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Hierarchical Models in the Brain

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  • Karl Friston

Abstract

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to generalised convolution models, with system noise, for nonlinear time-series analysis. Crucially, all of these models can be inverted using exactly the same scheme, namely, dynamic expectation maximization. This means that a single model and optimisation scheme can be used to invert a wide range of models. We present the model and a brief review of its inversion to disclose the relationships among, apparently, diverse generative models of empirical data. We then show that this inversion can be formulated as a simple neural network and may provide a useful metaphor for inference and learning in the brain. Author Summary: Models are essential to make sense of scientific data, but they may also play a central role in how we assimilate sensory information. In this paper, we introduce a general model that generates or predicts diverse sorts of data. As such, it subsumes many common models used in data analysis and statistical testing. We show that this model can be fitted to data using a single and generic procedure, which means we can place a large array of data analysis procedures within the same unifying framework. Critically, we then show that the brain has, in principle, the machinery to implement this scheme. This suggests that the brain has the capacity to analyse sensory input using the most sophisticated algorithms currently employed by scientists and possibly models that are even more elaborate. The implications of this work are that we can understand the structure and function of the brain as an inference machine. Furthermore, we can ascribe various aspects of brain anatomy and physiology to specific computational quantities, which may help understand both normal brain function and how aberrant inferences result from pathological processes associated with psychiatric disorders.

Suggested Citation

  • Karl Friston, 2008. "Hierarchical Models in the Brain," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-24, November.
  • Handle: RePEc:plo:pcbi00:1000211
    DOI: 10.1371/journal.pcbi.1000211
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    References listed on IDEAS

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    Cited by:

    1. John C. Boik, 2020. "Science-Driven Societal Transformation, Part I: Worldview," Sustainability, MDPI, vol. 12(17), pages 1-28, August.
    2. Micha Heilbron & Florent Meyniel, 2019. "Confidence resets reveal hierarchical adaptive learning in humans," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-24, April.
    3. Jaroslav Vítků & Petr Dluhoš & Joseph Davidson & Matěj Nikl & Simon Andersson & Přemysl Paška & Jan Šinkora & Petr Hlubuček & Martin Stránský & Martin Hyben & Martin Poliak & Jan Feyereisl & Marek Ros, 2020. "ToyArchitecture: Unsupervised learning of interpretable models of the environment," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-50, May.
    4. David Balduzzi & Giulio Tononi, 2009. "Qualia: The Geometry of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-24, August.
    5. Annika Garlichs & Helen Blank, 2024. "Prediction error processing and sharpening of expected information across the face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    6. Ünsal Özdilek, 2021. "Sensing Happiness in Senseless Information," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 16(5), pages 2059-2084, October.
    7. Alexander Ororbia & Daniel Kifer, 2022. "The neural coding framework for learning generative models," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Adeeti Aggarwal & Connor Brennan & Jennifer Luo & Helen Chung & Diego Contreras & Max B. Kelz & Alex Proekt, 2022. "Visual evoked feedforward–feedback traveling waves organize neural activity across the cortical hierarchy in mice," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    9. Mateus Joffily & Giorgio Coricelli, 2013. "Emotional valence and the free-energy principle," Post-Print halshs-00862392, HAL.
    10. Dileep George & Jeff Hawkins, 2009. "Towards a Mathematical Theory of Cortical Micro-circuits," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-26, October.
    11. Boris Vladimirskiy & Robert Urbanczik & Walter Senn, 2015. "Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.
    12. Falk Lieder & Klaas E Stephan & Jean Daunizeau & Marta I Garrido & Karl J Friston, 2013. "A Neurocomputational Model of the Mismatch Negativity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    13. Adam Safron, 2019. "Multilevel evolutionary developmental optimization (MEDO): A theoretical framework for understanding preferences and selection dynamics," Papers 1910.13443, arXiv.org, revised Nov 2019.
    14. Biswa Sengupta & Arturo Tozzi & Gerald K Cooray & Pamela K Douglas & Karl J Friston, 2016. "Towards a Neuronal Gauge Theory," PLOS Biology, Public Library of Science, vol. 14(3), pages 1-12, March.

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