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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cited by
Sorted by citations
Items 201–300 of 316
Signal Processing Challenges in Biohybrid System Design
Babić, Z. (2018) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito.2018.8748714 · Cited by 1
“116 result of the difference in experimental settings, a huge amount of different, sometimes contradicting models with different level of analysis and level of details raised since the beginning of the past century [17-20].”
BackgroundMethodPrecision annealing Monte Carlo methods for statistical data assimilation and machine learning
Fang, Z., Wong, A. S., Hao, K., Ty, A. J. A., & Abarbanel, H. D. I. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.013050 · Cited by 1
Review on Modelling Aspect of Ryanodine Receptor in CA1 Neuron
Sonupriya, & Dave, V. (2020) · International Journal of Engineering Research and · doi:10.17577/ijertv9is020083 · Cited by 1
“pyramidal neuron, ryanodine receptor are present at higher level, which is activated through calcium influx from VGCC, followed by calcium induced calcium release(CICR), which dominate in CA1 neuron for ryanodine receptor[42], so there is link between AHP and calcium release from Ryanodine receptor.”
Information Processing in Extended Hodgkin-Huxley Neuron Model
-Devi, M. G., Choudhary, D., & Garg, A. R. (2020) · doi:10.1109/icetce48199.2020.9091733 · Cited by 1
Frigg, R., & Nguyen, J. (2020) · Synthese Library/Synthese library · doi:10.1007/978-3-030-45153-0_8 · Cited by 1
The Nernst Equilibrium Potential
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.010 · Cited by 1
Bhattacharya, B. S., Cochran, A. L., & Érdi, P. (2017) · Springer series in bio-/neuroinformatics · doi:10.1007/978-3-319-49959-8_1 · Cited by 1
Старков, С. О., & Lavrenkov, Y. N. (2019) · Izvestiya Wysshikh Uchebnykh Zawedeniy Yadernaya Energetika · doi:10.26583/npe.2019.1.13 · Cited by 1
Optimizations for Neuron Time Warp(NTW) for stochastic reaction-diffusion models of neurons
Nazrul, M. N. I., Tropper, C., McDougal, R. A., et al. (2017) · Winter Simulation Conference · doi:10.1109/wsc.2017.8247871 · Cited by 1
Absolute Stability and Synchronization in Second-Order Neural Fields with Conduction Delays
Shih, C.-W., Tseng, J.-P., & Wu, C.-H. (2023) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/21m1441109 · Cited by 1
Analytical solutions for the short-term plasticity
Protachevicz, P. R., Batista, A. M., Caldas, I. L., & Baptista, M. S. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114678 · Cited by 2
Cell Master: A Versatile and User-Friendly Educational Software for Simulation of Neuronal Dynamics
Özcan, Z., Bozkır, İ. K., Yıldırım, Ö., Köse, C., & Kayıkçıoğlu, T. (2018) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno.2018.8597115 · Cited by 1
“Deneyler ve modeller üzerinde gerçekleştirilen çalışmalar bu alandaki bilgi üretiminde en önemli yöntemleri barındırmaktadır [1].”
Network Master: A Versatile and User-Friendly Educational Software for Simulation of Neural Networks
Yıldırım, Ö., Özcan, Z., Turhan, H., & Kayıkçıoğlu, T. (2018) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno.2018.8596865 · Cited by 1
“Yapılan simülasyon çalışmaları bu alan için büyük önem taşımaktadır [2].”
Paraskevov, A. V., & Zemskova, T. (2021) · Physics Letters A · doi:10.1016/j.physleta.2021.127379 · Cited by 1
Computational modeling of NMDA receptor response in Alzheimer’s disease
Dave, V., Shrimankar, A. D., Gokani, D., & Dashora, A. (2020) · Microsystem Technologies · doi:10.1007/s00542-020-05058-3 · Cited by 1
Dave, V., Shrimankar, A. D., Gokani, D., & Dashora, A. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-7486-3_10 · Cited by 1
Adaptive Multiscale Encoding: A Computational Function of Neuronal Synchronization
Pesenson, M. Z. (2013) · doi:10.1002/9783527671632.ch11 · Cited by 1
Mean Time-to-Fire for the Noisy LIF Neuron - A Detailed Derivation of the Siegert FormulaPreprint
Kreutz-Delgado, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04032 · Cited by 1
“For mammalian neurons a typical time-constant value is τ ∼ 5–10 msec [20, 18].”
BackgroundOptimization of neuronal morphologies for pattern recognition
de Sousa, G., Maex, R., Adams, R., Davey, N., & Steuber, V. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-s1-p80 · Cited by 1
“In this work I use a synaptic model with a dual exponential function, as it is replicates the conductance changes of typical synapses well [64].”
BackgroundMethodInfluentialStochastic models in neurobiology: from a multiunitary regime to EEG data
de Oliveira, A. D. (2016) · doi:10.11606/t.45.2016.tde-01062016-162919 · Cited by 1
The Distributed EngramPreprint
Feldesh, R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/583195 · Cited by 1
“The network size was varied within the range [1-6] for the inner and outer network size.”
