Summary
Understanding how the brain processes information and makes decisions is complicated. This paper introduces new ways to measure and think about information inside the brain, like information enthalpy and information enthalpy potential, which describe the brain's internal resources for making predictions. The authors connect these ideas with existing brain theories and propose a model inspired by physics (an n-body model) to explain how different brain processes interact to produce intelligence. Their framework offers testable ideas for better understanding neural behavior.
What this means in practice
- •For neural model developers: Introduce information enthalpy and enthalpy potential metrics into brain-inspired models to improve prediction of neural system behavior.
- •For complex system analysts: Use the n-body-inspired framework to model interactions in high-dimensional adaptive systems beyond neuroscience.
A position paper. It proposes an approach and reports no results.
Authors
Brett J. Kagan, Johnson Zhou, Forough Habibollahi, Valentina Baccetti
Abstract
Theories of how the brain processes information and returns intelligent outputs are numerous and often difficult to conclusively test. Here, we consider an approach grounded in informatics and algorithmic thermodynamics for how neural systems respond to different patterns of information in different ways, with a focus on the relationship between the information entropy of a signal and a new proposed quantity we term information enthalpy, which represents the internal resource of structured information within a system available to be used for predictions and other forms of work. To evaluate the capacity of the external signals to increase information enthalpy in the system, we introduce the information enthalpy potential (IEP) as a defined metric. We propose and implement a method for quantifying the amount of potential information enthalpy - the IEP - in a given signal across a range of example signals. We offer a conjecture of how information enthalpy may be treated by neural systems within a biological framework before showing how it may integrate and offer falsifiability to existing approaches such as the Free Energy Principle. This framework is also positioned in light of the role of neural criticality. Finally, we postulate a testable and falsifiable framework where the distinct mechanisms within each driver interact through a described n-body-inspired model to govern the "motion" of a neural system through a high dimensional state-space. Through these processes, it is proposed that these features form the most fundamental basis of the neural drivers that give rise to the complexity of adaptive behaviors that are commonly called: intelligence.