Estimating the energy of dissipative neural systems

J 2024

Estimating the energy of dissipative neural systems

FAGERHOLM, Erik Daniel, Robert LEECH, Federico E TURKHEIMER, Gregory SCOTT, Milan BRÁZDIL et. al.

Basic information

Original name

Estimating the energy of dissipative neural systems

Authors

FAGERHOLM, Erik Daniel, Robert LEECH, Federico E TURKHEIMER, Gregory SCOTT and Milan BRÁZDIL

Edition

COGNITIVE NEURODYNAMICS, DORDRECHT, SPRINGER, 2024, 1871-4080

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30210 Clinical neurology

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.700 in 2022

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1007/s11571-024-10166-1

UT WoS

001302327300001

Keywords in English

Computational neuroscience; Neural energy

Abstract

V originále

There is, at present, a lack of consensus regarding precisely what is meant by the term 'energy' across the sub-disciplines of neuroscience. Definitions range from deficits in the rate of glucose metabolism in consciousness research to regional changes in neuronal activity in cognitive neuroscience. In computational neuroscience virtually all models define the energy of neuronal regions as a quantity that is in a continual process of dissipation to its surroundings. This, however, is at odds with the definition of energy used across all sub-disciplines of physics: a quantity that does not change as a dynamical system evolves in time. Here, we bridge this gap between the dissipative models used in computational neuroscience and the energy-conserving models of physics using a mathematical technique first proposed in the context of fluid dynamics. We go on to derive an expression for the energy of the linear time-invariant (LTI) state space equation. We then use resting-state fMRI data obtained from the human connectome project to show that LTI energy is associated with glucose uptake metabolism. Our hope is that this work paves the way for an increased understanding of energy in the brain, from both a theoretical as well as an experimental perspective.

Citovat

FAGERHOLM, Erik Daniel, Robert LEECH, Federico E TURKHEIMER, Gregory SCOTT and Milan BRÁZDIL. Estimating the energy of dissipative neural systems. COGNITIVE NEURODYNAMICS. DORDRECHT: SPRINGER, 2024, 8 pp. ISSN 1871-4080. Available from: https://dx.doi.org/10.1007/s11571-024-10166-1.

@article{2436118,
   author = {Fagerholm, Erik Daniel and Leech, Robert and Turkheimer, Federico E and Scott, Gregory and Brázdil, Milan},
   article_location = {DORDRECHT},
   doi = {http://dx.doi.org/10.1007/s11571-024-10166-1},
   keywords = {Computational neuroscience; Neural energy},
   language = {eng},
   issn = {1871-4080},
   journal = {COGNITIVE NEURODYNAMICS},
   title = {Estimating the energy of dissipative neural systems},
   url = {https://link.springer.com/article/10.1007/s11571-024-10166-1},
   year = {2024}
}

TY  - JOUR
ID  - 2436118
AU  - Fagerholm, Erik Daniel - Leech, Robert - Turkheimer, Federico E - Scott, Gregory - Brázdil, Milan
PY  - 2024
TI  - Estimating the energy of dissipative neural systems
JF  - COGNITIVE NEURODYNAMICS
PB  - SPRINGER
SN  - 18714080
KW  - Computational neuroscience
KW  - Neural energy
UR  - https://link.springer.com/article/10.1007/s11571-024-10166-1
N2  - There is, at present, a lack of consensus regarding precisely what is meant by the term 'energy' across the sub-disciplines of neuroscience. Definitions range from deficits in the rate of glucose metabolism in consciousness research to regional changes in neuronal activity in cognitive neuroscience. In computational neuroscience virtually all models define the energy of neuronal regions as a quantity that is in a continual process of dissipation to its surroundings. This, however, is at odds with the definition of energy used across all sub-disciplines of physics: a quantity that does not change as a dynamical system evolves in time. Here, we bridge this gap between the dissipative models used in computational neuroscience and the energy-conserving models of physics using a mathematical technique first proposed in the context of fluid dynamics. We go on to derive an expression for the energy of the linear time-invariant (LTI) state space equation. We then use resting-state fMRI data obtained from the human connectome project to show that LTI energy is associated with glucose uptake metabolism. Our hope is that this work paves the way for an increased understanding of energy in the brain, from both a theoretical as well as an experimental perspective.
ER  -

FAGERHOLM, Erik Daniel, Robert LEECH, Federico E TURKHEIMER, Gregory SCOTT and Milan BRÁZDIL. Estimating the energy of dissipative neural systems. \textit{COGNITIVE NEURODYNAMICS}. DORDRECHT: SPRINGER, 2024, 8 pp. ISSN~1871-4080. Available from: https://dx.doi.org/10.1007/s11571-024-10166-1.

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