BackgroundSmith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.004 · Cited by 1
Dynamic range Analysis of HH Model for Excitable Neurons
Sharma, D., & Garg, A. R. (2014)
Nomenclature of Ion Channels (IUPHAR Scheme)
Sterratt, D. C. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_234
Self-citationA stochastic hybrid model of a biological filter
Ocone, A., & Sanguinetti, G. (2013) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.124.10
“They are highly over-represented no t o ly in microbial regulatory network, but in a variety of other contexts: for example, feed forward loo ps are an important structure in neuronal networks in the brain [18].”
BackgroundInfluence of the electrotonic architecture on single neurons dynamics : a computational approach
Castro, A. F. (2014) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)
Multi scale modelling of diffusion processes in dendrites and dendritic spines
Pettersen, F. E. (2014) · NORA - Norwegian Open Research Archives
Liang, P., Wu, S., & Gu, F. (2015) · Springer eBooks · doi:10.1007/978-94-017-7393-5_3
Background: Problem and Methods
Tveito, A., & Lines, G. T. (2016) · Lecture notes in computational science and engineering · doi:10.1007/978-3-319-30030-6_1
Numerical stability of a scalar neural-field model with a sigmoidal firing-rate function
Lepperød, B. M. E. (2014) · NORA - Norwegian Open Research Archives
Neural Network Modeling for Organizational Psychology
Pessa, E. (2016) · Advances in finance, accounting, and economics book series · doi:10.4018/978-1-4666-9770-6.ch011
A Mathematical Model of Biphasic Intracellular Calcium Dynamics in Hormone Producing Cells
Hovind, E. (2016) · BIBSYS Brage (BIBSYS (Norway))
Biomimicing the Excitatory and InhibitorySynapse
Prajapati, A. T., Rathod, S. S., & Sashidharan, P. T. (2016) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2016.7807860
Comparison of Simulated Extracellular Spikes from Pyramidal Neurons and Interneurons
Bjørnstad, D. M. (2016) · NORA - Norwegian Open Research Archives
Fuchs, M., Zimmer, M., Grosu, R., & Hasani, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01436
“Intracellular calcium dynamics are described by first-order kinetics binding and de-binding to a storage molecule and transport out of the cell via a calcium-dependent pump, as described in [18] which undergoes a calcium-dependent increase in pump conductance on a longer timescale.”
BackgroundSato, Y. D. (2018) · Chinese Physics Letters · doi:10.1088/0256-307x/35/5/058702
Development of a User-Friendly Neuron Analyzer and Simulator (NAS)
Chaud, V. M., & Kohn, A. F. (2017) · UKSim European Symposium on Computer Modeling and Simulation (EMS) · doi:10.1109/ems.2017.19
“Moreover, each synapse obeys a first order Markovian process as described in [20], [26].”
MethodShirrafiardekani, A., Frauendiener, J., Moustafa, A. A., & Beňušková, Ľ. (2018) · Springer series in computational neuroscience · doi:10.1007/978-3-319-99103-0_20
Viewing Rate-Based Neurons as Biophysical Conductance Outputting Models
Knudsen, M., Hendseth, S., Tufte, G., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-19311-9_14
Data-driven neural mass modelling
Poole, E. I., & Sofia, L. (2019) · TDX (Tesis Doctorals en Xarxa)
Starkov Sergey, O., & Lavrenkov Yury, N. (2019) · Nuclear Energy and Technology · doi:10.3897/nucet.5.36474
“An artificially implemented axonal transport mechanism plays an important role in maintaining a stable spike generation process (Sterratt et al. 2011) (Fig.”
BackgroundBrennan, E. K. W., Sudhakar, S., Jedrasiak‐Cape, I., & Ahmed, O. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/673954
Modelagem de células de Renshaw e sua utilização em simulações do sistema neuromuscular.
Urbizagastegui, P. A. D. A. (2019) · doi:10.11606/d.3.2019.tde-22112019-150635
The simplest and efficient form of Spiking Neuron
Gadamsetty, M., & Kailath, B. J. (2019) · IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (PrimeAsia) · doi:10.1109/primeasia47521.2019.8950715
“IAF neuron is one of the rich models for neuroscientists to prove their analytical results [1], [4], [5].”
