The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Neural constraints on learning
Open accessAbstract
Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.
Overview
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
Verification
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?
In the citing paper
“Brain machine interfaces can be used to set up specific local learning problems, in which the brain is asked to create certain user-specified representations, and the dynamics of this process can be monitored (Sadtler et al., 2014).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Sadtler et al., 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1954) · Journal of Experimental Psychology · doi:10.1037/h0055392 · Cited by 6,466
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1992) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.121.3.262 · Cited by 6,296
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013) · Nature · doi:10.1038/nature12742 · Cited by 1,910
Error Correction, Sensory Prediction, and Adaptation in Motor Control
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-153135 · Cited by 1,869
Neural population dynamics during reaching
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2012) · Nature · doi:10.1038/nature11129 · Cited by 1,704
Self-citationThe importance of mixed selectivity in complex cognitive tasks
Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S. (2013) · Nature · doi:10.1038/nature12160 · Cited by 1,639
Cortical control of a prosthetic arm for self-feeding
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008) · Nature · doi:10.1038/nature06996 · Cited by 1,591
Dimensionality reduction for large-scale neural recordings
Cunningham, J. P., & Yu, B. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3776 · Cited by 1,219
Self-citationLearning-Induced LTP in Neocortex
Rioult-Pedotti, M.-S., Friedman, D. J., & Donoghue, J. P. (2000) · Science · doi:10.1126/science.290.5491.533 · Cited by 1,014
Cited by
Sorted by citations
Dimensionality reduction for large-scale neural recordings
Cunningham, J. P., & Yu, B. M. (2014) · Nature Neuroscience · doi:10.1038/nn.3776 · Cited by 1,219
Self-citationSpeech synthesis from neural decoding of spoken sentences
Anumanchipalli, G. K., Chartier, J., & Chang, E. F. (2019) · Nature · doi:10.1038/s41586-019-1119-1 · Cited by 883
Krakauer, J. W., Hadjiosif, A. M., Xu, J., Wong, A. L., & Haith, A. M. (2019) · Comprehensive physiology · doi:10.1002/cphy.c170043 · Cited by 918
Neural Manifolds for the Control of Movement
Gallego, J. Á., Perich, M. G., Miller, L. E., & Solla, S. A. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.025 · Cited by 676
Inferring single-trial neural population dynamics using sequential auto-encoders
Pandarinath, C., O’Shea, D. J., Collins, J., Józefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0109-9 · Cited by 721
Self-citation
Metadata
- Authors
- 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.
- Affiliations
- University of Pittsburgh (US)
- Center for the Neural Basis of Cognition (US)
- Palo Alto Medical Foundation (US)
- Corresponding author
- Yu, Byron M.
- Batista, Aaron P.
- Venue
- Nature
- Published
- 2014-08
- Volume
- 512
- Issue
- 7515
- Pages
- 423-426
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4393644
- OpenAlex
- W1966537851
- Cited by
- 713
- References
- 41
- Citations by year
- 2026: 74 · 2025: 65 · 2024: 81 · 2023: 68 · 2022: 69 · 2021: 67 · 2020: 55 · 2019: 69 · 2018: 51 · 2017: 40 · 2016: 40 · 2015: 27 · 2014: 5
- FWCI
- 20.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS065065, P30NS076405)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD071686)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- neural activity patterns, neural network topology, motor cortex, brain-computer interface, closed-loop learning, brain reorganization, learning constraints, rhesus monkeys, primary motor cortex, skill transfer
- MeSH terms
- Animals, Computers, Learning, Macaca mulatta, Male, Models, Neurological, Motor Cortex, Motor Skills, Nerve Net, Neurons, Brain-Computer Interfaces
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
