Similar network activity from disparate circuit parameters
Abstract
It is often assumed that cellular and synaptic properties need to be regulated to specific values to allow a neuronal network to function properly. To determine how tightly neuronal properties and synaptic strengths need to be tuned to produce a given network output, we simulated more than 20 million versions of a three-cell model of the pyloric network of the crustacean stomatogastric ganglion using different combinations of synapse strengths and neuron properties. We found that virtually indistinguishable network activity can arise from widely disparate sets of underlying mechanisms, suggesting that there could be considerable animal-to-animal variability in many of the parameters that control network activity, and that many different combinations of synaptic strengths and intrinsic membrane properties can be consistent with appropriate network performance.
Integrity signals
Registry
Cites
Statistics
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?
Similar physiological outputs can be produced by variable sets of component parameters.
“similar physiological outputs can be produced by variable sets of component parameters [17][18][19][20]”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [18]
The 2-7 fold variations in mRNA expression are captured in computational models with degenerate solutions.
“and this is captured in computational models with degenerate solutions [18,31-34]”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [18]
Cites
Postsynaptic Depolarization Scales Quantal Amplitude in Cortical Pyramidal Neurons
Leslie, K. R., Nelson, S. B., & Turrigiano, G. G. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-19-j0005.2001 · Cited by 128 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667 · see citing works
Neuromodulation of neuronal circuits: back to the future
Marder, E. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.010 · Cited by 904 · see citing works
Self-citationBrian 2, an intuitive and efficient neural simulator
Stimberg, M., Brette, R., & Goodman, D. F. (2019) · eLife · doi:10.7554/elife.47314 · Cited by 749 · see citing works
Tognoli, E., & Kelso, J. A. S. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.022 · Cited by 718 · see citing works
Del Negro, C. A., Funk, G. D., & Feldman, J. L. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0003-6 · Cited by 663 · see citing works
Can structure predict function in the human brain?
Honey, C. J., Thivierge, J.-P., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.071 · Cited by 629 · see citing works
Parameter estimation for differential equations: a generalized smoothing approach
Ramsay, J. O., Hooker, G., Campbell, D., & Cao, J. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00610.x · Cited by 613 · see citing works
HOMEOSTATIC CONTROL OF NEURAL ACTIVITY: From Phenomenology to Molecular Design
Davis, G. W. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135751 · Cited by 543 · see citing works
Neuromechanics of muscle synergies for posture and movement
Ting, L. H., & McKay, J. L. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2008.01.002 · Cited by 514 · see citing works
Vogels, T. P., Rajan, K., & Abbott, L. F. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135637 · Cited by 497 · see citing works
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N., & Harvey, C. D. (2017) · Cell · doi:10.1016/j.cell.2017.07.021 · Cited by 445 · see citing works
Architectures of neuronal circuits
Luo, L. (2021) · Science · doi:10.1126/science.abg7285 · Cited by 390 · see citing works
Spatial Gene-Expression Gradients Underlie Prominent Heterogeneity of CA1 Pyramidal Neurons
Cembrowski, M. S., Bachman, J. L., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.013 · Cited by 328 · see citing works
Marder, E., O'Leary, T., & Shruti, S. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-013958 · Cited by 262 · see citing works
Self-citationMultiple Modes of Network Homeostasis in Visual Cortical Layer 2/3
Maffei, A., & Turrigiano, G. G. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.5298-07.2008 · Cited by 282 · see citing works
Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons
Cembrowski, M. S., & Spruston, N. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0125-5 · Cited by 258 · see citing works
Peelle, J. E., Troiani, V., Wingfield, A., & Grossman, M. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp142 · Cited by 245 · see citing works
How multiple conductances determine electrophysiological properties in a multicompartment model
Taylor, A. L., Goaillard, J.-M., & Marder, E. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.4438-08.2009 · Cited by 202 · see citing works
Self-citationFrom circuits to behavior: a bridge too far?
Carandini, M. (2012) · Nature Neuroscience · doi:10.1038/nn.3043 · Cited by 187 · see citing works
NetPyNE, a tool for data-driven multiscale modeling of brain circuits
Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., et al. (2019) · eLife · doi:10.7554/elife.44494 · Cited by 169 · see citing works
A forward-looking review of seizure prediction
Freestone, D. R., Karoly, P. J., & Cook, M. J. (2017) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000429 · Cited by 169 · see citing works
Gjorgjieva, J., Drion, G., & Marder, E. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.008 · Cited by 127 · see citing works
Self-citationWiring variations that enable and constrain neural computation in a sensory microcircuit
Tobin, W. F., Wilson, R. I., & Lee, W.-C. A. (2017) · eLife · doi:10.7554/elife.24838 · Cited by 123 · see citing works
Gerhard, S., Andrade, I., Fetter, R. D., Cardona, A., & Schneider-Mizell, C. M. (2017) · eLife · doi:10.7554/elife.29089 · Cited by 109 · see citing works
Is a new paradigm needed to explain how inhaled anesthetics produce immobility?
