Keynote & Tutorial
Monday, August 3, 4:30 - 6:15 pm
In silico neuroscience: an emerging paradigm for brain discovery
Alessandro Gifford1, Domenic Bersch2, Gemma Roig2, Radoslaw Cichy1, 1 Freie Universität Berlin, 2 Goethe Universität Frankfurt
Abstract
The functioning of the brain largely remains unsolved, partly because collecting in vivo neural data is slow and expensive, creating a bottleneck for brain experimentation and discovery. The emerging paradigm of in silico neuroscience addresses this limitation by leveraging encoding models of the brain, algorithms that predict neural responses to massive amounts of sensory stimuli in a fast and economical fashion. The scalability of in silico neural responses – which are used as stand-ins for in vivo neural responses during experimentation and data analysis – allows researchers to test more scientific hypotheses and to upscale exploratory research compared to experimenting on in vivo data. Crucially, novel findings from large-scale in silico experimentation are eventually validated in vivo, but with targeted small-scale data collection, therefore optimizing research resources and allowing for faster neuroscientific discovery. This keynote and tutorial advocates for this emerging research paradigm on theoretical, empirical, and methodological grounds. Theoretically, we will cover its advantages and limitations. Empirically, we will present recent work from our and other groups, where large-scale in silico experiments enabled discoveries that were then validated with targeted in vivo experiments. Methodologically, we will introduce the Brain Encoding Response Generator (BERG), a resource that we created consisting of pretrained encoding models of the brain and a Python package to facilitate researchers in the generation of in silico neural responses for visual and linguistic stimuli. Together, our goal is to excite interest in, and facilitate adoption of, this novel paradigm for brain discovery.
Tutorial Outline
The goal of the tutorial is to give participants hands-on experience with the emerging paradigm of in silico neuroscience, through three stages that logically progress into each other. In the first stage (~30 minutes), participants will learn how to use the Brain Encoding Response Generator (https://gifale95.github.io/BERG/) to generate in silico neural responses to visual stimuli using pre-trained encoding models of the brain. To establish the reliability of these in silico responses, in the second stage (~30 minutes) participants will experimentally show that they capture fundamental organizing principles of visual cortex, such as retinotopy or category selectivity. This will suggest that in silico responses also allow for scientific discovery. Thus, in the third stage (~45 minutes) participants will run large-scale experiments on in silico fMRI responses to explore visual selectivity across visual cortex, but also across underexplored cortical regions in vision neuroscience such as frontal areas. Throughout the tutorial, participants will actively make decisions such as which brain areas to investigate or which stimulus set to use, making the session highly interactive and experimental.
The tutorial will be accessible to researchers at all career stages with basic Python programming experience and a foundational understanding of fMRI data and regression-based statistical methods. Participants will implement in silico experiments through Google Colab notebooks in Python that run on the cloud without any software installation or data download, or locally as Jupyter notebooks. Although the tutorial will primarily focus on vision, the acquired experience naturally extends to other research domains within the CCN community such as language, audition, or motor planning, making the tutorial valuable to a diverse range of researchers interested in the emerging research paradigm of in silico neuroscience.