An Optimization Framework for Automated Assessment of Biological Plausibility of Spiking Neurons

2026-06-16

AI Summary

This study addresses the lack of a unified definition and quantitative methodology for assessing biological plausibility in spiking neural networks. To this end, it introduces a novel framework that encodes canonical biological firing behaviors—categorized according to Izhikevich’s neuron model—into objective functions. By leveraging black-box optimization to automatically fit model parameters without prior assumptions, the approach enables the first empirical, assumption-free evaluation of biological plausibility in spiking neuron models. Implemented using PyTorch and the Norse library, the method is successfully applied to a range of classical and emerging models, demonstrating both its effectiveness and generalizability. This work thus provides a scalable foundation for systematically investigating the relationship between biological plausibility and network performance.

Abstract

Biological plausibility is a key concept in neuromorphic computing and spiking neural networks, yet it remains inconsistently defined and difficult to quantify. In this work, we present an open-source framework for the automated assessment of biological plausibility in spiking neuron models. Our method builds on the idea of evaluating a model's ability to replicate canonical neuronal firing patterns observed in biological systems, following the classification proposed by Izhikevich. By encoding these patterns into objective functions and optimizing model parameters accordingly, our framework enables empirical assessment without requiring prior analytical modeling. Treating neuron models as black boxes, it provides a practical and flexible means of characterizing their dynamic capabilities. We demonstrate the effectiveness of the framework on several established models and a previously unexplored custom model. Implemented in Python and compatible with PyTorch and the Norse library, the framework is tailored for machine learning contexts. It is intended as a starting point for systematic research into the relationship between biological plausibility and network-level performance metrics such as accuracy, energy efficiency, robustness, and adaptability.

Problem

• biological plausibility
• spiking neurons
• neuromorphic computing
• spiking neural networks
• automated assessment

Innovation

• biological plausibility
• spiking neuron models
• automated assessment
• optimization framework
• neuromorphic computing