The Brain Encoding Response Generator (BERG)

Aug 12, 2025·
Alessandro T. Gifford
,
Domenic Bersch
,
Gemma Roig
,
Radoslaw M. Cichy
· 2 min read
Abstract
In silico neural responses generated from encoding models increasingly resemble in vivo responses recorded from real brains, enabling the novel research paradigm of in silico neuroscience. In silico neuroscience scales beyond what is possible with in vivo data, allowing to explore and test scientific hypotheses across vastly larger solution spaces. To catalyze this emerging research paradigm, here we introduce the Brain Encoding Response Generator (BERG), a resource consisting of multiple pre-trained encoding models of the brain and a Python package to generate accurate in silico neural responses to massive amounts of arbitrary stimuli with a few lines of code (https://github.com/gifale95/BERG). We show that BERG’s encoding models accurately predict neural responses to visual stimuli, and that these in silico responses reproduce key neural signatures of visual processing in the brain. Together, this opens the doors to using in silico neural responses for scientific discovery, which we envision will lead to a more efficient and reproducible science.
Type
Publication
Cognitive Computational Neuroscience (CCN)

Brain Encoding Response Generator (BERG) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.

In silico neural responses from encoding models increasingly resemble in vivo responses recorded from real brains, enabling the novel research paradigm of in silico neuroscience. In silico neural responses are quick and cheap to generate, allowing researchers to explore and test scientific hypotheses across vastly larger solution spaces than possible in vivo. Novel findings from large-scale in silico experimentation are then validated through targeted small-scale in vivo data collection, in this way optimizing research resources. Thus, in silico neuroscience scales beyond what is possible with in vivo data, and democratizes research across groups with diverse data collection infrastructure and resources.

To catalyze this emerging research paradigm, we introduce BERG, a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code. BERG includes a growing, well documented library of encoding models trained on different neural data acquisition modalities, datasets, subjects, stimulation types, and brain areas, offering broad versatility for addressing a wide range of research questions through in silico neuroscience.

This paper was presented at CCN 2025. For more details, visit the website, code repository, documentation, or CCN 2025 poster.

Summary. An optional shortened abstract.

summary: | Neural Encoding Simulation Toolkit (NEST) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.

tags:

  • Neuroscience
  • Machine Learning
  • Encoding Models

featured: true

links:

- name: Frontiers

url: https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2025.1515873/abstract

- name: DOI

url: https://doi.org/10.48550/arXiv.2208.09677

Featured image

To use, add an image named featured.jpg/png to your page’s folder.

image:

caption: ‘Image credit: Unsplash’

focal_point: ""

preview_only: false

Associated Projects (optional).

projects:

  • nest

slides: ""

Neural Encoding Simulation Toolkit (NEST) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.

This paper is still in preparation, but for more details, visit the code repository.

Neuroscience Machine Learning Encoding Models