Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective

Summary

This paper proposes a foundational, information-theoretic framework for effectively controlling adaptive complex systems like the mammalian nervous system. The article argues that brain-wide neural computations are organized hierarchically across multiple interacting spatiotemporal scales rather than being localized to individual components. Pointing out the shortcomings of traditional microscopic or single-scale neurostimulation paradigms, the paper demonstrates that successful neural control is better achieved by targeting aggregate computational outputs and matching the scale of external intervention to the specific circuit modules responsible for given functional behaviors.

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@ARTICLE{Dehghani_netControl_2018,    
AUTHOR={Dehghani, Nima},           
TITLE={Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective},          
JOURNAL={Frontiers in Computational Neuroscience},        
VOLUME={Volume 12 - 2018},
YEAR={2018},
URL={https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2018.00081},
DOI={10.3389/fncom.2018.00081},  
ISSN={1662-5188},
}

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Abstract

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.