Helmut Strey
I am an Associate Professor at the Biomedical Engineering Department at Stony Brook University. I also have affiliate positions at the Martinos Center for Biomedical Imaging at MGH/Harvard Medical School and at JuliaLab at MIT/CSAIL. I am currently leading the development of Neuroblox.jl, a Julia package to design, simulate, and analyze dynamic models of the brain. Our effort is built on top of ModelingToolkit.jl, but we are also developing our own, and sometimes more efficient, algorithms to build graphs of dynamical motives (we just released GraphDynamics.jl
Sessions
Computational neuroscience aims to simulate the brain in silico, from single synapses to brain-wide networks. In this workshop, you will learn the basics of computational neuroscience via hands-on model building in Neuroblox and Julia. You will simulate models from the literature, from single neurons to large circuits with synaptic plasticity, and fit them to neural data.
Scientific machine learning has proven effective in deriving equations for complex dynamical systems but faces challenges with chaotic systems, particularly in biological systems with incomplete theories and noisy data. We present a new approach combining universal differential equations with the prediction-error method from optimization to successfully learn neural system dynamics from simulated and real spiking neural networks.
Dynamic Causal Modeling (DCM) is a key method for inferring neural connectivity among brain regions. We present a novel Julia-based implementation of spectral DCM using Julia's ModelingToolkit and automatic differentiation. We provide a fast, modular platform for biophysically detailed models, validated against the widely used MATLAB based Statistical Parametric Mapping software commonly used to estimate DCMs.