An Optimization Framework for Automated Assessment of Biological Plausibility of Spiking Neurons
For researchers in neuromorphic computing and spiking neural networks, this framework provides a practical tool to quantify biological plausibility, addressing the lack of standardized assessment methods.
The paper introduces an open-source framework for automatically assessing the biological plausibility of spiking neuron models by evaluating their ability to replicate canonical firing patterns. The framework is demonstrated on several established models and a custom model, showing its effectiveness.
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.