The brain's best kept secret is its degenerate structure
Open accessAbstract
Degeneracy is defined as multiple sets of solutions that can produce very similar system performance. Degeneracy is seen across phylogenetic scales, in all kinds of organisms. In neuroscience, degeneracy can be seen in the constellation of biophysical properties that produce a neuron's characteristic intrinsic properties and/or the constellation of mechanisms that determine circuit outputs or behavior. Here, we present examples of degeneracy at multiple levels of organization, from single-cell behavior, small circuits, large circuits, and, in cognition, drawing conclusions from work ranging from bacteria to human cognition. Degeneracy allows the individual-to-individual variability within a population that creates potential for evolution.
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?
Biological systems are inherently degenerate.
“Biological systems are inherently degenerate [14-16]”
From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [14]
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
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · see citing works
Universally Sloppy Parameter Sensitivities in Systems Biology Models
Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030189 · Cited by 1,261 · see citing works
(2006) · doi:10.1093/acprof:oso/9780195100273.001.0001 · Cited by 1,186 · see citing works
Neural constraints on learning
Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., Yu, B. M., & Batista, A. P. (2014) · Nature · doi:10.1038/nature13665 · Cited by 713 · see citing works
Albantakis, L., Barbosa, L., Findlay, G., Grasso, M., Haun, A. M., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, B. E., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011465 · Cited by 292 · see citing works
Self-citationHow 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-citationAlbantakis, L., Hintze, A., Koch, C., Adami, C., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003966 · Cited by 101 · see citing works
What caused what? A quantitative account of actual causation using dynamical causal networks
Albantakis, L., Marshall, W., Hoel, E., & Tononi, G. (2019) · Entropy · doi:10.3390/e21050459 · Cited by 67 · see citing works
Self-citationCausal reductionism and causal structures
Grasso, M., Albantakis, L., Lang, J. P., & Tononi, G. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00911-8 · Cited by 42 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Pearl J (2000) Causality: models, reasoning and inference. Cambridge Univ Press.
Cited by
Sorted by citations
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
- Albantakis, L., Bernard, C., Brenner, N., Marder, E., & Narayanan, R.
- Affiliations
- University of Wisconsin–Madison (US)
- Inserm (FR)
- Technion – Israel Institute of Technology (IL)
- Brandeis University (US)
- Indian Institute of Science Bangalore (IN)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2024-10-02
- Volume
- 44
- Issue
- 40
- Pages
- e1339242024
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC11450540
- OpenAlex
- W4403110471
- Cited by
- 34
- References
- 9
- Citations by year
- 2026: 24 · 2025: 10
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- HHS | NIH | National Institute of Mental Health (R01 MH046742-26)
- HHS | NIH | National Institute of Neurological Disorders and Stroke (R35 NS097343)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- degeneracy, brain structure, neural circuits, interindividual variability, evolution, phylogenetic scale, cognition
- MeSH terms
- Animals, Brain, Cognition, Biological Evolution, Humans, Models, Neurological, Nerve Net, 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