Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Open accessAbstract
Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.
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?
RNNs trained in many computational tasks develop modular neural populations and dynamical motifs that are re-used across tasks.
“RNNs trained in many computational tasks develop modular neural populations and dynamical motifs that are re-used across tasks (Driscoll et al., 2022; Yang et al., 2019)”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Driscoll et al., 2022
RNNs trained in many computational tasks develop modular neural populations and dynamical motifs that are re-used across tasks.
“For example, RNNs trained in many computational tasks develop modular neural populations and dynamical motifs that are re-used across tasks (Driscoll et al., 2022; Yang et al., 2019).”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as Driscoll et al., 2022
Neurons in the medial entorhinal cortex typically represent one or more features such as spatial position, heading direction, and environmental landmarks and are therefore thought to support navigation.
“neurons in the medial entorhinal cortex typically represent one or more features such as spatial position, heading direction, and environmental landmarks and are therefore thought to support navigation.[15][16][17][18][19][20][21][22]”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [17]
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering
Strogatz, S. H., Friedman, M. J., Mallinckrodt, A. J., & McKay, S. R. (1994) · Computers in Physics · doi:10.1063/1.4823332 · Cited by 4,654 · see citing works
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · cited 1× · see citing works
“While artificial systems are capable of outperforming humans in specific complex cognitive tasks 1–3 , they so far lack flexibility for rapid learning and task switching.”
BackgroundHierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.2307/2282967 · Cited by 3,294 · see citing works
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
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 1,807 · see citing works
Self-citationMulti-task connectivity reveals flexible hubs for adaptive task control
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3470 · Cited by 1,704 · see citing works
Hensch, T. K. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144327 · Cited by 1,300 · 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
Variability, compensation and homeostasis in neuron and network function
Marder, E., & Goaillard, J.-M. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1949 · Cited by 1,212 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works
Cortical activity in the null space: permitting preparation without movement
Kaufman, M. T., Churchland, M. M., Ryu, S. I., & Shenoy, K. V. (2014) · Nature Neuroscience · doi:10.1038/nn.3643 · Cited by 831 · see citing works
Self-citationHierarchical organization of cortical and thalamic connectivity
Harris, J. A., Mihalas, S., Hirokawa, K. E., Whitesell, J. D., Choi, H., Bernard, A., Bohn, P., Caldejon, S., Casal, L., Cho, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1716-z · Cited by 785 · 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
A neural network that finds a naturalistic solution for the production of muscle activity
Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015) · Nature Neuroscience · doi:10.1038/nn.4042 · Cited by 598 · see citing works
Self-citationOpening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
Sussillo, D., & Barak, O. (2013) · Neural Computation · doi:10.1162/neco_a_00409 · Cited by 575 · see citing works
Self-citationTask representations in neural networks trained to perform many cognitive tasks
Yang, G. R., Joglekar, M. R., Song, H. F., Newsome, W. T., & Wang, X.-J. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0310-2 · Cited by 549 · see citing works
Robust timing and motor patterns by taming chaos in recurrent neural networks
Laje, R., & Buonomano, D. V. (2013) · Nature Neuroscience · doi:10.1038/nn.3405 · Cited by 454 · see citing works
Flexible timing by temporal scaling of cortical responses
Wang, J., Narain, D., Hosseini, E. A., & Jazayeri, M. (2017) · Nature Neuroscience · doi:10.1038/s41593-017-0028-6 · Cited by 425 · see citing works
Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks
Mastrogiuseppe, F., & Ostojic, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.003 · Cited by 394 · see citing works
Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response
Russo, A. A., Bittner, S. R., Perkins, S. M., Seely, J., London, B. M., Lara, A. H., Miri, A., Marshall, N. J., Kohn, A., Jessell, T. M., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.004 · Cited by 299 · see citing works
Learning by neural reassociation
Golub, M. D., Sadtler, P. T., Oby, E. R., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Chase, S. M., & Yu, B. M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0095-3 · Cited by 274 · see citing works
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization
Masse, N. Y., Grant, G. D., & Freedman, D. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803839115 · Cited by 260 · see citing works
Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics
Remington, E. D., Narain, D., Hosseini, E. A., & Jazayeri, M. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.020 · Cited by 251 · see citing works
The role of population structure in computations through neural dynamics
Dubreuil, A., Valente, A., Beiran, M., Mastrogiuseppe, F., & Ostojic, S. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01088-4 · Cited by 237 · see citing works
Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions
Pinto, L., Rajan, K., DePasquale, B., Thiberge, S. Y., Tank, D. W., & Brody, C. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.025 · Cited by 235 · see citing works
New neural activity patterns emerge with long-term learning
Oby, E. R., Golub, M. D., Hennig, J. A., Degenhart, A. D., Tyler-Kabara, E. C., Yu, B. M., Chase, S. M., & Batista, A. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820296116 · Cited by 221 · cited 1× · see citing works
“We hypothesize that this two-stage process of slow and fast learning could provide some intuition for off-and on-manifold brain-machine interface learning results in nonhuman primates 33–35 .”