BackgroundBiophysical modeling of the neural origin of EEG and MEG signalsPreprint
Næss, S., Halnes, G., Hagen, E., Hagler, D. J., Dale, A. M., Einevoll, G. T., & Ness, T. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.01.181875
Suggestions for Further Reading
Price, D., Jarman, A. P., Mason, J., & Kind, P. C. (2011) · Wiley eBooks · doi:10.1002/9780470979624.furread
Exploring a Network Model of the Mouse Primary Visual Cortex
Myklebust, L. (2020) · NORA - Norwegian Open Research Archives
Ionic homeostasis at the perisynaptic cradle
Breslin, K. (2019) · Ulster University Research Portal (Ulster University)
A Study on Patterns of Neural Activity Generation from A Bio-realistic Cerebellum Neural Network
Vo, N. T., Duc, P. A., Le, H. N., et al. (2020) · International Conference on Green Technology and Sustainable Development (GTSD) · doi:10.1109/gtsd50082.2020.9303169
“In the case these models were implemented with a threshold function, they would also work very well for digital computations and yet with fewer neurons within the network as the first generation [4].”
MethodTheoretical Study of the Function of the IP3 Receptor / BK Channel Complex in a Single Neuron
Pérez-Bonilla, M. E., Monreal, M. R., Pérez, J. Q., et al. (2021) · Revista Mexicana de Ingeniería Biomédica · doi:10.17488/rmib.42.2.2
Development of the L-type CaV / BK Complex Simulator (I): electrophysiological interaction
Monreal, M. R., Pérez, J. Q., Escalera, M. F. P., et al. (2021) · South Florida Journal of Development · doi:10.46932/sfjdv2n2-009
Bifurcation Diagrams of Scalar ODEs
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.008
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.011
Engelbrecht, J., Tamm, K., & Peets, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-75039-8_6
Brain Rhythm Fluctuations: Envelope-Phase Modeling and Phase Synchronization
Powanwe, A. S. (2021) · uO Research (University of Ottawa) · doi:10.20381/ruor-26338
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.016
Hodgkin-Huxley Model of the Action Potential
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.017
BroomyCell: An Introductory Educational Software for Neuroscience
Özcan, Z., Yıldırım, Ö., & Kayıkçıoğlu, T. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477851
Ligands, Receptors and Rate Laws
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.006
Type I Excitability and Oscillations (SNIC and SHO Bifurcations)
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.024
Type II Excitability and Oscillations (Hopf Bifurcation)
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.023
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.019
Network mechanisms of working memory: the role of neuronal nonlinearitiesPreprint
Suarez-Perez, A., Harish, O., & Hansel, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.05091
“It has units of [1/s] and it is described by a dual exponential waveform describing the fast dynamics at the opening and the slow dynamics at the closing of the synaptic receptors [56]:”
Computational study of thalamocortical interactions: simulating oscillatory activity
Shimoura, R. O. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.59.2021.tde-06122021-160402
Nomenclature of Ion Channels (IUPHAR Scheme)
Sterratt, D. C. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_234-1
Self-citationFunction Families and Characteristic Times
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.007
Regenerative Ionic Currents and Bistability
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.015
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.001
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.027
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.002
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.026
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.014
The Low-Threshold Calcium Spike
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.025
GHK Theory of Membrane Permeation
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.012
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.005
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.020
Synchronization of Chaotic Electroencephalography (EEG) Signals
Zaqueros-Martínez, J., Rodríguez‐Gómez, G., Tlelo‐Cuautle, E., & Orihuela‐Espina, F. (2022) · Studies in big data · doi:10.1007/978-3-030-92166-8_5
Neural Network Modeling for Organizational Psychology
Pessa, E. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0414-7.ch072
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.028
Sterratt, D. C. (2014) · PLoS ONE · doi:10.1371/annotation/6d3b4757-2737-4570-9018-28802eb9437a
Self-citationReeke, G. N. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1997-6_126
Melendy, R. F., & Nguyen, L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.25.501272
Mohapatra, N., Deans, H. T., Santamarı́a, F., et al. (2018) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_239-3
Sterratt, D. C. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_232
Self-citationMohapatra, N., Deans, H. T., Santamarı́a, F., et al. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_239
Rothman, J. S. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_240
Graham, B. P. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_241
Self-citationLab-NMDAR: simuladores de la electrofisiología básica del receptor NMDA
Monreal, M. R., Bonilla, M. E. P., Pérez, J. Q., Lazalde, A. R., & Flores‐Hernández, J. (2022) · Acta Universitaria · doi:10.15174/au.2022.3597
Aminzare, Z., & Kay, A. R. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.08.519683
“There are several analytical tools for making sense of how voltage-gated ion channels interact to generate neuronal excitability [32], [33], but little for the case where one incorporates ion transporters.”
BackgroundLow Temperature and Afferent Nerve Damage Affect Pain Sensation: A Simulation Study
Rad, M. K., Ahmadi‐Pajouh, M. A., & Saviz, M. (2022) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme57741.2022.10052844
Waigh, T. A. (2014) · doi:10.1002/9781118698310.ch23
Waigh, T. A. (2014) · doi:10.1002/9781118698310.ch26
Data-driven neural mass modelling
Sofia, L. (2019) · doi:10.5821/dissertation-2117-131745
Neuronal oscillations: from single-unit activity to emergent dynamics and back
Alonso, B. D. S. (2013) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-95068
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
