BRAIN RESTING STATE, LANDAUER PRINCIPLE AND CARNOT CYCLE
Eva Déli
Institute for Consciousness Research
Nyiregyhaza
Benczur ter 9, Hungary
eva.kdeli@gmail.com
James F. Peters
Department of Electrical and Computer Engineering, University of Manitoba
75A Chancellor’s Circle, Winnipeg, MB R3T 5V6 CANADAand
Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey,
Department of Mathematics, Faculty of Arts and Sciences, Adıyaman University
02040 Adıyaman, Turkey and Computational Intelligence Laboratory, University of
Manitoba, WPG, MB, R3T 5V6, Canada
James.Peters3@umanitoba.ca
Arturo Tozzi
Center for Nonlinear Science, University of North Texas,
1155 Union Circle, #311427, Denton, TX 76203-5017, USAtozziarturo@libero.it

Abstract
The brain displays a low-frequency ground energy conformation, called the resting state, which is characterized byenergy/information balance via self-regulatory mechanisms. Despite the high-frequency evoked activity accumulates
information from the detail-oriented sensory processing of environmental data, nevertheless the brain’s automatic
regulation is always able to recover the resting state. Indeed, we show that the two energetic processes arecomplementary and symmetric: while activation decreases temporal dimensionality via transient bifurcations, theensuing brain’s response leads to procedures that satisfy the Landauer’s principle. Landauer’s principle, which statesthat information erasure requires energy, predicts heat accumulation in the system: this means that information
accumulation is correlated with increases in temperature and with 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 as energy of synaptic connections, and then dissipates in the resting state. In deep learning, representationof information might occur via the same mechanism.

Key words:
Energy/information exchange, information erasure, entropy, Landauer’s principle, Carnot cycle, deep learning.

INTRODUCTION
The cortical brain is an evolutionary marvel which interacts with the outside world via self-regulation, based on its
resting or ground state. The resting state, disturbed during stimulus and sensory processing, is recovered by automatic
operations. In warm-blooded animals, the brain’s energy is greatly increased: although the human brain represents
only 2% of the body weight, it receives 15% of the cardiac output, 20% of total body oxygen consumption and 25%of total body glucose utilization (Clark and Sokoloff, 1999). It is thought that the baseline brain energy consumption
has been almost exclusively dedicated to neuronal signaling (Shulman et al., 2009). This almost constant and hugeenergy use maintains the brain’s alertness, even during sleep, by an active balance between inhibitory and excitatory1

