Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules (2506.06750v1)

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.

• The paper combines spiking neural networks with Lempel-Ziv Complexity to enhance classification performance on spatiotemporal data.
• Backpropagation achieved perfect accuracy on various datasets but demands high computational resources, while bio-inspired rules offer efficient alternatives.
• The study highlights that integrating LZC with SNNs provides robust, interpretable models ideal for neuromorphic and real-time applications.

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

The paper "Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks - Influence of the Learning Rules" offers an insightful exploration into the integration of Spiking Neural Networks (SNN) with Lempel-Ziv Complexity (LZC) to enhance classification performance on spatiotemporal data. The researchers, Zofia Rudnicka, Janusz Szczepanski, and Agnieszka Pregowska, propose a hybrid approach that seeks to leverage the bio-realistic temporal dynamics of SNNs alongside the analytical prowess of LZC's structural complexity in classifying neural data.

Key Findings and Contributions

1. Challenge of SNN Training: Training SNNs is inherently challenging due to their reliance on spike-based, non-differentiable communications. The paper explores various learning algorithms, focusing on both classical and biologically inspired rules, such as backpropagation, tempotron, and Spikprop. Each presents distinct trade-offs between computational efficiency and accuracy.
2. Backpropagation Efficiency and Cost: The paper confirms the high classification accuracy of backpropagation, achieving perfect results across different types of datasets, including Bernoulli, Markov, and Poisson processes. However, this comes at the cost of significant computational resources, rendering it impractical for real-time applications.
3. Bio-Inspired Alternatives: Tempotron and Spikprop deliver competitive performance with reduced computational demands, marking them as suitable for applications requiring swift processing. These findings underline the utility of biologically inspired methods in reducing computational complexity while maintaining robust model performance.
4. LZC Integration for Classification: The innovative use of LZC in conjunction with SNNs facilitates a framework that is not only efficient but also offers interpretability and robustness against varying temporal dynamics in data, such as Poisson-distributed signals.
5. Numerical Analysis: The paper presents a comprehensive numerical analysis revealing classification accuracies from 89.50% to 100.00%, showing varied computational times. Algorithms like BP and STBP achieved perfect accuracy but at the cost of increased computational time, especially with Poisson inputs, underscoring the complexity of handling irregular spike train patterns.

Implications for Future Research

The paper draws attention to several pivotal areas for future exploration:

• Optimization of Learning Algorithms: The development of learning strategies that dynamically adjust according to the characteristics of input data could further enhance the efficiency of SNNs. Optimizing parameters like spike timing and plasticity could bridge the gap between model accuracy and computational feasibility.
• Adaptability Under Stochastic Inputs: Considering the challenges posed by Poisson-distributed data, future research is encouraged to focus on improving the adaptability of learning algorithms to high-stochastic environments. This would bolster the robustness of SNNs in more biologically authentic scenarios.
• Applications in Neuromorphic Hardware: The findings provide a pertinent basis for translating SNN and LZC integrations into neuromorphic hardware applications, emphasizing real-time adaptability and energy-efficient processing.
• Real-time Trade-off Evaluation: Incorporating real-time analysis of trade-offs between accuracy and computational costs in diverse environments could refine the applicability and efficiency of such bio-inspired frameworks.

In conclusion, the paper offers a nuanced perspective on the synergy between spiking neural computations and information-theoretic measures, contributing significantly to the discourse on achieving efficient, interpretable, and biologically plausible computational models. As SNNs continue to gain traction, embracing bio-inspired strategies will be pivotal for advancing computational neuroscience and AI.