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 101–200 of 316
Marghoti, G., Prado, T. D. L., Conte, A. C., et al. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112401 · Cited by 9
Noise-induced coexisting firing patterns in hybrid-synaptic interacting networks
Wang, X., Zhang, X., Zheng, M., Xu, L., & Xu, K. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.128591 · Cited by 9
Control of sampling rate in map-based models of spiking neurons
Rulkov, N. F., & NEIMAN, A. B. (2018) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2018.01.021 · Cited by 9
“Numerical simulations of network models for neuronal activity is an important component of many neurobiological studies [1] .”
BackgroundBursting Dynamics of Spiking Neural Network Induced by Active Extracellular Medium
Викторович, С. С., & Kazantsev, V. (2023) · Mathematics · doi:10.3390/math11092109 · Cited by 9
Belozor, О. S., Yakovleva, D. A., Potapenko, I., Shuvaev, А. N., Smolnikova, M. V., Vasilev, A., Пожиленкова, Е. А., & Shuvaev, А. N. (2019) · Brain Sciences · doi:10.3390/brainsci9040080 · Cited by 9
“This curve is convenient for the prediction of the conduction changes in synapses [27].”
BackgroundSterratt, D. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067096 · Cited by 8
“The update scheme that Gierer [24] used is not clear from his paper, but the endpoints of the results I obtain are the same as his; a more detailed discussion is available elsewhere [51].”
Self-citationPanche, I. D. L. P., Gómez-Orozco, V., Álvarez, M. A., Henao, O. A., Daza-Santacoloma, G., & Orozco, A. A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19390-8_77 · Cited by 8
“where V (n) m and V (n) o are the transmembrane potencial and the extracellular potential at the nth compartment, cm is the specific membrane capacitance, ra is the intracellular axial resistance per unit length, ∆x and d are the compartment length and diameter, respectively, and iion is the total ionic current flowin…”
BackgroundIntrinsic physiology of inhibitory neurons changes over auditory development
Carroll, B. J., Bertram, R., & Hyson, R. L. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00447.2017 · Cited by 8
“We tested the effect of temperature on voltage behavior with the hatchling model, assuming Q10 3 for n, y, z, and h and Q10 1.2 for gL, gNa, gLT, and gHT (Sterratt et al. 2011).”
MethodInfluentialEvaluation of connectivity estimates using spiking neuronal network models
Nunes, R. V., Reyes, M. B., & Camargo, R. Y. D. (2019) · Biological Cybernetics · doi:10.1007/s00422-019-00796-8 · Cited by 8
Improving Neural Simulations with the EMI Model
Buccino, A. P., Kuchta, M., Schreiner, J., et al. (2020) · Simula SpringerBriefs on computing · doi:10.1007/978-3-030-61157-6_7 · Cited by 8
“Whereas the use of this modeling framework has been the gold standard to simulate neuronal activity for decades (19; 6), there are some important assumptions that need to be discussed: • A neuron is represented as a discrete set of nodes.”
MethodRegular spiking in high-conductance states: The essential role of inhibition
Bárta, T., & Kostal, L. (2021) · Physical review. E · doi:10.1103/physreve.103.022408 · Cited by 8
Kaiser, M. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/11980.001.0001 · Cited by 25
Calculating the Consequences of Left-Shifted Nav Channel Activity in Sick Excitable Cells
Joós, B., Barlow, B., & Morris, C. E. (2017) · Handbook of experimental pharmacology · doi:10.1007/164_2017_63 · Cited by 8
Susi, G., Garcés, P., Paracone, E., Cristini, A., Salerno, M. S., Maestú, F., & Pereda, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-91513-8 · Cited by 9
“Regarding the latter, diverse models have been developed to approximate experimentally observed conductance changes (e.g., alpha function 45,46 and difference of two exponentials 47,48 ), which unfortunately are not suitable for event-driven implementations, at least in their original form.”