neurons (Dehghani et al., 2016; Okun and Lampl, 2009). The slightest variations in excitation determine whether aspike is generated. Such a delicate balance of excitatory and inhibitory neurons turns the resting state into a highly
energy-requiring state (Gruenert et al, 2011; Kim and Fiorillo, 2017; Vanag et al, 2011). Even in the absence ofstimulus, the equilibrium of excitatory and inhibitory neurons produces seemingly arbitrary bursts of resting state’s
activation (Luczak et al., 2015). This active balance between contrasting influences ensures the brain’s ability for
rapid, targeted response to environmental stimuli. For example, changes in inhibitory neurons increase brain
frequencies and their energetic needs (Gruenert et al, 2011). Via the sensory system, the brain partakes in theenergy/information exchange with the environment: the precarious energy balance turns interactions into a closed
physical process: the thermodynamic entropy.
Entropy is a concept rooted in experience and experimental data. Different scientific fields (such as information theoryor quantum mechanics) have their own, although related, definitions (Duncan & Semura, 2004; Martin et al., 2013),
with a significant amount of disagreement over diverse definitions of information and entropy. To make just a few
examples, it has been recently suggested that the negative work cost of erasure stands for negative entropy (del Rio etal., 2011); furthermore, entropy might drive order-increasing interactions, in closed systems under special
circumstances (Haji-Akbari et al., 2009; Haji-Akbari et al., 2011; Bertalanffy, 1933).
The brain’s resting state allows stimuli-related energy changes, based on the laws of thermodynamics. Sensory
activation is coupled to information increase via enhanced frequencies (Yamada and Kawabe, 2011), so that the brain’s
energy/information cycle might form a closed, thermodynamic system, similar to the Carnot cycle. To gain better
insight into the nature of entropy, here we investigate the role of stimulus during the entropic changes occurring duringcognitive process.
Landauer’s principle (i.e., the erasure of information produces a minimal quantity of heat, proportional to the thermalenergy) (Landauer, 1961), shows that a certain amount of work is necessary to erase a bit of information, dissipatedas heat to the environment. The needed amount of work couples with the function of uncertainty about the system,
because information about the system reduces the cost of erasure. Likewise, in the brain, the information value of
stimuli, which determines the extent of response, is highly subjective. For example, the correlation of neuronal
activation with visual stimulus is mediated by intracortical mechanisms (Doiron et al, 2016). Furthermore, theoscillation dynamics of local neurons reflect the anatomical/functional occurrence of intra-cortical feedback,thalamocortical networks, global signal propagation, synaptic clustering, network states and other residual activities
well beyond the sensory value of inputs (Wright et al., 2016). Thus, the connection between ‘information’ and ‘work’is the function of environmental entropy. Thus, applying Landauer’s principle to the brain’s cognitive process might
uncover the thermodynamic nature of the energy/information exchange during the sensory process.
One of the biggest puzzles in neuroscience is how comprehension and meaning emerge from the complex patterns ofneural activity. Hemodynamic, metabolic parameters, membrane potential, electric spikes, neurotransmitter release
have been studied (Gazzaniga, 2009; Kavalali et al. 2011; O‘Donnell and Van Rossum 2014). EEG, lesion studies(Buzsaki and Watson, 2012; Jensen et al., 2014), diffuse tensor imaging, MEG and fMRI analysis (Friston, 2010;
Sporns, 2013; Tozzi, 2015) demonstrate that consciousness results from coherent, globally coordinated electricactivities (Koch et al., 2016; Touboul, 2012). Since electric activities in the brain is correlated with conscious actions,the mind forms a unified experience by connecting sensory perception with mental states based on event related
potentials (Guterstam et al., 2015; Mancini et al., 2011). An ensemble of cortical and subcortical regions (including
the rostral anterior cingulate, precuneus, posterior cingulate cortex, posterior parietal cortex, dorsal prefrontal cortex,
along with the caudate head, anterior claustrum and posterior thalamus) supports the occurrence and convergence ofmultiple resting-state networks (Bell and Shine 2015). Resting-state network convergence might facilitate global brain
communication and underpinning systems-level integration in the human brain (Northoff et al., 2010). Local
electromagnetic potential differences in the complex neural landscape vary according to the principle of least action,
forming metastable changes of spatiotemporal patterns that are isomorphic with cognitive and phenomenal
occurrences (Fingelkurts et al., 2009; 2013). Via recurring, highly reproducible harmonic functions (Atasoy et al.,2016) the brain’s resting state has been shown to form the functional curvature of a four dimensional torus, which
manifests itself as repetitive patterns of mental trajectories (Tozzi and Peters, 2016a,b; Tozzi et al., 2016). Even in theabsence of synaptic anatomical connectivity, the resting state displays great autonomy and complexity, indicating its
essential role as the ground state of the brain (Robinson et al., 2016).
Biological organisms live with a temporal expiration date, determined by the access to air, water, food, mating, which
modulates behavior between urgency and relaxation. The inner clock of brain activity is not connected to any local
system, but results from a global temporal rhythm (Teki, 2016), which originate in midbrain dopamine neurons (Soares
et al., 2016). For example, sleep compresses the daytime firings of neurons in time (Watson & Buzsáki, 2015). In fact,time turns into a relative experience, because intentional binding formulates the perception of time (Jo et al., 2014)
and gives rise to agency. The importance of temporal considerations is further illustrated by the connection between

2deviations in time perceptions and brain diseases (Kranick & Hallett, 2013). Major depression seems to slow timeperception (Stanghellini et al., 2016), whereas time passes at a jerky in schizophrenia. Ticks and Parkinson’s disease
is characterized by both impairment in time control as well as time delay. Therefore, time perception, an elementarypart of the formation of conscious experience, might be regulated by the energy/information cycle of the brain.
Understanding the brain’s energy/information exchange is critical, because disturbances in this cycle can cause healthproblems and disease without any obvious pathologic change in brain morphology.
The gradually improving responses to stimuli have molded brain organization into a coherent, structural,organizational mirror of physical systems (Déli, 2015). However, biological survival needs, such as water, food,
reproduction, etc. to be dictated by a relativity of time and an oscillating motivation between urgency and relaxation.
Mental operations, which form an increasingly improving mirror of the social environment, constantly respond to theenvironment and modify it, forming their common evolution. This way, environmental abstraction becomes integral
part of a wholesome mental world, engendering an interconnected system which determines and governs thoughts,
intentions and behavior according to the laws of physics.