BackgroundSavings upon Re-Aiming in Visuomotor Adaptation
Morehead, J. R., Qasim, S. E., Crossley, M. J., & Ivry, R. B. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1046-15.2015 · Cited by 215 · see citing works
Concepts and Compositionality: In Search of the Brain's Language of Thought
Frankland, S., & Greene, J. D. (2019) · Annual Review of Psychology · doi:10.1146/annurev-psych-122216-011829 · Cited by 184 · see citing works
From fixed points to chaos: Three models of delayed discrimination
Barak, O., Sussillo, D., Romo, R., Tsodyks, M. V., & Abbott, L. F. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.02.002 · Cited by 172 · see citing works
Self-citationHand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way
Willett, F. R., Deo, D. R., Avansino, D. T., Rezaii, P., Hochberg, L. R., Henderson, J. M., & Shenoy, K. V. (2020) · Cell · doi:10.1016/j.cell.2020.02.043 · Cited by 171 · see citing works
Self-citationReward-based training of recurrent neural networks for cognitive and value-based tasks
Song, H., Yang, G. R., & Wang, X. (2017) · eLife · doi:10.7554/elife.21492 · Cited by 169 · see citing works
Linguistic generalization and compositionality in modern artificial neural networks
Baroni, M. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0307 · Cited by 156 · see citing works
Compositionality of Rule Representations in Human Prefrontal Cortex
Reverberi, C., Görgen, K., & Haynes, J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr200 · Cited by 139 · see citing works
Carnevale, F., de Lafuente, V., Romo, R., Barak, O., & Parga, N. (2015) · Neuron · doi:10.1016/j.neuron.2015.04.014 · Cited by 119 · see citing works
Shaping dynamics with multiple populations in low-rank recurrent networks
Beiran, M., Dubreuil, A., Valente, A., Mastrogiuseppe, F., & Ostojic, S. (2021) · Neural Computation · doi:10.1162/neco_a_01381 · Cited by 92 · see citing works
Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition
Wang, X.-J. (2022) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-110920-035434 · Cited by 83 · see citing works
Saxena, S., Russo, A. A., Cunningham, J. P., & Churchland, M. M. (2022) · eLife · doi:10.7554/elife.67620 · Cited by 82 · see citing works
Computational models in the age of large datasets
O’Leary, T., Sutton, A. C., & Marder, E. E. (2015) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.01.006 · Cited by 80 · see citing works
Universality and individuality in neural dynamics across large populations of recurrent networks
Maheswaranathan, N., Williams, A. H., Golub, M. D., Ganguli, S., & Sussillo, D. (2019) · PubMed · doi:10.48550/arxiv.1907.08549 · Cited by 72 · see citing works
Self-citationModularity and robustness of frontal cortical networks
Chen, G., Kang, B., Lindsey, J. W., Druckmann, S., & Li, N. (2021) · Cell · doi:10.1016/j.cell.2021.05.026 · Cited by 65 · see citing works
Shaping Dynamics With Multiple Populations in Low-Rank Recurrent Networks
Beiran, M., Dubreuil, A., Valente, A., Mastrogiuseppe, F., & Ostojic, S. (2021) · UCL Discovery (University College London) · Cited by 58 · see citing works
Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems
Jordan, I. D., Sokół, P., & Park, I. M. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.678158 · Cited by 56 · see citing works
How to study the neural mechanisms of multiple tasks
Yang, G. R., Cole, M. W., & Rajan, K. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.07.001 · Cited by 54 · see citing works
Goudar, V., Peysakhovich, B., Freedman, D. J., Buffalo, E. A., & Wang, X. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01293-9 · Cited by 49 · see citing works
Organizing recurrent network dynamics by task-computation to enable continual learning
Duncker, L., Driscoll, L. N., Shenoy, K. V., Sahani, M., & Sussillo, D. (2020) · UCL Discovery (University College London) · Cited by 45 · see citing works
Self-citationResidual dynamics resolves recurrent contributions to neural computation
Galgali, A. R., Sahani, M., & Mante, V. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01230-2 · Cited by 43 · see citing works
A Dataset and Architecture for Visual Reasoning with a Working Memory
