Fast and accurate low-dimensional reduction of biophysically detailed neuron models
Open accessAbstract
Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the methods used so far to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to > 40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
No efforts to build reduced electrical models of neurons have used the compartment structure of the brain.
“While there have been efforts to build reduced, but still accurate, electrical models of neurons (Marasco et al., 2012), none of these to our knowledge have used the compartment structure of the brain.”
From A connectome and analysis of the adult Drosophila central brain (2020) · cites this paper as Marasco et al., 2012
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The NEURON Simulation Environment
Hines, M. L., & Carnevale, N. T. (1997) · Neural Computation · doi:10.1162/neco.1997.9.6.1179 · Cited by 2,630 · see citing works
Markram, H. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1848 · Cited by 1,191 · see citing works
Branching dendritic trees and motoneuron membrane resistivity
Rall, W. (1959) · Experimental Neurology · doi:10.1016/0014-4886(59)90046-9 · Cited by 1,013 · see citing works
Neuronal Networks of the Hippocampus
Traub, R. D., & Miles, R. (1991) · doi:10.1017/cbo9780511895401 · Cited by 802 · see citing works
$\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems
Serkan Gugercin, A., Antoulas, A. C., & Beattie, C. (2008) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/060666123 · Cited by 673 · see citing works
Neuron: A Tool for Neuroscientists
Hines, M. L., & Carnevale, N. T. (2001) · The Neuroscientist · doi:10.1177/107385840100700207 · Cited by 639 · see citing works
Intrinsic and network rhythmogenesis in a reduced traub model for CA3 neurons
Pinsky, P. F., & Rinzel, J. (1994) · Journal of Computational Neuroscience · doi:10.1007/bf00962717 · Cited by 513 · see citing works
Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction
Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D. (2006) · Science · doi:10.1126/science.1127240 · Cited by 450 · see citing works
Intrinsic and network rhythmogenesis in a reduced traub model for CA3 neurons
Pinsky, P. F., & Rinzel's, J. (1995) · Journal of Computational Neuroscience · doi:10.1007/bf00961439 · Cited by 410 · see citing works
Gamma Oscillations in the Hippocampus
Colgin, L. L., & Moser, E. I. (2010) · Physiology · doi:10.1152/physiol.00021.2010 · Cited by 376 · see citing works
Dendritic Low-Threshold Calcium Currents in Thalamic Relay Cells
Alain Destexhe, A., Neubig, M., Ulrich, D., & Huguenard, J. R. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03574.1998 · Cited by 365 · see citing works
Morgan, R. J., & Soltesz, I. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801372105 · Cited by 347 · see citing works
On the Initiation and Propagation of Dendritic Spikes in CA1 Pyramidal Neurons
Gasparini, S., Migliore, M., & Magee, J. C. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.2520-04.2004 · Cited by 340 · see citing works
Self-citationSanthakumar, V., Aradi, I., & Soltesz, I. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00777.2004 · Cited by 262 · see citing works
Multiple origins of the cortical gamma rhythm
Whittington, M. A., Cunningham, M. O., LeBeau, F. E. N., Racca, C., & Traub, R. D. (2011) · Developmental Neurobiology · doi:10.1002/dneu.20814 · Cited by 253 · see citing works
Bhalla, U. S., & Bower, J. M. (1993) · Journal of Neurophysiology · doi:10.1152/jn.1993.69.6.1948 · Cited by 202 · see citing works
Active dendrites: colorful wings of the mysterious butterflies
Johnston, D., & Narayanan, R. (2008) · Trends in Neurosciences · doi:10.1016/j.tins.2008.03.004 · Cited by 186 · see citing works
Reduced compartmental models of neocortical pyramidal cells
Bush, P. C., & Sejnowski, T. J. (1993) · Journal of Neuroscience Methods · doi:10.1016/0165-0270(93)90151-g · Cited by 175 · see citing works
Narayanan, R., & Johnston, D. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0835-08.2008 · Cited by 172 · see citing works
Hill, S., Wang, Y., Riachi, I., Schürmann, F., & Markram, H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202128109 · Cited by 162 · see citing works
Bianchi, D., Marasco, A., Limongiello, A., Marchetti, C., Marie, H., Tirozzi, B., & Migliore, M. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0383-y · Cited by 147 · see citing works
Self-citationThe theoretical foundation of dendritic function: Selected papers of Wilfrid Rall with commentaries
Segev, I., Rinzel, J., & Shepherd, G. M. (1994) · The MIT Press eBooks · Cited by 139 · see citing works
The theoretical foundation of dendritic function
Borst, A. (1995) · Trends in Neurosciences · doi:10.1016/0166-2236(95)90052-7 · Cited by 133 · see citing works