Eger, E. I., Raines, D. E., Shafer, S. L., Hemmings, H. C., & Sonner, J. M. (2008) · Anesthesia & Analgesia · doi:10.1213/ane.0b013e318182aedb · Cited by 119 · see citing works
Shomrat, T., Graindorge, N., Bellanger, C., Fiorito, G., Loewenstein, Y., & Hochner, B. (2011) · Current Biology · doi:10.1016/j.cub.2011.09.011 · Cited by 83 · see citing works
Transcriptomic correlates of neuron electrophysiological diversity
Tripathy, S. J., Toker, L., Li, B., Crichlow, C.-L., Tebaykin, D., Mancarci, B. O., & Pavlidis, P. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005814 · Cited by 69 · see citing works
Fisher, D., Olasagasti, I., Tank, D. W., Aksay, E. R. F., & Goldman, M. S. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.041 · Cited by 75 · see citing works
Hochner, B. (2013) · Brain Behavior and Evolution · doi:10.1159/000353419 · Cited by 66 · see citing works
A biophysical perspective on the resilience of neuronal excitability across timescales
Marom, S., & Marder, E. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00730-9 · Cited by 59 · see citing works
Self-citationYang, J., Shakil, H., Ratté, S., & Prescott, S. A. (2022) · eLife · doi:10.7554/elife.72875 · Cited by 41 · see citing works
Workflow for generating competing hypothesis from models with parameter uncertainty
Gomez-Cabrero, D., Compte, A., & Tegner, J. (2011) · Interface Focus · doi:10.1098/rsfs.2011.0015 · Cited by 33 · see citing works
Deficit in Long-Term Synaptic Plasticity Is Rescued by a Computationally Predicted Stimulus Protocol
Liu, R.-Y., Zhang, Y., Baxter, D. A., Smolen, P., Cleary, L. J., & Byrne, J. H. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.0643-13.2013 · Cited by 29 · see citing works
An integrative data-driven model simulating C. elegans brain, body and environment interactions
Zhao, M., Wang, N., Jiang, X., Ma, X., Ma, H., He, G., Du, K., Ma, L., & Huang, T. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00738-w · Cited by 29 · see citing works
“Graded synapses, including excitatory and inhibitory synapses, were modeled according to published C. elegans models 9,40,41 .”
Rational thoughts in neural codes
Wu, Z., Kwon, M., Daptardar, S., Schrater, P., & Pitkow, X. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912336117 · Cited by 27 · see citing works
Degeneracy in the nervous system: From neuronal excitability to neural coding
Kamaleddin, M. A. (2022) · BioEssays · doi:10.1002/bies.202100148 · Cited by 24 · see citing works
Editorial: Can There Be a Physics of the Brain?
Beggs, J. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.220001 · Cited by 17 · see citing works
Berndt, L. C. S., Singh, K. D., & Shaw, A. D. (2026) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaf149 · Cited by 3 · see citing works
A feature and not a bug: Replication failures can lead to new biology
Marder, E. (2026) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2026.103268
Self-citation
- Authors
- Prinz, A. A., Bucher, D., & Marder, E.
- Affiliations
- Brandeis University (US)
- Corresponding author
- Prinz, Astrid A
- Venue
- Nature Neuroscience
- Published
- 2004-11-21
- Volume
- 7
- Issue
- 12
- Pages
- 1345-1352
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2132047598
- Cited by
- 1,091
- References
- 1
- Citations by year
- 2026: 65 · 2025: 54 · 2024: 80 · 2023: 58 · 2022: 78 · 2021: 59 · 2020: 43 · 2019: 92 · 2018: 50 · 2017: 48 · 2016: 31 · 2015: 61 · 2014: 51 · 2013: 53 · 2012: 43
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (MH 46742)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurobiology and Insect Physiology Research
- Keywords
- neuronal network activity, synaptic efficacy, intrinsic membrane properties, stomatogastric ganglion, pyloric network, Crustacea, parameter sensitivity
- MeSH terms
- Action Potentials, Animals, Crustacea, Nerve Net, Neurons, Synapses
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