Much of what consumes our everyday existence is fundamentally about understanding and managing human behavior. Science in general offers a very clear outline as to how we might pursue that aim. First, science describes the fundamental operations of all living systems, explaining how they self-organize
and self-assemble within the environment that is planet earth, how quantum physics drive the fundamental dynamics of chemistry, and how those dynamics in turn drive the workings of biological (living) systems. We humans are of course the most complex of these biological systems, and many different cognitive sciences (e.g., linguistics, psychology, psychiatry, neuroscience, anthropology), as well as disciplines like philosophy, seek to explain the intricacies of human nature and the behaviors that flow out of our complex human biological systems.
But what scientific discipline integrates and reconciles all these complex biochemical mechanics within living systems with all the other driving forces of our universe and solar system? We propose that the Center for Integrative Research’s approach to “brain systems science” accomplishes precisely this
function. Brain systems science emphasizes the notion that the human brain is fundamentally a self-organizing, self-assembling complex adaptive system, consistent with and parallel to the same self-organizing dynamics of the universe itself, as evidenced by the many open, dissipative systems that are found on earth. Accordingly, in order to explain human nature and behavior, brain systems science
not only resorts to research from the field of neuroscience but incorporates the tenets and relevant insights of thermodynamics, chaos theory and complexity science, information theory, network science, genetics/epigenetics, and even cultural anthropology.
In fulfilling this integrating function, brain systems science highlights some of the key elements that factor into every aspect of human existence. Brain systems science relies heavily on the dynamics of self-organization to explain not only the computational nature of the human brain system but the evolutionary dynamics of our social and cultural systems as well. More specifically, brain systems science
focuses on the importance of the human brain’s adaptive capacities and interdependent relationship with its particular environmental conditions, networked functional systems, top-down and bottom up computational dynamics, robust feedforward and feedback systems, and predictable tendency to become
increasingly complex but constrained by such complexity. Perhaps most importantly, brain systems science highlights the value its self-assembling, complex adaptive system’s many emergent phenomena, particularly the mind and its many cognitive tools such as imagination, ambition, and intentionality. And
because brain systems science merges principles of complexity science and neuroscience, it pulls in key principles of physics, such as thermodynamics (especially concepts of entropy, dissipation of energy, and disorder) and information theory, which make it an ideal context-generating platform for investigating and researching a wide variety of issues related to human nature and behavior.
For example, brain systems science is also particularly well suited to investigating the challenges and prospects related to one of the world’s most relevant and significant technological developments—the emergence of artificial intelligence and its functionality within biotech. The possibilities that exist within
the future of biotech and related computational activities appear at this point to be practically endless, but many issues will emerge that require thoughtful analysis and resolution. For example, many of the LLM (large language model) AI systems that can engage in deep learning perform well at some aspects of prediction making that the human brain system does particularly well, key questions remain about how versatile such artificial intelligence can be extended. There may be certain constraints and potential liabilities that emerge from all the developmental processes behind intelligent machines, and brain systems science can used to identify many of them.
The computational systems built on platforms of artificial neural networks, like those of DeepMind’s DQN, LaMDA, and AlphaFold2 products, may suggest an endlessly open and unlimited ascent toward artificial or even quasi-artificial computing power and functional prowess, but brain systems science can already
identify three significant unanswered questions that might someday stymie that ascent.
- First, computational systems will be built using organic components that might be able to repair and replicate themselves in ways that parallel living systems like the human brain—however, they have yet to prove that they could do so in direct response to their environmental conditions, to provide the optimal capacity for prediction making and adaptive shifts in computational strategies.
- Second, such systems have yet to establish that they can access and utilize the ideal configurations of diverse information inputs that are naturally embedded in self-organizing, living computational systems like the human brain system. Moreover, artificial intelligence may not be able to achieve the same naturally forming and computationally optimal configurations of neural and functional networks that self-organize and self-assemble within the human brain system, in concert with the demands placed upon it by its environment. Accordingly, artificial machines may never become as easily and rapidly adaptive as the human brain.
- Second, such systems have yet to establish that they can access and utilize the ideal configurations of diverse information inputs that are naturally embedded in self-organizing, living computational systems like the human brain system. Moreover, artificial intelligence may not be able to achieve the same naturally forming and computationally optimal configurations of neural and functional networks that self-organize and self-assemble within the human brain system, in concert with the demands placed upon it by its environment. Accordingly, artificial machines may never become as easily and rapidly adaptive as the human brain.
- Third, such artificial and quasi-artificial computational systems have not even scratched the surface of the dynamics by which the human brain system naturally generates emergent phenomena like conscious mind, intent, feelings, agency, motivation, and so on. Such emergent phenomena serve critical roles, especially within top-down operational dynamics, in determining the range of behaviors and cognitive outputs that define human intelligence and productivity.
We are fast approaching the time when any conversation about the utility and significance of artificial intelligence will require a deeper examination of legal, ethical, and moral issues that accompany its increasing computational effectiveness. Brain systems science makes clear that natural, or living, systems
compute by relying on neural networks that self-assemble from information and intelligence that has been tested for efficiency and resilience over more than a billion years. For example, neurons emerged within the long evolutionary process because of how effective their cellular design is at both storing and transmitting information, feats that cannot yet be duplicated by artificial neurons. Additionally, thanks to self-organizing, evolutionary dynamics, the human brain system contains a highly optimized set of neural network structures that have been tested over millions of years for both efficiency of information transfer and resilience to network failure. Such naturally occurring designs are not easily replicated in artificial machines, either by reverse engineering structures in the human brain or by use of artificially generated design systems.