Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity Article Swipe
Christopher Versteeg
,
Jonathan McCart
,
Mitchell Ostrow
,
David M. Zoltowski
,
Clayton Washington
,
Laura Driscoll
,
Olivier Codol
,
Jonathan A. Michaels
,
Scott W. Linderman
,
David Sussillo
,
Chethan Pandarinath
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.07.637062
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.02.07.637062
A primary goal of systems neuroscience is to discover how ensembles of neurons transform inputs into goal-directed behavior, a process known as neural computation. A powerful framework for understanding neural computation uses neural dynamics – the rules that describe the temporal evolution of neural activity – to explain how goal-directed input-output transformations occur. As dynamical rules are not directly observable, we need computational models that can infer neural dynamics from recorded neural activity. We typically validate such models using synthetic datasets with known ground-truth dynamics, but unfortunately existing synthetic datasets don’t reflect fundamental features of neural computation and are thus poor proxies for neural systems. Further, the field lacks validated metrics for quantifying the accuracy of the dynamics inferred by models. The Computation-through-Dynamics Benchmark (CtDB) fills these critical gaps by providing: 1) synthetic datasets that reflect computational properties of biological neural circuits, 2) interpretable metrics for quantifying model performance, and 3) a standardized pipeline for training and evaluating models with or without known external inputs. In this manuscript, we demonstrate how CtDB can help guide the development, tuning, and troubleshooting of neural dynamics models. In summary, CtDB provides a critical platform for model developers to better understand and characterize neural computation through the lens of dynamics.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.02.07.637062
- OA Status
- green
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407278427
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- OpenAlex ID
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https://openalex.org/W4407278427Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.02.07.637062Digital Object Identifier
- Title
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Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activityWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-08Full publication date if available
- Authors
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Christopher Versteeg, Jonathan McCart, Mitchell Ostrow, David M. Zoltowski, Clayton Washington, Laura Driscoll, Olivier Codol, Jonathan A. Michaels, Scott W. Linderman, David Sussillo, Chethan PandarinathList of authors in order
- Landing page
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https://doi.org/10.1101/2025.02.07.637062Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.1101/2025.02.07.637062Direct OA link when available
- Concepts
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Computer science, Benchmark (surveying), Computation, Artificial neural network, Artificial intelligence, Machine learning, Field (mathematics), Models of neural computation, System dynamics, Algorithm, Mathematics, Geography, Geodesy, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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