Discussion
Theoretical foundations
Stimuli provide information input that leads to a thermodynamic relationship with the environment, in the form of theelectric activities of the neuronal network. Because all evoked oscillations are greater than electric activities of theresting state and accompanied by entropic and dimensionality changes, the brain’s information/energy cycle between
stimulus and recovery of the resting state can be analyzed based on thermodynamics. Stimulus generates heat that canbe assessed in terms of enhanced brain frequencies, synaptic modification. That way, the brain’s responses to stimulus
in the present state shapes its ability for future interactions. The energy devoted to neuronal connections satisfies theLandauer’s principle, which reflects sensory interaction based on energy/information exchange. Evoked states are
information-saturated, thus produce high entropy and involve high frequencies; these high frequencies are unstable
and, due to their huge energy requirement, become energetically untenable over time.
Because the sensory system transforms spatial information into the temporal language of the brain oscillations, thebrain is organized along temporal coordinates (Buzsaki et al, 2013, Fingelkurts & Fingelkurts, 2014). Here we analyze
the brain’s global energy/information cycle in terms of entropic and topological considerations, in view of thecorrelation between physiology and electric activities. We achieve generalizations that allow the assessment of therelationship of the brain’s electric activities--independent of their scale, magnitude, specific features and local
boundaries—and cognitive experiences. We show that interconnected synaptic networks form a closed system, whichleads to self-regulation based on energy/information transformations satisfying the Landauer’s principle. Information
accumulation corresponds to an increase in temperature indicated by enhanced frequencies, so that erasing information
frees energy, which increases order through the energy accumulated in cortical synaptic connections. Therefore, selfregulation of brain activities is based on low entropy.
Brain activities do not exist in isolation; rather from a self-regulating, interconnected and coherent energy system.
Moreover, brain models suggest that constantly changing nervous activities take place on different kinds of curvaturesof the brain phase space (see, for example, Sengupta et al., 2016). Thus, brain activity can be assessed based on
topological activity. Topology suggests structures such as a toroidal view of brain activities and the projection ofpercepts to higher dimensional spaces (Tozzi and Peters, 2016a) as well as mappings of antipodal points on opposite
perceived images into hyperbolic manifolds (Peters, 2016a). Because disturbances in this self-regulating physicalnetwork might play a role in various pathologic conditions, we will take into account the relationship between temporaldimensionality and entropy, and the brain’s evoked energy/information exchange (interaction).

Relationship to the Carnot cycle
The Carnot cycle provides an upper limit on the efficiency that any classical thermodynamic engine can achieve during
the conversion of heat into work. No engine operating between two heat reservoirs can be more efficient than thecorresponding Carnot engine. The Carnot cycle is an idealization, since no classical engine is reversible and physicalprocesses involve some increase in entropy. Surprisingly, the Carnot bound can be surpassed in microscopic systems,
because thermal fluctuations induce transient decreases of entropy, allowing for possible violations of the Carnot limit.
Such a miniaturized Carnot engine was realized using a single Brownian particle (Martinez et al., 2016). The Carnot

3cycle represents execution of work in a heat bath. The process consists of two isothermal processes, where the working
substance is respectively in contact with thermal baths at different temperatures Th and Tc , connected by two adiabatic
processes, where the substance is isolated and heat is neither delivered, nor absorbed (Shangi et al., 2014).
In the brain, neural activation is forwarded via synaptic connections, allowing the analysis of energy/ informationexchange based on the principle of least action. We suggest that the nervous entropic changes in information andresting state display a thermodynamic relationship with the Carnot cycle. The cortical insulation of the limbic system,
which lies on the top of the resting state, correlates with the occurrence of evoked potential. In turn, evoked potential
triggers electric activities that restore the resting state (Figure 1). This recurrent energy /information exchange forms
a closed system and allows the examination of brain activity based on thermodynamic considerations. Landauer’s
principle shows how heat production is tied to information/energy transformations. The frequencies of the brain’s
evoked cycle reflect both its computational limit and the added heat of stimulus, which also determines the energy
usage via synaptic changes.

