Editorial: Decision making from the perspective of neural thermodynamics and molecular information processing (original) (raw)
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The thermodynamics of cognition: A Mathematical Treatment
Computational and Structural Biotechnology Journal 19(2), 2021
There is a general expectation that the laws of classical physics must apply to biology, particularly the neural system. The evoked cycle represents the brain's energy/information exchange with the physical environment through stimulus. Therefore, the thermodynamics of emotions might elucidate the neurological origin of intellectual evolution, and explain the psychological and health consequences of positive and negative emotional states based on their energy profiles. We utilized the Carnot cycle and Landauer's principle to analyze the energetic consequences of the brain's resting and evoked states during and after various cognitive states. Namely, positive emotional states can be represented by the reversed Carnot cycle, whereas negative emotional reactions trigger the Carnot cycle. The two conditions have contrasting energetic and entropic aftereffects with consequences for mental energy. The mathematics of the Carnot and reversed Carnot cycles, which can explain recent findings in human psychology, might be constructive in the scientific endeavor in turning psychology into hard science.
Brain activity and cognition: a connection from thermodynamics and information theory
Frontiers in Psychology, 2015
The connection between brain and mind is an important scientific and philosophical question that we are still far from completely understanding. A crucial point to our work is noticing that thermodynamics provides a convenient framework to model brain activity, whereas cognition can be modeled in information-theoretical terms. In fact, several models have been proposed so far from both approaches. A second critical remark is the existence of deep theoretical connections between thermodynamics and information theory. In fact, some well-known authors claim that the laws of thermodynamics are nothing but principles in information theory. Unlike in physics or chemistry, a formalization of the relationship between information and energy is currently lacking in neuroscience. In this paper we propose a framework to connect physical brain and cognitive models by means of the theoretical connections between information theory and thermodynamics. Ultimately, this article aims at providing further insight on the formal relationship between cognition and neural activity.
2015
— The brain is both a thermodynamic system and an information processor. Cognition is described well in terms of information-based models and brain activity as a physical process, is accurately addressed via a thermodynamic approach. A connection between information theory and thermodynamics in neuroscience is currently lacking in the literature. The aim of this paper is to propose an integrative approach regarding information and energy as two related magnitudes in the brain, and to discuss the main connections between information theory and thermodynamics that may be helpful for understanding brain activity. In this sense, the link between both approaches is based on the concepts of entropy and negentropy, the Boltzmann formula, the Landauer’s Principle and the energetic cost for the observation of information proved by Szilard. This set of connections enables us to show that information and energy are two strongly related and interchangeable magnitudes in the brain with the possi...
The Thermodynamic Brain and the Evolution of Intellect: The Role of Mental Energy.
Cognitive Neurodynamics, 2020
The living state is low entropy, highly complex organization, yet it is part of the energy cycle of the environment. Due to the recurring presence of the resting state, stimulus and its response form a thermodynamic cycle of perception that can be modeled by the Carnot engine. The endothermic reversed Carnot engine relies on energy from the environment to increase entropy (i.e., the synaptic complexity of the resting state). High entropy relies on mental energy, which represents intrinsic motivation and focuses on the future. It increases freedom of action. The Carnot engine can model exothermic, negative emotional states, which direct the focus on the past. The organism dumps entropy and energy to its environment, in the form of aggravation, anxiety, criticism, and physical violence. The loss of mental energy curtails freedom of action, forming apathy, depression, mental diseases, and immune problems. Our improving intuition about the brain's intelligent computations will allow the development of new treatments for mental disease and novel find applications in robotics and artificial intelligence (AI).
