Event Details:
Continue the conversation: Join the speaker for a complimentary dinner in the Theory Center (second floor of the neurosciences building) after the seminar
Efficient optimization of biophysical neurons using gradient descent
Abstract
Neuroscientists fit simulations of morphologically and biophysically detailed neurons to 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 fast. We verify the usefulness 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 models identifiable. Identification breaks down gracefully as less 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.
Ilenna Jones
Harvard University
Ilenna Jones is a Research fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard. Her research interests include neuronal biophysics and computation, model optimization, and neuroscience for AI. She received her B.A. in Neuroscience in 2015 from Dartmouth College. In 2023 she received her PhD in Neuroscience at the University of Pennsylvania in Konrad Kording’s laboratory. Ilenna began her position as a Research Fellow in the Kempner Institute in 2023. There she continues her work in the neuro-AI space to investigate how subcellular neuronal properties enable single neuron and network computation using principles from optimization, deep learning, and biophysics.
Hosted by Alice Tor (see profile below)
About the Mind, Brain, Computation, and Technology (MBCT) Seminar Series
The Stanford Center for Mind, Brain, Computation and Technology (MBCT) Seminars explore ways in which computational and technical approaches are being used to advance the frontiers of neuroscience.
The series features speakers from other institutions, Stanford faculty, and senior training program trainees. Seminars occur about every other week, and are held at 4:00 pm on Mondays at the Cynthia Fry Gunn Rotunda - Stanford Neurosciences E-241.
Questions? Contact neuroscience@stanford.edu
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