Computational neuroscience - biophysical modeling of neural systems

Harrison Stratton, Jennie Si

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Only within the past few decades have we had the tools capable of probing the brain to search for the fundamental components of cognition. Modern numerical techniques coupled with the fabrication of precise electronics have allowed us to identify the very substrates of our own minds. The pioneering work of Hodgkin and Huxley provided us with the first biologically validated mathematical model describing the flow of ions across the membranes of giant squid axon. This model demonstrated the fundamental principles underlying how the electrochemical potential difference, maintained across the neuronal membrane, can serve as a medium for signal transmission. This early model has been expanded and improved to include elements not originally described through collaboration between biologists, computer scientists, physicists and mathematicians. Multi-disciplinary efforts are required to understand this system that spans multiple orders of magnitude and involves diverse cellular signaling cascades. The massive amount of data published concerning specific functionality within neural networks is currently one of the major challenges faced in neuroscience. The diverse and sometimes disparate data collected across many laboratories must be collated into the same framework before we can transition to a general theory explaining the brain. Since this broad field would typically be the subject of its own textbook, here we will focus on the fundamental physical relationships that can be used to understand biological processes in the brain.

Original languageEnglish (US)
Title of host publicationSpringer Handbook of Computational Intelligence
PublisherSpringer Berlin Heidelberg
Pages649-663
Number of pages15
ISBN (Print)9783662435052, 9783662435045
DOIs
StatePublished - Jan 1 2015

Fingerprint

Brain
Cell signaling
Membranes
Textbooks
Electronic equipment
Mathematical models
Neural networks
Fabrication
Ions
Substrates
Axons

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Stratton, H., & Si, J. (2015). Computational neuroscience - biophysical modeling of neural systems. In Springer Handbook of Computational Intelligence (pp. 649-663). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_34

Computational neuroscience - biophysical modeling of neural systems. / Stratton, Harrison; Si, Jennie.

Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. p. 649-663.

Research output: Chapter in Book/Report/Conference proceedingChapter

Stratton, H & Si, J 2015, Computational neuroscience - biophysical modeling of neural systems. in Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, pp. 649-663. https://doi.org/10.1007/978-3-662-43505-2_34
Stratton H, Si J. Computational neuroscience - biophysical modeling of neural systems. In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg. 2015. p. 649-663 https://doi.org/10.1007/978-3-662-43505-2_34
Stratton, Harrison ; Si, Jennie. / Computational neuroscience - biophysical modeling of neural systems. Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. pp. 649-663
@inbook{e5fef7fcd7a84a849868a0ede1b4f861,
title = "Computational neuroscience - biophysical modeling of neural systems",
abstract = "Only within the past few decades have we had the tools capable of probing the brain to search for the fundamental components of cognition. Modern numerical techniques coupled with the fabrication of precise electronics have allowed us to identify the very substrates of our own minds. The pioneering work of Hodgkin and Huxley provided us with the first biologically validated mathematical model describing the flow of ions across the membranes of giant squid axon. This model demonstrated the fundamental principles underlying how the electrochemical potential difference, maintained across the neuronal membrane, can serve as a medium for signal transmission. This early model has been expanded and improved to include elements not originally described through collaboration between biologists, computer scientists, physicists and mathematicians. Multi-disciplinary efforts are required to understand this system that spans multiple orders of magnitude and involves diverse cellular signaling cascades. The massive amount of data published concerning specific functionality within neural networks is currently one of the major challenges faced in neuroscience. The diverse and sometimes disparate data collected across many laboratories must be collated into the same framework before we can transition to a general theory explaining the brain. Since this broad field would typically be the subject of its own textbook, here we will focus on the fundamental physical relationships that can be used to understand biological processes in the brain.",
author = "Harrison Stratton and Jennie Si",
year = "2015",
month = "1",
day = "1",
doi = "10.1007/978-3-662-43505-2_34",
language = "English (US)",
isbn = "9783662435052",
pages = "649--663",
booktitle = "Springer Handbook of Computational Intelligence",
publisher = "Springer Berlin Heidelberg",

}

TY - CHAP

T1 - Computational neuroscience - biophysical modeling of neural systems

AU - Stratton, Harrison

AU - Si, Jennie

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Only within the past few decades have we had the tools capable of probing the brain to search for the fundamental components of cognition. Modern numerical techniques coupled with the fabrication of precise electronics have allowed us to identify the very substrates of our own minds. The pioneering work of Hodgkin and Huxley provided us with the first biologically validated mathematical model describing the flow of ions across the membranes of giant squid axon. This model demonstrated the fundamental principles underlying how the electrochemical potential difference, maintained across the neuronal membrane, can serve as a medium for signal transmission. This early model has been expanded and improved to include elements not originally described through collaboration between biologists, computer scientists, physicists and mathematicians. Multi-disciplinary efforts are required to understand this system that spans multiple orders of magnitude and involves diverse cellular signaling cascades. The massive amount of data published concerning specific functionality within neural networks is currently one of the major challenges faced in neuroscience. The diverse and sometimes disparate data collected across many laboratories must be collated into the same framework before we can transition to a general theory explaining the brain. Since this broad field would typically be the subject of its own textbook, here we will focus on the fundamental physical relationships that can be used to understand biological processes in the brain.

AB - Only within the past few decades have we had the tools capable of probing the brain to search for the fundamental components of cognition. Modern numerical techniques coupled with the fabrication of precise electronics have allowed us to identify the very substrates of our own minds. The pioneering work of Hodgkin and Huxley provided us with the first biologically validated mathematical model describing the flow of ions across the membranes of giant squid axon. This model demonstrated the fundamental principles underlying how the electrochemical potential difference, maintained across the neuronal membrane, can serve as a medium for signal transmission. This early model has been expanded and improved to include elements not originally described through collaboration between biologists, computer scientists, physicists and mathematicians. Multi-disciplinary efforts are required to understand this system that spans multiple orders of magnitude and involves diverse cellular signaling cascades. The massive amount of data published concerning specific functionality within neural networks is currently one of the major challenges faced in neuroscience. The diverse and sometimes disparate data collected across many laboratories must be collated into the same framework before we can transition to a general theory explaining the brain. Since this broad field would typically be the subject of its own textbook, here we will focus on the fundamental physical relationships that can be used to understand biological processes in the brain.

UR - http://www.scopus.com/inward/record.url?scp=84944587068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84944587068&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-43505-2_34

DO - 10.1007/978-3-662-43505-2_34

M3 - Chapter

SN - 9783662435052

SN - 9783662435045

SP - 649

EP - 663

BT - Springer Handbook of Computational Intelligence

PB - Springer Berlin Heidelberg

ER -