The neuron as a direct data-driven controller

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptuali...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2024-07, Vol.121 (27), p.e2311893121
Main Authors: Moore, Jason J, Genkin, Alexander, Tournoy, Magnus, Pughe-Sanford, Joshua L, de Ruyter van Steveninck, Rob R, Chklovskii, Dmitri B
Format: Article
Language:English
Subjects:
Action Potentials - physiology
Animals
Biological Sciences
Computational neuroscience
Control systems
Control theory
Effectiveness
Feedback
Feedback control
Feedback loops
Firing pattern
Humans
Models, Neurological
Motor task performance
Neural networks
Neuronal Plasticity - physiology
Neurons
Neurons - physiology
Neuroplasticity
Optimization
Physical Sciences
Physiology
Potentiation
Sensory feedback
Sensory neurons
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Summary:In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.2311893121