2025-07-22 –, Main Room 4
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.
Computational neuroscience aims to simulate the brain in silico, from single synapses to brain-wide networks. The field has matured in tandem with experimental neuroscience, where computational modeling has become an indispensable tool for understanding neuroscience data and motivating future experiments. However, building such models can involve a painstaking process of translating concepts from the literature into working code and then optimizing it to run in a reasonable timeframe.
Neuroblox is a new software platform for computational neuroscience that aims to break down these barriers. It is based on the Julia programming language and built with simplicity, modularity, and performance. It consists of a library of modular computational building blocks (“Blox”) that can be easily assembled to simulate brain dynamics in code or via an easy-to-use graphical interface. Our tools bridge scales from spiking neurons to brain waves and fMRI and have applications to neurology and psychiatry. Moreover, the behavior of multiple model variants can be compared to discriminate between competing hypotheses.
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. By the end of the course, you will be able to model your own data and build your own custom “blox” that may be incorporated into the Neuroblox library.
The structure of the workshop is as follows:
Each session consists of a short (~15 min) lecture + hands-on activities
Session 1: Introduction to Neuroblox, Differential equations and plotting in Julia, Design new Blox components and connection rules
Session 2: Single neurons & neural masses with external sources, Multiple neurons & neural masses in circuits
Session 3: Running a decision-making task with a circuit model, Synaptic plasticity, and learning
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