Figure 1. Changes in energy balance due to stimulus. The brain frequencies change from high, on the left (#1), tolow, toward the right (#3), determining the direction of information flow in the brain (shown by thin line). The restingstate of the brain is energy neutral before stimulus (#1) and after a response (#3). Evoked potential are illustrated by
2. The high energy need of enhanced brain frequencies induces an urgency (indicated by 1), whereas the highamplitude of the lowest frequencies permits an overarching, global connectome and mental vision, indicated by 3.
The brain’s energy/information cycle:
In the brain, stimuli increase frequencies, which correlate with heat absorbed by the system, giving rise to an increasein temperature (Figure 2). This phase, marked DA in the picture, correspond to the Carnot cycle’s adiabatic
compression phase. In a classical gas, heat production is function of particles collisions, which is dependent on thetemperature and the number of particles (volume of the gas). In neural networks as well as in deep learning systems,
network connections, which spread information, correlate with collisions. Therefore, number of dimensions
corresponds to the volume of the gas, whereas the depth of the network might represent the temperature of the system.
The second phase, marked by AB, in which the frequencies spread throughout the whole cortex, stands for theisothermal expansion (the signal expands, exerting work). The third, relaxation phase, marked by BC, stands in thebrain for evoked potentials reversing the direction of the electric flow on lower frequencies; the reduction oftemperature exerts work, by accumulating energy as the complexity of synaptic connections. In this phase, meanings
and concepts might emerge, based on the subjective information value of the stimulus. The fourth phase, marked byCD, correlates with the contraction phase of the cycle: the brain’s electric activities contract and recover the restingstate.

The amount of thermal energy transferred in the process is the following:

4

=∫

(1)

The area inside the cycle will then be the amount of work done by the system over the cycle.
W= ∮

= ∮(

−

) = ∮(

−

)=∮

−∮

=∮

Since dU is an exact differential, its integral over any closed loop is zero: it follows that the area inside the loop on a
T-S diagram is equal to the total work performed
The amount of energy transferred as work is
W= ∮=∮= ( − )( − )The total amount of thermal energy transferred from the hot reservoir to the system will be
The efficiency ƞ is defined to be:
ƞ=
=1−

=

(

−

)

where

W is the work done by the system (energy exiting the system as work),is the heat taken from the system (heat energy leaving the system),is the heat put into the system (heat energy entering the system),is the absolute temperature of the cold reservoir, andis the absolute temperature of the hot reservoir.
is the maximum system entropy
is the minimum system entropy
The heat absorbed by the system toward order increasing transformation:dQ = T dS
Therefore, the amount of released heat corresponds to the entropy decrease (increasing synaptic complexity) of thesystem.

5

Figure 2: A Carnot cycle acting as a heat engine, illustrated on a temperature-entropy diagram. The cycle takes place
between a hot reservoir at temperature TH and a cold reservoir at temperature TC. The vertical axis displays thetemperature, whereas the horizontal axis the entropy. In the brain, during the (DA)compression phase, the stimulus
increases the frequencies,due to heat absorbed by the system. In neural networks as well as in deep learning systems
the depth and number of connections of the network is related to the system’s ability to produce work or tolearning.(AB):the frequencies spread throughout the whole cortex: the signal expands, exerting work. (BC): evoked
potentials reverse the direction of the electric flow on lower frequencies (the temperature decreases, the energy leaves
the system). CD: the electric activities contract and return to the resting state.

The brain’s energy/information cycle determines the temporal dimensionality of electric activities inunderstanding, comprehension and learning
Landauer’s principle, originally developed for computation, was proven in increasingly sophisticated experiments andconsequently, applicable for the tightly regulated environment of the brain (Bérut et al 2012; Hong et al., 2016;
Landauer, 1961). According to the principle, information accumulation by electric activities of the brain during evokedstates corresponds to enhanced temperature. Landauer’s principle also shows that the recovery of the resting state─frees energy for work, due to information/energy transformation. This process correlates with lower temperature.
Decreasing frequencies form a sink of low information content and increasing degrees of freedom (Tozzi et al, 2017).
Thus, evoked activities modify the temporal field curvature and dimensionality via bifurcations in the brain’s electricactivities, whereas the resting, equilibrium state formulate Euclidean torus (Tozzi and Peters, 2016a). In contrast,
decreasing frequencies increase the oscillations amplitude (da Silva et al 2016), thus activating greater brain regions,or modules. Simultaneous activation of wider regions of the brain might engender mental coherence, ideas andlearning. The potential assessment of neuronal activations and global cognitive processes in terms of brain energy
cycle has immense potential in understanding various neural conditions, such as depression, PTSD and other neuraldiseases.