An integrated approach to classifying neuronal phenotypes
Migliore, M., & Shepherd, G. M. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1769 · Cited by 124 · see citing works
Self-citationHill, J. J., Jin, K., Mao, X., Xie, L., & Greenberg, D. A. (2012) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1205697109 · Cited by 99 · see citing works
Signal Propagation in Oblique Dendrites of CA1 Pyramidal Cells
Migliore, M., Ferrante, M., & Ascoli, G. A. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00521.2005 · Cited by 97 · see citing works
Self-citationPostlethwaite, M., Hennig, M. H., Steinert, J. R., Graham, B. P., & Forsythe, I. D. (2006) · The Journal of Physiology · doi:10.1113/jphysiol.2006.123612 · Cited by 87 · see citing works
Morphologically accurate reduced order modeling of spiking neurons
Kellems, A. R., Chaturantabut, S., Sorensen, D. C., & Cox, S. J. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0229-4 · Cited by 73 · see citing works
The Theoretical Foundation of Dendritic Function
Segev, I., Rinzel, J., & Shepherd, G. M. (1994) · The MIT Press eBooks · doi:10.7551/mitpress/6743.001.0001 · Cited by 69 · see citing works
Hendrickson, E. B., Edgerton, J. R., & Jaeger, D. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0258-z · Cited by 64 · see citing works
Effective Stimuli for Constructing Reliable Neuron Models
Druckmann, S., Berger, T. K., Schürmann, F., Hill, S., Markram, H., & Segev, I. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002133 · Cited by 52 · see citing works
Low-dimensional, morphologically accurate models of subthreshold membrane potential
Kellems, A. R., Roos, D., Xiao, N., & Cox, S. J. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-008-0134-2 · Cited by 49 · see citing works
Migliore, M. (2003) · Journal of Computational Neuroscience · doi:10.1023/a:1021906818333 · Cited by 43 · see citing works
Self-citationSimplified models of neocortical pyramidal cells preserving somatodendritic voltage attenuation
Alain Destexhe, A. (2001) · Neurocomputing · doi:10.1016/s0925-2312(01)00428-3 · Cited by 40 · see citing works
Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling
Brown, S.-A. N., Moraru, I. I., Schaff, J. C., & Loew, L. M. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0317-0 · Cited by 37 · see citing works
Creation and Reduction of a Morphologically Detailed Model of a Leech Heart Interneuron
Tobin, A.-E., Hooser, S. D. V., & Calabrese, R. L. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00026.2006 · Cited by 36 · see citing works
The use of automated parameter searches to improve ion channel kinetics for neural modeling
Hendrickson, E. B., Edgerton, J. R., & Jaeger, D. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0312-x · Cited by 23 · see citing works
Reply to Pape et al. : The phylogeography of HIV-1 group M subtype B
Worobey, M., Pitchenik, A. E., Gilbert, M. T. P., Wlasiuk, G., & Rambaut, A. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0712128105 · Cited by 12 · see citing works
Sejnowski, T. (2010) · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Stratford K., Mason A., Larkman A., Major G. & Jack J. The modeling of pyramidal neurones in the visual cortex. in The Computing Neuron.. Addison-Wesley, Workingham, UK (1989
Cited by
Sorted by citations
A connectome and analysis of the adult Drosophila central brain
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., et al. (2020) · eLife · doi:10.7554/elife.57443 · Cited by 1,128 · see citing works
Global and Multiplexed Dendritic Computations under In Vivo-like Conditions
Ujfalussy, B. B., Makara, J. K., Lengyel, M., & Branco, T. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.032 · Cited by 114 · see citing works
Computational neurobiology is a useful tool in translational neurology: the example of ataxia
Brown, S.-A., McCullough, L., & Loew, L. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00001 · Cited by 59 · see citing works
Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue
D'Angelo, E. U., Antonietti, A., Casali, S., Casellato, C., Garrido, J. A., Luque, N. R., Mapelli, L., Masoli, S., Pedrocchi, A., Prestori, F., et al. (2016) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2016.00176 · Cited by 74 · see citing works
Abuhassan, K., Taylor, J. D., Morris, P. G., Donati, E., Bortolotto, Z. A., Indiveri, G., Paton, J. F. R., & Nogaret, A. (2019) · Nature Communications · doi:10.1038/s41467-019-13177-3 · Cited by 72 · see citing works
Introducing the Dendrify framework for incorporating dendrites to spiking neural networks
Pagkalos, M., Chavlis, S., & Poirazi, P. (2023) · Nature Communications · doi:10.1038/s41467-022-35747-8 · Cited by 64 · see citing works
An efficient analytical reduction of detailed nonlinear neuron models
Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar, P., Schürmann, F., & Segev, I. (2020) · Nature Communications · doi:10.1038/s41467-019-13932-6 · Cited by 59 · see citing works
Nogaret, A., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2016) · Scientific Reports · doi:10.1038/srep32749 · Cited by 36 · see citing works
Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness
Geminiani, A., Casellato, C., Locatelli, F., Prestori, F., Pedrocchi, A., & D'Angelo, E. U. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00088 · Cited by 33 · see citing works
Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models
Marasco, A., Limongiello, A., & Migliore, M. (2013) · Scientific Reports · doi:10.1038/srep02934 · Cited by 23 · see citing works
Self-citationAutomated point-neuron simplification of data-driven microcircuit modelsPreprint
Rössert, C., Pozzorini, C., Chindemi, G., Davison, A. P., Eroe, C., King, J. G., Newton, T. H., Nolte, M., Ramaswamy, S., Reimann, M., et al. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.48550/arxiv.1604.00087 · Cited by 23 · see citing works
Silicon central pattern generators for cardiac diseases
Nogaret, A., O’Callaghan, E. L., Lataro, R. M., Salgado, H. C., Meliza, C. D., Duncan, E. R., Abarbanel, H. D. I., & Paton, J. F. R. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.282723 · Cited by 21 · see citing works
Geminiani, A., Casellato, C., D'Angelo, E. U., & Pedrocchi, A. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00035 · Cited by 18 · see citing works
Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons
Oláh, V. J., Pedersen, N. P., & Rowan, M. J. (2022) · eLife · doi:10.7554/elife.79535 · Cited by 17 · see citing works
Forrest, M. (2015) · BMC Neuroscience · doi:10.1186/s12868-015-0162-6 · Cited by 13 · see citing works
An integrator circuit in cerebellar cortex
Maex, R., & Steuber, V. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12272 · Cited by 12 · see citing works
Projection-based order reduction of a nonlinear biophysical neuronal network model
Lehtimäki, M., Paunonen, L., & Linne, M. (2019) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc40024.2019.9029510 · Cited by 12 · see citing works
Yu, G. J., Bouteiller, J. C., Song, D. B., & Berger, T. W. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2894410 · Cited by 10 · see citing works
Yu, G. J., Bouteiller, J. C., & Berger, T. W. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.588881 · Cited by 8 · see citing works
Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory
Plaster, B., & Kumar, G. (2019) · Algorithms · doi:10.3390/a12100203 · Cited by 6 · see citing works
Approaches to Parameter Estimation from Model Neurons and Biological Neurons
Nogaret, A. (2022) · Algorithms · doi:10.3390/a15050168 · Cited by 4 · see citing works
Firing patterns of the CA1 pyramidal neuron with geometric singular perturbation: a model study
Liu, Y., & Liu, S. (2020) · International Journal of Modern Physics B · doi:10.1142/s0217979220503166 · Cited by 2 · see citing works
Pham, D.-T. J., Yu, G. J., Bouteiller, J. C., & Berger, T. W. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.733155 · Cited by 2 · see citing works
Yu, G. J., Feng, Z., & Berger, T. W. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856799 · Cited by 1 · see citing works
An efficient analytical reduction of detailed nonlinear neuron modelsPreprint
Amsalem, O., Eyal, G., Rogozinski, N., Schürmann, F., Gevaert, M., Kumbhar, P., & Segev, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/506485 · Cited by 1 · see citing works
Marasco, A., & Migliore, M. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_245-1
Self-citationMarasco, A., & Migliore, M. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_245
Self-citationMarasco, A., & Migliore, M. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_245
Self-citation
- Authors
- Marasco, A., Limongiello, A., & Migliore, M.
- Affiliations
- University of Naples Federico II (IT)
- National Research Council (IT)
- Corresponding author
- Migliore, Michele
- Venue
- Scientific Reports
- Published
- 2012-12-05
- Volume
- 2
- Issue
- 1
- Pages
- 928-928
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC3514644
- OpenAlex
- W2025695585
- Cited by
- 28
- References
- 39
- Citations by year
- 2023: 1 · 2022: 3 · 2021: 1 · 2020: 4 · 2019: 6 · 2018: 3 · 2016: 3 · 2015: 3 · 2014: 2 · 2013: 2
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Funders
- Compagnia di San Paolo
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- membrane potential dynamics, synaptic input, computational efficiency, large-scale neural network simulation, biological variation
- MeSH terms
- Action Potentials, Biophysics, Humans, Models, Biological, Neurons
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
