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Principles of Computational Modelling in Neuroscience
Abstract
The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.
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“smaller models of large networks with realistic single cell electrophysiological properties (e.g., input resistance and resting membrane potential) remain silent unless synaptic strengths or numbers are arbitrarily increased beyond the biologically relevant levels to compensate for fewer inputs to their model cells”
From Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit (2016) · cites this paper as Sterratt et al., 2011
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Stochastic phase-change neurons
Tůma, T., Pantazi, A., Gallo, M. L., Sebastian, A., & Eleftheriou, E. S. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,107
Sterling, P., & Laughlin, S. (2015) · doi:10.7551/mitpress/9780262028707.001.0001 · Cited by 482
Bezaire, M. J., Raikov, I., Burk, K., Vyas, D., & Soltesz, I. (2016) · eLife · doi:10.7554/elife.18566 · Cited by 198
“DOI: 10.7554/eLife.18566 1 of 106 for fewer inputs to their model cells (e.g., [Dyhrfjeld-Johnsen et al., 2007; Sterratt et al., 2011]).”
BackgroundInfluentialThe Scientific Case for Brain Simulations
Einevoll, G. T., Alain Destexhe, A., Diesmann, M., Grün, S., Jirsa, V., de Kamps, M., Migliore, M., Ness, T. V., Pleßer, H. E., & Schürmann, F. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.027 · Cited by 192
“Starting with the seminal work of Hodgkin andHuxley (1952), who developed amechanisticmodel for action-potential generation and propagation in squid giant axons, biophysics-basedmodeling of neurons is nowwell estab- lished (Koch, 1999; Dayan and Abbott, 2001; Sterratt et al., 2011).”
BackgroundSingle-layer graphene modulates neuronal communication and augments membrane ion currents
Pampaloni, N. P., Lottner, M., Giugliano, M., Matruglio, A., D’Amico, F., Prato, M., Garrido, J. A., Ballerini, L., & Scaini, D. (2018) · Nature Nanotechnology · doi:10.1038/s41565-018-0163-6 · Cited by 162
“In fact, the Nernst equilibrium 247 potential depends on the K concentrations outside and inside the membranes 248 [35].”
Metadata
- Authors
- Sterratt, D., Graham, B., Gillies, A., & Willshaw, D.
- Affiliations
- University of Edinburgh (GB)
- University of Stirling (GB)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2011-06-30
- OpenAlex
- W1481371813
- Cited by
- 320
- References
- 0
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 16 · 2023: 16 · 2022: 23 · 2021: 21 · 2020: 33 · 2019: 50 · 2018: 30 · 2017: 32 · 2016: 20 · 2015: 28 · 2014: 25 · 2013: 11 · 2012: 4
- FWCI
- 5.05
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Education › Undergraduate Neuroscience Education and Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- computational neuroscience, neuron models, neural circuits, ion channels, neuronal morphology, synapses, intracellular signaling, neuronal networks, model abstraction, brain development, circuit modeling
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