Classification Accuracy of Minimal Spiking Neural Networks Follows a Log-Reciprocal Function

Abstract

We investigate classification accuracy in minimal LIF-based spiking neural networks, examining its dependence on neuron count, stimulus nodes, and category number. Using an LLM to guide functional-form discovery, we compare power-law, exponential decay, and log-reciprocal candidates. The log-reciprocal model offers the strongest explanatory power: accuracy decays as 1/log(C), with neuron and stimulus effects marginal. This LLM-assisted approach efficiently identifies concise, interpretable descriptions, outperforming fixed-template methods. Our findings highlight AI's utility in computational neuroscience for uncovering interpretable relationships under resource constraints.

We need to improve the academic writing and the model in this paper