BackgroundNeural Density Estimation and Likelihood-free InferencePreprint
Papamakarios, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.13233 · Cited by 78
“…2010), population generics (Beaumont et al., 2002), and computational neuroscience (Lueckmann et al., 2017; Markram et al., 2015; Pospischil et al., 2008; Sterratt et al., 2011) In addition to their scientific applications, simulator models can be useful in engineering and artificial intelligence.”
BackgroundMacromolecular rate theory explains the temperature dependence of membrane conductance kinetics
Pahlavan, B., Buitrago, N., & Santamarı́a, F. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.033 · Cited by 7
Chaos shapes transient synchrony activities and switchings in the excitatory-inhibitory networks
Zhu, G., Zhang, Y., Wu, J., Zheng, M., & Xu, K. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-09471-5 · Cited by 7
Patoary, M. N. I., Tropper, C., Lin, Z., et al. (2014) · Winter Simulation Conference · doi:10.5555/2693848.2694278 · Cited by 7
AMPA receptors at ribbon synapses in the mammalian retina: kinetic models and molecular identity
Hartveit, E., Zandt, B., Madsen, E., Castilho, Á., Mørkve, S. H., & Veruki, M. L. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1520-1 · Cited by 7
“The different processes and phenomena underlying synaptic transmission (transmitter diffusion, receptor kinetics, single-channel conductance, etc.) display marked dependence (roughly exponential) on temperature (for reviews, see Hille 2001; Roth and van Rossum 2010; Sterratt et al. 2011).”
BackgroundSystematic Data Mining of Hippocampal Synaptic Properties
Moradi, K., & Ascoli, G. A. (2018) · Springer series in computational neuroscience · doi:10.1007/978-3-319-99103-0_11 · Cited by 8
Mean-reverting neuronal model based on two alternating patterns
Ratanov, N. (2020) · Biosystems · doi:10.1016/j.biosystems.2020.104190 · Cited by 6
Cut wires: The Electrophysiology of Regenerated Tissue
Lowe, A. L., & Thakor, N. V. (2021) · Bioelectronic Medicine · doi:10.1186/s42234-021-00062-y · Cited by 6
Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit
Merchan, J. A. D., Tequita-Vargas, O. F., & Naranjo, F. (2016) · Ingeniería y Ciencia · doi:10.17230/ingciencia.12.23.5 · Cited by 6
“Due to the fact these equations have no exact solution, makes necessary to use the numerical methods, in this case, fourth order Runge-Kutta method to solve them [3],[7],[16],[17].”
MethodLarge time step discrete-time modeling of sharp wave activity in hippocampal area CA3
Malerba, P., Rulkov, N. F., & Bazhenov, M. (2018) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2018.12.009 · Cited by 6
Gawthrop, P. J., Siekmann, I., Kameneva, T., Saha, S., Ibbotson, M. R., & Crampin, E. J. (2015) · arXiv (Cornell University) · Cited by 8
Integration without integrated models or theories
Hochstein, E. (2023) · Synthese · doi:10.1007/s11229-023-04298-w · Cited by 5
“Computational modeling’s dependence on idealizations and abstractions have been well documented (see, for example, Chirimuuta, 2014; Sejnowski et al., 1988; Sterratt et al., 2011, p. 316).”
MethodDiscrete Modeling of Multi-transmitter Neural Networks with Neuronal Competition
Bazenkov, N. I., Dyakonova, V. E., Kuznetsov, O. P., Sakharov, D. A., VORONTSOV, D. D., & Zhilyakova, L. Y. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-63940-6_2 · Cited by 5
“A review of continuous computational models of neurons, neuron compartments, and neural networks can be found in the book (Sterratt, Graham, Gillies, & Willshaw, 2011).”
BackgroundChanges of Firing Rate Induced by Changes of Phase Response Curve in Bifurcation Transitions
Sato, Y. D., & Aihara, K. (2014) · Neural Computation · doi:10.1162/neco_a_00653 · Cited by 5
“In the transition state theory, such nonlinearity is derived on the basis of a thermodynamic concept of the transition probability of the voltage-gated channel activation (Sterratt et al., 2011).”
