2025-07-23 –, Main Room 2
Neuroblox.jl is designed for computational neuroscience and psychiatry applications. Our tools range from control circuit system identification to brain circuit simulations bridging scales from spiking neurons to fMRI-derived circuits, parameter-fitting models to neuroimaging data, interactions between the brain and other physiological systems, experimental optimization, and scientific machine learning. In this talk we will give an update on the new features we added in the last two years.
Neuroblox.jl is based on a library of modular computational building blocks (“blox”) in the form of systems of symbolic dynamic differential equations that can be combined to describe large-scale brain dynamics. Once a model is built, it can be simulated efficiently and fit electrophysiological and neuroimaging data. Moreover, the circuit behavior of multiple model variants can be investigated to aid in distinguishing between competing hypotheses. We employ ModelingToolkit.jl to describe the dynamical behavior of blox as symbolic (stochastic/delay) differential equations. Our libraries of modular blox consist of individual neurons (Hodgkin-Huxley, IF, QIF, LIF, etc.), neural mass models (Jansen-Rit, Wilson-Cowan, Lauter-Breakspear, Next Generation, microcanonical circuits etc.) and biomimetically-constrained control circuit elements. A GUI designed to be intuitive to neuroscientists allows researchers to build models that automatically generate high-performance systems of numerical ordinary/stochastic differential equations from which one can run simulations with parameters fit to experimental data. Our benchmarks show that the increase in speed for simulation often exceeds a factor of 100 as compared to neural mass model implementation by the Virtual Brain (python and similar packages in MATLAB. For parameter fitting of brain circuit dynamical models, we use Optimization.jl for least-square fitting, andTuring.jl to perform probabilistic modeling, including Hamilton-Monte-Carlo sampling and Automated Differentiation Variational Inference. New features of Neuroblox.jl include 1) deep-brain stimulation, 2) implementation of various neurotransmitter receptor dynamics to model brain-drug interactions, and a new GUI with a flatbrain interface.
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