Michael Deistler

Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

The paper introduces JO_SCPLOWAXLEYC_SCPLOW, a framework for differentiable simulation of biophysical neuron models, enabling efficient parameter optimization and task-driven training of large-scale models.

From Simulations to Inference: Using Machine Learning to Tune Patient-Specific Finite-Element Models of the Middle Ear Towards Objective Diagnosis

This paper uses simulation-based inference to tune a finite-element model of the middle ear, providing probability distributions of parameters that reproduce experimental data and enabling objective diagnosis of hearing loss.

Combined statistical-mechanistic modeling links ionchannel genes to physiology of cortical neuron types

The paper develops a hybrid modeling approach to link ion channel gene expression to the electrophysiological properties of cortical neuron types using statistical and mechanistic models.

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes

This paper introduces an SBI framework for diffusion MRI analysis, showing it provides fast and accurate parameter estimation and uncertainty mapping compared to classical Bayesian methods.

A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations

The paper develops a biophysical model of the mouse outer retina to study contrast invariant coding, finding that nonlinearities in the retina enable robust classification despite contrast variations.

Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms

The paper introduces AutoMIND, a method for inverse modeling of neural circuits, revealing dynamic-dependent invariances in parameter configurations across different neural dynamics.

Energy efficient network activity from disparate circuit parameters

The paper investigates how metabolic cost influences neural circuit parameters in the crab pyloric network, finding energy-efficient circuits with diverse conductance combinations.

Training deep neural density estimators to identify mechanistic models of neural dynamics

This paper introduces a machine learning method using deep neural density estimators to perform Bayesian inference on neural dynamics models, demonstrating its application to various neural systems.