BackgroundThe transfer function of neuron spike
Palmieri, I., Monteiro, L. H. A., & Miranda, M. D. M. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.04.003 · Cited by 5
An asynchronous GVT computing algorithm in neuron time warp-multi thread
Lin, Z., & Yao, Y. (2015) · Winter Simulation Conference · doi:10.5555/2888619.2888741 · Cited by 5
Hodgkin–Huxley model based on ionic transport in axoplasmic fluid
Bhatia, S., Singh, P., & Sharma, P. (2017) · Journal of Integrative Neuroscience · doi:10.3233/jin-170029 · Cited by 5
Bhattacharya, R. K., & Chatterjee, N. D. (2021) · Environmental science and engineering · doi:10.1007/978-3-030-72296-8_7 · Cited by 5
Hanssen, K. Ø., & Malthe‐Sørenssen, A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.967735 · Cited by 5
“This interaction can affect transport, most likely lowering the diffusion constant as the particle is subject to forces from a lot of other particles, effectively slowing it down (Sterratt et al., 2011).”
BackgroundParallel Stochastic Discrete Event Simulation of Calcium Dynamics in Neuron
Patoary, M. N. I., Tropper, C., McDougal, R. A., et al. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2756930 · Cited by 5
Discrete Modeling of Neuronal Interactions in Multi-Transmitter Networks
Bazenkov, N. I., VORONTSOV, D. D., Dyakonova, V. E., Zhilyakova, L. Y., Zakharov, I., Kuznetsov, O. P., Геннадьевич, К. С., & Sakharov, D. A. (2018) · Scientific and Technical Information Processing · doi:10.3103/s0147688218050015 · Cited by 6
Reduced order models of myelinated axonal compartments
Ioan, D., Bărbulescu, R., Silveira, L. M., & Ciuprina, G. (2019) · Journal of Computational Neuroscience · doi:10.1007/s10827-019-00726-4 · Cited by 5
Industry 5.0: principles of creating system tetrad of cybersocial ecosystems
Babkin, A., Liberman, I., Klachek, P., et al. (2023) · VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES ECONOMICS · doi:10.24143/2073-5537-2023-1-103-120 · Cited by 4
McDonnell, M. D., Mohan, A., & Stricker, C. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00058 · Cited by 4
On the dynamics of axonal membrane: Ion channel as the basic unit of a deterministic model
Correale, T. G., & Monteiro, L. H. A. (2016) · Applied Mathematics and Computation · doi:10.1016/j.amc.2016.07.006 · Cited by 4
Load balancing for multi-threaded PDES of stochastic reaction-diffusion in neurons
Lin, Z., Tropper, C., Yao, Y., et al. (2016) · Journal of Simulation · doi:10.1057/s41273-016-0033-x · Cited by 4
“Electrical models for neurons were developed some time ago, using well-known laws of electricity (Lytton, 2002; Carnevale and Hines, 2006; Sterratt et al , 2011), e.g., Ohm, Kirchhoff, capacitance.”
MethodSubthreshold linear modeling of dendritic trees: A computational approach
Khodaei, A., & Pierobon, M. (2016) · doi:10.1109/embc.2016.7590683 · Cited by 4
“equal to the resting potential Em, in agreement with widely accepted models from the neurophysiology literature [15].”
ResultThe effects of paranodal myelin damage on action potential depend on axonal structure
Kohan, E. D., Lashkari, B. S., & Sparrey, C. J. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1691-1 · Cited by 4
“membrane capacitance due to the myelin sheath around axons [75].”
BackgroundMethodMelendy, R. F. (2018) · Journal of Electrical Bioimpedance · doi:10.2478/joeb-2018-0015 · Cited by 4
“The Leaky Cable Conductance Property of an Axon One solution to the neuronal cable equations is a function describing the input resistance Rin (Ω) of a leaky cable along the longitudinal length of neuronal fiber [21,22]:”
BackgroundAn asynchronous GVT computing algorithm in neuron time warp-multi thread
Lin, Z., & Yao, Y. (2015) · Winter Simulation Conference · doi:10.1109/wsc.2015.7408238 · Cited by 4
“Finally we show results of simulating a calcium wave model in an unbranched apical dendrite of a hippocampal pyramidal neuron.”