Thoughts about Thinking: Cognition According to the Second Law of Thermodynamics
Advanced Studies in Biology, 2013
A holistic account of the human brain is provided by the Second Law of Thermodynamics. According to this universal precept, the central nervous system is governed by the quest to consume free energy in the least possible time. The brain is like any other system of nature that has evolved over eons and continues to develop over an individual's life time. This physical portrayal is singularly appropriate because power-law characteristics as well as oscillatory and at times unpredictable functioning are not exclusive attributes of the brain but are found in other systems throughout nature. The neural network comprises pathways for signal propagation just as other natural systems have pathways for the transmission of energy in various forms. These universalities support the view of the evolution and development of the human brain as a natural thermodynamic process. In a like manner perception, sensation and learning as well as the processes of memory, emotions and consciousness can be regarded as natural expressions of the neural network under the suzerainty of the Second Law. The outcomes of cognitive processes, like other natural processes, are non-deterministic because the interactive effects of flows of energy as signals with differences in energy as their driving forces cannot be separated from each other. This naturalistic framework also provides insight into mental disorders and cognitive defects. *arto.annila@helsinki.fi *cbeck@ualberta.ca 136 S. Varpula, A. Annila and C. Beck
THE THERMODYNAMIC ANALYSIS OF NEURAL COMPUTATION
J Neurosci Clin Res, 2018
The brain displays a low-frequency ground energy conformation, called the resting state, which is characterized by an energy/information balance via self-regulatory mechanisms. Despite the high-frequency evoked activity, e.g., the detail-oriented sensory processing of environmental data and the accumulation of information, nevertheless the brain's automatic regulation is always able to recover the resting state. Indeed, we show that the two energetic processes, activation that decreases temporal dimensionality via transient bifurcations and the ensuing brain's response, lead to complementary and symmetric procedures that satisfy the Landauer's principle. Landauer's principle, which states that information era-sure requires energy, predicts heat accumulation in the system, this means that information accumulation is correlated with increases in temperature and lead to actions that recover the resting state. We explain how brain synaptic networks frame a closed system, similar to the Carnot cycle, where the information/energy cycle accumulates energy in synaptic connections. In deep learning, representation of information might occur via the same mechanism.
The Mental Maxwell Relations: A Thermodynamic Allegory for Higher Brain Functions
Frontiers in Neuroscience
The theoretical framework of classical thermodynamics unifies vastly diverse natural phenomena and captures once-elusive effects in concrete terms. Neuroscience confronts equally varied, equally ineffable phenomena in the mental realm, but has yet to unite or to apprehend them rigorously, perhaps due to an insufficient theoretical framework. The terms for mental phenomena, the mental variables, typically used in neuroscience are overly numerous and imprecise. Unlike in thermodynamics or other branches of physics, in neuroscience, there are no core mental variables from which all others formally derive and it is unclear which variables are distinct and which overlap. This may be due to the nature of mental variables themselves. Unlike the variables of physics, perhaps they cannot be interpreted as composites of a small number of axioms. However, it is well worth exploring if they can, as that would allow more parsimonious theories of higher brain function. Here we offer a theoretical...
The evolution of brain and mind: a non-equilibrium thermodynamics approach
Ludus Vitalis Revista De Filosofia De Las Ciencias De La Vida Journal of Philosophy of Life Sciences Revue De Philosophie Des Sciences De La Vie, 2007
The evolution of human brain and mind presupposes the study of the evolution of organic life-forms and the study of the phylogeny of preconscious animal cognition. Thus this paper takes as its basis the framework of non-equilibrium thermodynamics for biological systems. According to this viewpoint, biological systems seem to violate the Second Law of Thermodynamics: organisms keep themselves alive in their highly organized states because they absorb energy from the environment and process it to produce a state of low entropy within themselves. So it can be said that biological systems are feed of or attract negative entropy in order to compensate for the increase of entropy they create when living (i.e., life is negentropic). The paper then traces the phylogeny of brain and mind from the complexity of negentropic processes in biological systems (metabolism, thermo-regulation, irritability, sensation, perception) to non-human animal mind and human consciousness.
WILL ARTIFICIAL INTELLIGENCE BECOME CONSCIOUS? CAN THERMODYNAMICS EXPLAIN THE EVOLUTION OF INTELLECT
J Math Technique, 2022
Deep neural networks (DNNs), founded on the brain's neuronal organization, can extract higher-level features from raw input. However, complex intellect via autonomous decision-making is way beyond current AI design. Here we propose an autonomous AI inspired by the thermodynamic cycle of sensory perception, operating between two information density reservoirs. Stimulus unbalances the high entropy resting-state and triggers a thermodynamic cycle. By recovering the initial conditions, self-regulation generates a response while accumulating an orthogonal, holographic potential. The resulting high-density manifold is a stable memory and experience field, which increases future freedom of action via intelligent decision-making.