The DOI matches its Crossref record.
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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Items 301–316 of 316
A brief introduction to the brain
Yu, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-98829-2.00012-8
Darbas, M., Lohrengel, S., & Sulis, B. (2025) · Mathematical Modelling of Natural Phenomena · doi:10.1051/mmnp/2025001
“This model could be generalized to spatially extended neurons by considering the cable equation or more general reaction-diffusion systems (see e.g. [ 38]), still resolved at the scale of a few neurons, resulting in much heavier computations.”
Викторович, С. С. (2024) · Izvestiya of Saratov University Physics · doi:10.18500/1817-3020-2024-24-2-138-149
“…образования внеклеточного матрикса мозга, протеаз и рецепторов в зависимо-сти от активности нейронов; H ECM , P , R сигмовид-ные функции активации [39] для концентраций внеклеточного матрикса мозга, протеаз и рецепто-ров можно записать в следующем виде: Значения параметров в уравнениях (5) и (6)…”
Implementation of Hierarchically Coupled Tiny Network of Neurons to Discriminate OdorsPreprint
Ramachandran, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4895632
Model of the HVC neural network as a song motor in zebra finch
Xia, P., & Abarbanel, H. D. I. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1417558
Implementation of hierarchically coupled tiny network of neurons to discriminate odors
Sunitha., R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127793
Computational Modelling of Synaptic Plasticity in the Dentate Gyrus Granule Cell
Hananeia, N. (2015) · Otago University Research Archive (University of Otago)
Investigation on uncertainty and sensitivity analysis of complex systems
Zhu, Y. (2017) · theses.fr (ABES) · doi:10.70675/f299841cz2bbfz4ba4z8f63z089263cdc31a
“They have been diffusely utilized in many disciplines as diverse as mathematics [37, 38], physics [24, 25, 39–42], chemistry [43], etc.”
MethodUnifying experimental heterogeneity in a geometrical synaptic plasticity model
Rodrigues, Y. E. (2021) · theses.fr (ABES) · doi:10.70675/62ca5e06zd2c0z4133za51ez04d3f2ee6023
Bouté, J. (2023) · theses.fr (ABES) · doi:10.70675/966a40d5za9f3z4912zb2e7z569a1ee3f16d
Barone, S., Gallo, L., & Maggioli, F. (2026) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise70044.2026.11607606
“This strengthens how groups of neurons work together while stopping unrelated groups from working together, creating a competitive environment [8].”
Container-based framework for large-scale spiking network simulation
James, O., & Hong, S. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1893064
“Over the past decades, advances in computational neuroscience have enabled increasingly sophisticated models that capture neuronal activity across multiple spatial and temporal scales (Dayan and Abbott, 2001; Gerstner et al., 2014; Koch and Segev, 1998; Sterratt et al., 2011).”
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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