BackgroundNjegovanović, A. (2023) · International Journal of Social Science Studies · doi:10.11114/ijsss.v11i1.5872 · Cited by 4
Rifat, M. N. I., & Adnan, A. (2024) · Multiscale Science and Engineering · doi:10.1007/s42493-024-00112-y · Cited by 4
“In light of the refractory period exhibited by ion channels, which necessitates a recovery interval before they may accumulate subsequent signals [29–32], our study also sought to investigate the potential behavior of the damaged channels under varying strain rates.”
Trends Innovations Challenges in Employing Interdisciplinary Approaches to Biomedical Sciences
Ganeshkumar, M., Ayyadhury, S., & Murugan, E. (2024) · Springer eBooks · doi:10.1007/978-981-97-1777-4_20 · Cited by 4
Phase diagram of spiking neural networks
Seyed-allaei, H. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00019 · Cited by 4
“…for Research in Fundamental Sciences (IPM), Tehran, Iran Edited by: Stefano Fusi, Columbia University, USA Reviewed by: Kechen Zhang, Johns Hopkins Department of Biomedical Engineering, USA Gianluigi Mongillo, Paris Descartes University, France *Correspondence: Hamed Seyed-allaei, School of…”
BackgroundMethodInfluentialNeurobiological Computation and Synthetic Intelligence
Lindley, C. A. (2013) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-642-37225-4_4 · Cited by 6
“All of the hierarchical levels of the human nervous system have been simulated in detail using digital computer technology [22].”
BackgroundMethodParameter optimization and nonlinear fitting for computational models in neuroscience on GPGPUs
Nair, M., Subramanyan, K., Nair, M., et al. (2014) · doi:10.1109/ichpca.2014.7045324 · Cited by 4
Load Balancing for Parallel Discrete Event Simulation of Stochastic Reaction and Diffusion
Lin, Z., & Yao, Y. (2015) · doi:10.1109/smartcity.2015.137 · Cited by 4
“Stochastic model is a far more realistic and accurate representation [3], [4] for this sort of situation.”
BackgroundSmith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905.021 · Cited by 4
Linaro, D., & Giugliano, M. (2014) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_131-1 · Cited by 5
Graham, B. P. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_241-1 · Cited by 4
Self-citationAnalysis of proteins in computational models of synaptic plasticityPreprint
Heil, K. F., Wysocka, E., Sorokina, O., Kotaleski, J. H., Simpson, T. I., Armstrong, J. D., & Sterratt, D. C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/254094 · Cited by 5
Self-citationZhou, X., Zhang, Y., Gu, T., Zheng, M., & Xu, K. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129523 · Cited by 3
Integration of Rule-Based Models and Compartmental Models of Neurons
Sterratt, D. C., Sorokina, O., & Armstrong, J. D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27656-4_9 · Cited by 3
“A normalised Goldmann-Hodgkin-Katz (GHK) current equation [25] can be used for fS,k.”
BackgroundMethodSelf-citationDave, V., & Manchanda, R. (2017) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720017500111 · Cited by 3
Cellular Biophysics and Modeling
Smith, G. D. (2019) · Cambridge University Press eBooks · doi:10.1017/9780511793905 · Cited by 4
Modeling Dendrites and Spatially-Distributed Neuronal Membrane Properties
Chavlis, S., & Poirazi, P. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_2 · Cited by 3
Patoary, M. N. I., Tropper, C., Lin, Z., et al. (2014) · Winter Simulation Conference · doi:10.1109/wsc.2014.7020177 · Cited by 3
Metamodelling of a two-population spiking neural network
Skaar, J.-E. W., Haug, N., Stasik, A., Pleßer, H. E., Einevoll, G. T., & Tøndel, K. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011625 · Cited by 2
Model of self-oscillations in a neuron generator under the action of an active medium
Adamchik, D. A., Matrosov, V. V., Semyanov, A. V., & Kazantsev, V. (2015) · Journal of Experimental and Theoretical Physics Letters · doi:10.1134/s0021364015210031 · Cited by 3
Parallel Discrete Event Simulation of Stochastic Reaction and Diffusion Using Reverse Computation
Lin, Z., & Yao, Y. (2015) · IEEE International Conference on Smart City/SocialCom/SustainCom · doi:10.1109/smartcity.2015.142 · Cited by 4
“It is well known that a system consisting of a collection of chemical reactions can be represented by a chemical master equation, the solution of which is a probability distribution of the chemical reactants in the system [6].”
