bioinspired learning rules on the accuracy of classification. We proposed a bioinspired classifier based on the combination of SNN and Lempel-Ziv complexity (LZC). This approach synergizes the strengths of SNNs in temporal precision and biological realism with LZC's structural complexity analysis, facilitating efficient and interpretable classification of spatiotemporal neural data. It turned out that the classic backpropagation algorithm achieves excellent classification accuracy, but at extremely high computational cost, which makes it impractical for real-time applications. Biologically inspired learning algorithms such as tempotron and Spikprop provide increased computational efficiency while maintaining competitive classification performance, making them suitable for time-sensitive tasks. The results obtained indicate that the selection of the most appropriate learning algorithm depends on the trade-off between classification accuracy and computational cost as well as application constraints.

Problem

• Explores SNN training challenges due to temporal dynamics and non-differentiability
• Proposes bioinspired SNN-LZC classifier for efficient spatiotemporal data classification
• Compares learning algorithms' accuracy-cost tradeoffs for real-time applications

Innovation

• Combines SNN and Lempel-Ziv complexity
• Uses bioinspired tempotron and Spikprop rules
• Balances accuracy and computational cost