Brain as a complex system, harnessing systems neuroscience tools & notions for an empirical approach (2312.13478v1)

Abstract: Finding general principles underlying brain function has been appealing to scientists. Indeed, in some branches of science like physics and chemistry (and to some degree biology) a general theory often can capture the essence of a wide range of phenomena. Whether we can find such principles in neuroscience, and [assuming they do exist] what those principles are, are important questions. Abstracting the brain as a complex system is one of the perspectives that may help us answer this question. While it is commonly accepted that the brain is a (or even the) prominent example of a complex system, the far reaching implications of this are still arguably overlooked in our approaches to neuroscientific questions. One of the reasons for the lack of attention could be the apparent difference in foci of investigations in these two fields -- neuroscience and complex systems. This thesis is an effort toward providing a bridge between systems neuroscience and complex systems by harnessing systems neuroscience tools & notions for building empirical approaches toward the brain as a complex system. Perhaps, in the spirit of searching for principles, we should abstract and approach the brain as a complex adaptive system as the more complete perspective (rather than just a complex system). In the end, the brain, even the most "complex system", need to survive in the environment. Indeed, in the field of complex adaptive systems, the intention is understanding very similar questions in nature. As an outlook, we also touch on some research directions pertaining to the adaptivity of the brain as well.

• The paper introduces novel multiscale neural data analysis techniques to dissect the brain's intricate dynamics.
• The paper demonstrates how the criticality hypothesis explains neural activity near the edge of chaos, enhancing our understanding of brain computation.
• The paper links complex systems theory with cognition by exploring bistable perception to bridge cellular mechanisms with emergent behavior.

Brain as a Complex System: Integrating Neuroscience Tools

The document "Brain as a Complex System" by Shervin Safavi, submitted as a PhD thesis, navigates the intricate landscape of neuroscience from the viewpoint of systems theory, positing the brain as a quintessential example of a complex system. The work leverages tools and frameworks from systems neuroscience to provide insights into the brain's functionality at multiple scales, and in doing so, draws heavily on concepts from complex systems science.

Overview

The thesis is structured around three central domains: methods for neural data analysis, neural theories, and cognition. Each of these informs the overarching conceptual framework that perceives the brain as an intricate network of processes displaying emergent properties and distributed functionality characteristic of complex systems.

Methods for Neural Data Analysis

In the first domain, the thesis introduces novel statistical methodologies for analyzing neural data across multiple scales. It suggests that a comprehensive understanding of the brain's complexity necessitates such an integrative approach. The proposed methods aim to dissect the neural dynamics by examining data from the cellular level to whole-brain activities, suggesting that understanding the brain's functionality requires looking beyond isolated scales and more towards interactions across these scales.

Neural Theories

The second domain discusses neural theories, particularly the criticality hypothesis, which posits that the brain operates near a critical point between order and chaos, benefiting computational processes. The thesis explores how approaching neural systems through this lens provides a complementary perspective to traditional reductionist views. This framework fundamentally suggests that the brain's dynamic complexity may be explained through principles similar to those governing other complex systems and invites further exploration of how such critical states optimize informational and computational capabilities in neural networks.

Cognition and Behavior

The third focus of the thesis is on cognition, with a particular emphasis on phenomena such as bistable perception, which are explored through complex systems theory. The investigation into perceptual processes like binocular rivalry is framed as a way to understand cognitive functions as emergent properties of complex neural interactions. The work argues for a cross-scale examination of these phenomena, proposing that understanding the neural bases of cognition will bridge the gap between large-scale observations and detailed cellular mechanisms.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the introduced methodologies can enhance our capacity to interpret multiscale neural data, potentially influencing fields like neuroengineering and computational neuroscience. Theoretically, it invites reconceptualization of neural processes in terms of criticality and complexity theory, which could offer new insights into general principles of brain function and dysfunction.

The thesis speculates that future developments in AI and computational neuroscience might increasingly draw from these findings, especially as they pertain to emulating or understanding the brain's emergent properties and adaptability. This orientation towards understanding brain function through the complex systems lens may also inform the development of artificial systems that mimic these natural computations.

Conclusion

Overall, the thesis "Brain as a Complex System" makes significant contributions to neuroscience by integrating systems neuroscience tools with complex systems theory, offering a robust framework for exploring the brain's dynamics. By widening the conceptual lens to include both the microscopic intricacies and macroscopic phenomena, it sets the stage for future research to unfold within this interdisciplinary paradigm, ultimately aiming to unravel the brain's complexities in a more holistic and integrated manner.