BackgroundNair, M., Surya, S., Kumar, R. S., et al. (2015) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics.2015.7488425 · Cited by 3
“The neuron is abstracted as an RC circuit, and the basic current, voltage laws are applied to calculate the voltage across the membrane[22].”
BackgroundDynamics of HH Model for Excitable Neuron with Added Voltage Gated Calcium Channel
Sharma, D., & Garg, A. R. (2014) · Cited by 3
Interaction of STDP and metaplasticity in modelling heterosynaptic plasticity.
Shirrafiardekani, A. (2017) · Otago University Research Archive (University of Otago) · Cited by 2
An electrodiffusive neuron-extracellular-glia model with somatodendritic interactionsPreprint
Sætra, M. J., Einevoll, G. T., & Halnes, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.13.200287 · Cited by 3
“Membrane parameters Parameter Value Reference cm 3 · 10−2 F/m [33, 45] ḡNa,leak,n 0.”
BackgroundMethodInfluentialQ10: The Effect of Temperature on Ion Channel Kinetics
Sterratt, D. C. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_236 · Cited by 3
Self-citationSometimes extracellular recordings fail for good reasons
Jæger, K. H., & Tveito, A. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00730-2 · Cited by 2
The role of synchronization of neural structures on neuron dynamics
Karabacak, Ö., Elibol, R., & Şengör, N. S. (2014) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2014.6830439 · Cited by 2
“Nöral hücreler arasındaki azaltıcı ve arttırıcı bağlantıların etkisini modellemek üzere gx girişi eklendi ve bu sinaptik etkileşim literatüre uygun olarak [17]–[19] aşağıda verilen dinamik sinaps bağlantısı ile verildi:”
Computational Study of Astroglial Calcium Homeostasis in a Semi-isolated Synaptic Cleft
Toman, M., Wade, J. J., Mcdaid, L. J., & Harkin, J. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206981 · Cited by 2
Response of Gap Junction-Coupled Dendrites: A Sum-Over-Trips Approach
Timofeeva, Y., & Coombes, S. (2013) · Springer series in computational neuroscience · doi:10.1007/978-1-4614-8094-5_27 · Cited by 2
Spiking Neural Networks and Mathematical Models
Gasparinatou, M. M., Matzakos, N., & Vlamos, P. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-31982-2_8 · Cited by 2
Spiking Neural Network with Tetrapartite Synapse
Викторович, С. С., & Kazantsev, V. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-44865-2_9 · Cited by 2
Lebedev, A., Kazantsev, V., & Stasenko, S. V. (2024) · Izvestiya VUZ Applied Nonlinear Dynamics · doi:10.18500/0869-6632-003092 · Cited by 2
Leveraging Homeostatic Plasticity to Enable Anomaly Detection in Spiking Neural Networks
Nawaiseh, R. M. A., Vita, F. D., Catalfamo, E., et al. (2024) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp61445.2024.00046 · Cited by 2
“The following equation describes the behaviour of synaptic weights following the reverse STDP technique. t pre − t post > 0, A + > 0 (3) This is not the only way to define STDP, more complex formulations can take into account a set of spikes from presynaptic and postsynaptic pulse trains [14].”
