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Biological constraints on neural network models of cognitive function

Nature Reviews Neuroscience volume 22, pages 488–502 (2021)Cite this article

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Abstract

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling.

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Fig. 1: Seven constraints for making neural networks models more biologically plausible.
Fig. 2: Networks for modelling cognitive functions.
Fig. 3: Multilevel network for explaining neural dynamics based on neuroanatomical constraints.
Fig. 4: Model of evolutionary connectivity change in left frontotemporal cortex and its functional consequences.
Fig. 5: Brain-constrained model of semantic grounding.

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Acknowledgements

The authors thank A. Aertsen, A. Cangelosi, L. Fekonja, M. Garagnani, A. Glenberg, L. Grisoni, S. Harnad, A. Knoblauch, G. Palm, T. Picht, S. Rotter and W. Schäffner for comments and suggestions on earlier versions of the manuscript and related discussions. Research funding was provided by the European Research Council, Advanced Grant “Material Constraints Enabling Human Cognition” (ERC-2019-ADG 883811), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence “Matters of Activity. Image Space Material” (DFG EXC 2025/1 – 390648296).

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Authors and Affiliations

  1. Brain Language Laboratory, Department of Philosophy and Humanities, WE4, Freie Universität Berlin, Berlin, Germany

    Friedemann Pulvermüller, Rosario Tomasello & Malte R. Henningsen-Schomers

  2. Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany

    Friedemann Pulvermüller

  3. Einstein Center for Neurosciences Berlin, Berlin, Germany

    Friedemann Pulvermüller

  4. Cluster of Excellence ‘Matters of Activity’, Humboldt-Universität zu Berlin, Berlin, Germany

    Friedemann Pulvermüller, Rosario Tomasello & Malte R. Henningsen-Schomers

  5. School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK

    Thomas Wennekers

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All authors researched data for the article. F.P. and T.W. contributed substantially to discussion of the content. F.P. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Pulvermüller, F., Tomasello, R., Henningsen-Schomers, M.R. et al. Biological constraints on neural network models of cognitive function. Nat Rev Neurosci 22, 488–502 (2021). https://doi.org/10.1038/s41583-021-00473-5

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