Discovering sparse control strategies in neural activity

Author summaryThe relationship between underlying biological circuitry and behavior is complex and difficult to probe experimentally. Part of the problem is that, for organisms of even modest size, there are an overwhelming number of possible combinations of interacting circuit components. We develop a theoretical framework to simplify this problem with experiments that change the system minimally with small perturbations. In the realm of small perturbations, not only does system behavior remain close to normal but the range of possible perturbations is greatly reduced to only pairs of experimental targets. We demonstrate such a perturbation using a minimal model of neural activity in the C. elegans worm. We find that a few combinations of pivotal neurons strongly affect the statistics of synchronous activity, suggesting they may be important for neural control of behavior. Our work suggests generalizable, feasible, and perturbative experiments to map how the physical components of an organism control emergent collective activity. Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations-ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms-to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, pivotal neurons that account for most of the system's sensitivity, suggesting a sparse mechanism of collective control.

Automated summary

The relationship between biological circuitry and behavior is complex and difficult to study experimentally. This study develops a theoretical framework that simplifies the problem by using minimal perturbations to the system. It is found that even with small perturbations, the system behavior remains close to normal and the range of perturbations is greatly reduced. By applying this protocol to a minimal model of neural activity in C. elegans, it is demonstrated that a few pivotal neurons strongly affect the statistics of synchronous activity, highlighting their importance in neural control of behavior.