Relationship between the resting state and learning
Sensory interaction increases information content and entropy, but the living brain incessantly recovers its resting, lowentropy state. Maintaining and restoring the resting state seems to be an inherent and essential part of healthy brain
operation. Spontaneous brain activity, accounts for up to 70% of the great energy need of the brain (Tomasi et al.,62013). Information input enhances frequencies and compresses time (by forming positive temporal curvature), which
increases entropy, similar to a Brownian motion. In accordance with Landauer’s principle, heat must be released when
the resting state is restored (Pop-Jordanova and Pop-Jordanov, 2005). Surprisingly, deep learning can also be divided
into phases that constitute similar entropic effects and energetic transformations, consisting of compression of
information and relaxation, which culminates in representation of information (Shwartz-Ziv and Tishby, 2017; Gao &Duan, 2017). This entropy conservation in closed systems is expressed by the low entropy principle (Déli, 2017).

Entropic considerations of the brain’s topology and activities: a mathematical treatment
Brain activities might follow particle paths traversing surfaces of geometric spaces formed by temporal rhythms ofelectric activities (Tozzi and Peters, 2017). Thus, electric activities might manifest as dimensional structures, thatchange from n-dimensional (e.g., a two-dimensional disk), to n+1 -dimensional structure (e.g., negative curvaturesurface, which correspond to temporal expansion) and n-1 dimensional surface (positive temporal curvature surface,which correspond to temporal compression).Since the energy cost of sensory processing is proportional to frequencies,
sensory information is translated into an energy signal in the brain. The sensory transmission toward the sensory cortex
via fast oscillations, together with response via slow oscillations, form the polarity effects of the brain's
electromagnetic flows. Nevertheless, the strength and extent of synaptic connections (i.e. increasing synaptic
complexity) differ before and after stimulus (Figure 3). The enhanced neural organization must be proportional withthe release of thermal energy and consequent reduction of entropy. Therefore, stimulus, which increases temperature,might supply the energy to modulate synaptic strength or form new connections in the brain. The organizational
constancy and charge neutrality of the resting state guarantees that the energy difference due to the released heat (asin a heat engine) leads to a higher order of the synaptic organization. Therefore, the brain is an active member of theenvironmental energy cycle.
A topological view of this synergy between the brain and the environmental energy cycles exhibits strong proximity
both spatially and descriptively. The spatial strong proximity (Peters, 2016a) of the brain and environment is in theform of temporally overlapping cerebral energy and environmental energy readings (e.g., the occurrence energy highs
and lows of the one set of readings overlaps with the occurrence similar readings of highs and lows in the other during
the same timeframe). For example, let A and B represent nonempty sets of recordings of brain and environmentalw

energy recordings that are temporally concomitant in situ. The spatial form of strong proximity is denoted by d .
From the strong proximity axiom (Peters, 2016b, p. 16), we havew

A d B implies A Ç B ¹ Æ,i.e., the two sets of energy readings overlap. There is also a descriptively strong proximity between the two sets ofw

energy readings. The descriptive form of strong proximity is denoted by d J . The subscript J refers to a mapping

J:A®

n

on the set of readings A into an n-dimensional feature spacen, defined by J ( x ) (a feature vector, ) that is a feature value of a reading x Î A. Similarly, there is a mapping J : B ® n on the readings in B
into the feature space n . The two sets of energy readings with common descriptions give rise to a descriptive
intersection (denoted by Ç ) of the two sets:
n

in

J

A Ç B = { x Î A È B : J ( x ) Î J ( A ) & J ( x ) Î J ( B )} ,Ji.e., there is at least one cortical energy reading (e.g., cortical energy amplitude) in A that has the same description as
an environmental energy reading (e.g., environmental energy amplitude) in B. From these structures, we can elicit astrong descriptive proximity between A and B (Peters, 2016b, p. 28-29) in terms ofA Ç B ¹ Æ implies A Ç B ¹ Æ,Ji.e., overlapping energy readings implies an overlap between the descriptions of the two sets of readings.
observation leads to the following descriptive proximity property:w

A dJ B implies A Ç B ¹ Æ.
J

7

This

The importance of this pair of proximities lies in the fact it is then possible to derive Leader uniform topologies onpairs of sets of energy readings. This is done in the following manner. Start with a collection of sets of cerebral
energy readings U Ai and a collection environmental energy readings U Bi gathered during withing the samew

timeframe. For each given subset A ÎU Ai , find all B ÎU Bi , such that A d J B. Doing this for each given setof cerebral energy readings, leads to a collection of clusters of strongly near sets of readings. In effect, we have
topologized the collections of energy readings by introducing a Leader uniform topology (Leader, 1959; Peters, 2015)
on the space of energy readings.
An important outcome of topologized energy readings is the introduction of a search space in which clusters ofreadings exhibit spatial and descriptive proximities.
Moreover, our approach shows the role of observer in information processing: the history and current state of the brain
is just as important in determining the degree of neuronal activation as the stimulus itself. Thus, the state of theobserver i.e. the brain, determines the information value of the stimulus. This way, information processing in the brain
is holographic. Thus, the degree of comprehension or understanding (manifested as increasing synaptic complexity)
has a large subjective quality, the observer effect.