MethodOrima, T., Horio, Y., & Tsuji, T. (2024) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.15.796 · Cited by 2
In action with action potentials
Heck, A. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1286/1/012054 · Cited by 2
Bursting analysis and synapse mechanism of a piece-wise constant spiking neuron model
Yamashita, Y., & Torikai, H. (2012) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252389 · Cited by 2
Typical frequency-current curves of neurons obtained from a model based on cellular automaton
Correale, T. G., & Monteiro, L. H. A. (2017) · Applied Mathematics and Computation · doi:10.1016/j.amc.2017.01.042 · Cited by 2
A biophysical model of heat sensitivity in nociceptive C-fiber neurons
Aruljothi, S., Mandge, D., & Manchanda, R. (2017) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2017.8008422 · Cited by 2
“The Q10 factor quantifies temperature dependence [17].”
Uncertainty and sensitivity analysis to complex systems
Zhu, Y., Wang, Q. A., Li, W., et al. (2017) · International Journal of Modern Physics C · doi:10.1142/s0129183117501091 · Cited by 2
Hodgkin–Huxley Model Revisited to Incorporate the Physical Parameters Affected by Anesthesia
Bhatia, S., Singh, P., & Sharma, P. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-5687-1_47 · Cited by 1
de Pereira, U. T., Panche, I. D. L. P., Gómez-Orozco, V., de Pereira, U. T., Álvarez, M. A., Henao-Gallo, Ó. A., de Pereira, U. T., Daza-Santacoloma, G., Cafetero, I. D. E. Y. P. D. E., Orozco, A. A., et al. (2017) · Revista Facultad de Ingeniería Universidad de Antioquia · doi:10.17533/udea.redin.n84a03 · Cited by 2
“The term Δx in φm and φ0 is the compartment length [21-23].”
Cellular Biophysics and Modeling: A Primer on the Computational Biology of Excitable Cells
Smith, G. D. (2019) · Cited by 2
Yue, K., Lee, R. K., & Parker, A. C. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8716935 · Cited by 2
“The model of AMPAR used in this work is from [10].”
MethodFunctional Connectivity Analysis of Neuronal Interactions
Nischay, Sundeep, M. S. G., Reddy, R., et al. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.073 · Cited by 2
Statistical Data Assimilation: Formulation and Examples From Neurobiology
Miller, A., Li, D., Platt, J. A., Daou, A., Margoliash, D., & Abarbanel, H. D. I. (2018) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2018.00053 · Cited by 3
“where the reversal potential is the equilibrium Nernst potential [8, 9].”
BackgroundMethodInfluentialMohapatra, N., Deans, H. T., Santamarı́a, F., et al. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_239 · Cited by 2
Discrete Modeling of Multi-Transmitter Neural Networks with Neuron CompetitionPreprint
Bazenkov, N. I., Dyakonova, V. E., Kuznetsov, O. P., Sakharov, D. A., VORONTSOV, D. D., & Zhilyakova, L. Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02176 · Cited by 2
“A review of continuous computational models of neurons, neuron compartments, and neural networks can be found in the book (Sterratt, Graham, Gillies, & Willshaw, 2011).”
BackgroundA comparison of six numerical methods for integrating a compartmental Hodgkin-Huxley type modelPreprint
Park, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08159 · Cited by 2
Sterratt, D. C. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_232-3 · Cited by 2
Self-citationRothman, J. S. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_240-1 · Cited by 3
Linaro, D., & Giugliano, M. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_131 · Cited by 2
Mohapatra, N., Deans, H. T., Santamarı́a, F., et al. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_239-2 · Cited by 2
Ohtaki, S., & Torikai, H. (2024) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.15.682 · Cited by 1
High-order synchronization in identical neurons with asymmetric pulse coupling
Abhay, & Dar, G. (2025) · Physical review. E · doi:10.1103/physreve.111.034202 · Cited by 2
Yi, G., Cui, J., & Bai, R. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10297-z · Cited by 1
“The spatial extensibility allows the dendrites to receive synaptic inputs from other neurons, and the dendrites convert these inputs into electrical signals and transmit them to the axon initial segment (Branco and Hausser 2010; Sterratt et al. 2011; Duan et al. 2020).”
Generalized Synchronization of Hindmarsh–Rose Neurons with Memristive Couplings
Carro-Pérez, I., & Barajas-Ramírez, J. G. (2025) · Dynamics · doi:10.3390/dynamics5040050 · Cited by 1
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
