Abstract
Most animal brains present two mirror-symmetric sides, but closer inspection reveals a range of asymmetries (in shape and function) that seem more salient in more cognitively complex species. Sustaining symmetric, redundant neural circuitry has associated metabolic costs, but it might aid in implementing computations within noisy environments or with faulty pieces. It has been suggested that the complexity of a computational task might play a role in breaking bilaterally symmetric circuits into fully lateralized ones; however, a rigorous, mathematically grounded theory of how this mechanism might work is missing. Here, we provide such a mathematical framework, starting with the simplest assumptions but extending our results to a comprehensive range of biologically relevant scenarios. We show mathematically that only fully lateralized or bilateral solutions are relevant within our framework (dismissing configurations in which circuits are only partially engaged). We provide maps that show when each configuration is preferred depending on costs, fitness contributed, circuit reliability, and task complexity. We discuss evolutionary paths leading from bilateral to lateralized configurations and other possible outcomes. The implications of these results for evolution, development, and rehabilitation of damaged or aging brains is discussed. Our work constitutes a limit case that should constrain and underlie similar mappings when other aspects (besides task complexity and circuit reliability) are considered.
- Received 23 March 2023
- Accepted 7 July 2023
DOI:https://doi.org/10.1103/PhysRevX.13.031028
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
synopsis
synopsisBrain Asymmetry Driven by Task Complexity
Published 13 September 2023
A mathematical model shows how increased intricacy of cognitive tasks can break the mirror symmetry of the brain’s neural network.
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Popular Summary
The human brain is mirror symmetric: Both hemispheres are similar but flipped sideways. Because of this, most neural circuitry comes in couples. There are, for example, two motor cortices, one in each side. This duplicity can be useful for controlling body parts that are mirror symmetric too, such as hands and legs. But it can also be redundant—we need to control only one language system with no obvious symmetries. Nevertheless, redundant circuits can help improve computing accuracy or provide replacements if one unit gets damaged. It has been hypothesized that increased cognitive complexity can cause the loss of bilateral symmetry, leading to specialized circuits in one side only. Here, we prove this to be true using a rigorous mathematical framework.
We lay out a minimal mathematical model to map optimal configurations (bilateral, fully lateralized, or somewhere in between) of computing neural units as a function of their running costs, fitness gain, reliability, and complexity of the task at hand. Our analysis of this model shows that increasing complexity can indeed cause the breakup of mirror symmetry in computing circuits. We also prove that complexity can operate in the opposite direction, favoring the recovery of lost duplicity. The different outcomes could be obtained in real neural systems as Darwinian evolution proceeds, or as a brain develops and ages. We chart when to expect each scenario depending on the cost and reliability of neural units and given the complexity of a cognitive task and the fitness that it contributes.
Our findings can help explain real neuroscientific scenarios, such as how a brain responds to damage or aging as well as how cognitively demanding tasks shape brain architecture.
