{
  "abstract": "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 govern how neural activity evolves over time – 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 may therefore be 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 Toolkit (CtDToolkit) addresses 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 CtDToolkit can help guide the development, tuning, and troubleshooting of neural dynamics models. In summary, CtDToolkit provides a necessary framework for model developers to better understand and characterize neural computation through the lens of dynamics. Author summary: Understanding how the brain works requires interpretable accounts of how populations of neurons process information to produce behavior. One powerful approach is to study “neural dynamics,” the patterns of how neural activity evolves over time. Scientists develop computational models to infer these dynamics from neural recordings, but it has been challenging to know when the inferred dynamics are trustworthy. Existing datasets often lack key features of biological neural circuits, and current performance metrics can provide an incomplete picture of model quality.",
  "abstract_shown": true,
  "affiliations": [
    "Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America",
    "Center for Machine Learning, Georgia Institute of Technology, Atlanta, Georgia, United States of America",
    "Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America",
    "Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States of America",
    "Department of Statistics, Stanford University, Stanford, California, United States of America",
    "Allen Institute for Neural Dynamics, Seattle, Washington, United States of America",
    "Department of Neurobiology & Biophysics, University of Washington, Seattle, Washington, United States of America",
    "Département de Neurosciences, Faculté de Médecine, Université de Montréal, Montréal, Canada",
    "MILA, Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada",
    "School of Kinesiology and Health Science, Faculty of Health, York University, Toronto, Ontario, Canada",
    "Department of Electrical Engineering, Stanford University, Stanford, California, United States of America",
    "Department of Neurosurgery, Emory University, Atlanta, Georgia, United States of America"
  ],
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      "name": "Jonathan D. McCart"
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      "name": "Mitchell Ostrow"
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      "name": "David M. Zoltowski"
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    {
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      "name": "Clayton B. Washington"
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      "name": "Laura Driscoll",
      "orcid": "0000-0001-5247-1857"
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      "name": "Olivier Codol"
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      "name": "Jonathan A. Michaels",
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      "name": "Scott W. Linderman"
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        10
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      "name": "David Sussillo"
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    {
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      "name": "Chethan Pandarinath"
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  "published": "2026-10-15",
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  "subjects": [
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      "field": "biology",
      "origin": "heading:Cell Biology",
      "subfield": "cell-biology"
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  "title": "Computation-through-Dynamics Toolkit: Simulated datasets and quality metrics for dynamical models of neural activity",
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