Efficient optimization of ODE neuron models using gradient descent

TL;DR

This paper presents a gradient-based optimization method for detailed neuron models using differentiable ODE solvers, enabling efficient high-dimensional parameter fitting on GPUs, advancing neuron modeling capabilities.

Contribution

The authors introduce a scalable, gradient-based algorithm for optimizing complex neuron models with many parameters, improving over traditional gradient-free methods.

Findings

Gradient-based optimization is efficient for high-dimensional neuron models.

Using differentiable ODE solvers enables parallel GPU computations.

Model parameters become identifiable with comprehensive stimulation and recording.

Abstract

Neuroscientists fit morphologically and biophysically detailed neuron simulations to physiological data, often using evolutionary algorithms. However, such gradient-free approaches are computationally expensive, making convergence slow when neuron models have many parameters. Here we introduce a gradient-based algorithm using differentiable ODE solvers that scales well to high-dimensional problems. GPUs make parallel simulations fast and gradient calculations make optimization efficient. We verify the utility of our approach optimizing neuron models with active dendrites with heterogeneously distributed ion channel densities. We find that individually stimulating and recording all dendritic compartments makes such model parameters identifiable. Identification breaks down gracefully as fewer stimulation and recording sites are given. Differentiable neuron models, which should be added to popular neuron simulation packages, promise a new era of optimizable neuron models with many free parameters, a key feature of real neurons.