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. 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.