Figure 3. The entropic changes during a learning cycle. (A) Sensory information activates the neural system byenhancing the frequency of oscillations, which corresponds to changes in temperature. The increase in frequencies
leads to positive temporal curvature, which compresses time perception. The entropy also increases between S1 to
S2 . (B) The second part of the cycle recovers the low entropy state and the Euclidean geometry. The slowing
frequencies correspond to negative temporal curvature and to increases in time perception, which slows down thesystem. (A) The entropy decrease leads to order, manifested as synaptic complexity, underpinning concepts andmeaning. Therefore, the energy increase between A and A, is supplied by sensory stimulus. Or in other words,
perception of stimulus modifies mental organization.

8

Conclusions
Physical principles often find application in other, seemingly unrelated fields, proving that the physical laws underline
all of nature. The brain’s immense energy use engenders a readiness on which interaction with the environment ispossible. Here, in our investigation of brain processes, we analyzed the resting state in light of two important physicalprinciples, the Carnot cycle and Landauer’s principle. We traced back the brain electromagnetic activities onto theseprinciples based on the principle of least action.
The brain’s fundamental regulation is founded on field effects of the resting state; in its constant interaction with theenvironment, the mind constantly changes and adapts. However, the maintenance of the resting state against theconstant barrage of sensory activation by the environment contributes to the brain’s immense energy consumption.
Sensory activation corresponds to information/entropy increase and the reduction of temporal dimensionality (Tozzi
and Peters, 2016a). Landauer’s principle in the brain means that evoked activities correspond to enhanced‘temperature,’ whereas recovery of the resting state correlates with cooling down (Pop-Jordanova and Pop-Jordanov,2005) and the initial and final conditions differ in neural organization. The incurred thermal energy might enhanceneural organization, which engenders meaning, mental coherence and correlates with low entropy. Therefore, stimulus,
which generates activation, might supply the energy to modulate synaptic strength or form new connections in thebrain. Thus, Landauer’s principle might explain the relationship between the electric activities of the brain and thephysiological states of the mind. Two sides of the brain energy/information cycle are bound by the organizational
constancy and charge neutrality of the resting state. The brain as well as deep learning systems maintain a permanentreadiness state, on which energy/information cycle with the environment can take shape. Therefore, both in the brain
and in deep learning systems, Maxwell’s demon can reverse the second law of thermodynamics. The existence of thestable resting state turns the brain into a globally static system. In this way, the brain’s self-regulation utilizes theinformation input delivered by the environment, leading to an increase of its neural organization (mental capacity,
corresponding to greater synaptic complexity). Thus, the brain is part of the thermodynamic cycle of the environment.
In a highly interconnected relationship, the brain modifies its environment, but stimulus also evolves and changes thebrain. A connection between life processes and the entropic changes of the environment was suggested by Schrodinger(1945) and was further developed by Déli (2015). We have shown that the constant barrage of information by theenvironment induces a thermodynamic cycle in the brain, similar to the Carnot cycle, which increases synapticorganization and complexity, i.e. order. The ability to transform information into energy seems to be the most essential
quality of the brain. However, the brain is compelled by comprehension and interpretation of visual, auditory, tactile
experience or taste by the stimulus. The brain does not engage the environment. Instead, the environment highjacks
the brain, by forcing it to process incoming information (Déli, 2015; Déli et al., 2017). Furthermore, the environment
charges the brain with sensory information: instead of the brain having control over the environment, the environment
provokes and extorts the brain’s energy consumption needs. Feeding on complex organic compounds reduces theenvironment’s entropy. Thus, evolution of life is the environment’s convenient way to reduce entropy. The brain’s
immense energy use during its resting state ensures that very tiny changes in potential can trigger considerable
activation and order increasing transformations. Thereby, thinking, memory and other conscious processes require
minute computational costs. This permits us to uncover how energy/information transformations in the brain satisfy
physical principles.

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