A dataset of differentiable biologically-derived single neuron models

Abstract

Biological neural networks contain diverse cell types with heterogeneous electrophysiological properties. Artificial neural networks (ANNs) model computational aspects of biology but use homogeneous neurons, limiting realism. A key obstacle to building bio-inspired ANNs is the absence of a database of neuron models that both fit biological data and remain differentiable for gradient-based learning. We present such a database: over 1,000 differentiable single-neuron firing-rate models with accompanying PyTorch code for integration with machine learning. The models belong to the linear-nonlinear (LN) class, which, for ease of reference, we term Generalized Firing Rate (GFR) neurons. Each GFR neuron uses input and firing-rate history filters across multiple timescales, followed by a nonlinearity to generate firing rates. Models are fit to patch-clamp recordings from mouse and human central nervous system slices. Parameter clustering reflects electrophysiological diversity and partially aligns with transgenic lines. This resource will enable development of bio-inspired ANNs built from biologically grounded, differentiable single-neuron models. HighlightsO_LIResource of over 1,000 differentiable single-neuron models fit to mouse and human patch-clamp recordings C_LIO_LIOpen source PyTorch code and reproducible pipelines support model fitting, evaluation, and network integration C_LIO_LIFitted model parameters capture electrophysiological diversity, with parameter clusters partially aligning to inhibitory/excitatory types and Cre driver lines C_LI