Yang, G. R., Ganichev, I., Wang, X., Shlens, J., & Sussillo, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_44 · Cited by 43 · see citing works
Self-citationNatural language instructions induce compositional generalization in networks of neurons
Riveland, R., & Pouget, A. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01607-5 · Cited by 39 · see citing works
Golub, M. D., & Sussillo, D. (2018) · The Journal of Open Source Software · doi:10.21105/joss.01003 · Cited by 37 · see citing works
Self-citationCognitive experience alters cortical involvement in goal-directed navigation
Arlt, C., Barroso-Luque, R., Kira, S., Bruno, C. A., Xia, N., Chettih, S. N., Soares, S., Pettit, N. L., & Harvey, C. D. (2022) · eLife · doi:10.7554/elife.76051 · Cited by 33 · see citing works
Recurrent networks endowed with structural priors explain suboptimal animal behavior
Molano‐Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & de la Rocha, J. (2023) · Current Biology · doi:10.1016/j.cub.2022.12.044 · Cited by 32 · see citing works
Low-dimensional encoding of decisions in parietal cortex reflects long-term training history
Latimer, K. W., & Freedman, D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-36554-5 · Cited by 25 · see citing works
Pagan, M., Tang, V. D., Aoi, M., Pillow, J. W., Mante, V., Sussillo, D., & Brody, C. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.28.518207 · Cited by 21 · see citing works
Self-citationOstrow, M., Eisen, A. J., Kozachkov, L., & Fiete, I. (2023) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2023.1356-0 · Cited by 13 · see citing works
FROM CONTINUOUS DYNAMICS TO SYMBOLS
JAEGER, H. (1999) · Studies of nonlinear phenomena in life science · doi:10.1142/9789812815354_0003 · Cited by 11 · see citing works
Codimension-2 parameter space structure of continuous-time recurrent neural networks
Beer, R. D. (2022) · Biological Cybernetics · doi:10.1007/s00422-022-00938-5 · Cited by 11 · see citing works
Ecologically pre-trained RNNs explain suboptimal animal decisionsPreprint
Molano‐Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & de la Rocha, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.15.444287 · Cited by 6 · see citing works
Low-dimensional encoding of decisions in parietal cortex reflects long-term training historyPreprint
Latimer, K. W., & Freedman, D. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.07.463576 · Cited by 5 · see citing works
Generalization in Sensorimotor Networks Configured with Natural Language InstructionsPreprint
Riveland, R., & Pouget, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.22.481293 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Bower, G. H.) vol. 24 109-165 (Academic Press, 1989).
- Goudar, V., Peysakhovich, B., Freedman, D. J. & Buffalo, E. A. Elucidating the neural mechanisms of Learning-to-Learn. bioRxiv (2021).
- Guangyu Robert Yang, Madhura R Joglekar, H Francis Song, William T Newsome, and Xiao-Jing Wang. Task representations in neural networks trained to perform many cognitive tasks. Nature neuroscience, 22(2):297, 2019.
- Matthew Golub and David Sussillo. Fixedpointfinder: A tensorflow toolbox for identifying and character- izing fixed points in recurrent neural networks. Journal of Open Source Software, 3(31):1003, 2018.
- Valerio Mante, David Sussillo, Krishna V Shenoy, and William T Newsome. Context-dependent computa- tion by recurrent dynamics in prefrontal cortex. nature, 503(7474):78-84, 2013.
Cited by
Sorted by citations
Reconstructing computational system dynamics from neural data with recurrent neural networks
Durstewitz, D., Koppe, G., & Thurm, M. I. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00740-7 · Cited by 133 · see citing works
Preparatory activity and the expansive null-space
Churchland, M. M., & Shenoy, K. V. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00796-z · Cited by 126 · see citing works
Self-citationA neural manifold view of the brain
Perich, M. G., Narain, D., & Gallego, J. Á. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02031-z · Cited by 104 · see citing works
Abstract representations emerge naturally in neural networks trained to perform multiple tasks
Johnston, W. J., & Fusi, S. (2023) · Nature Communications · doi:10.1038/s41467-023-36583-0 · Cited by 89 · see citing works
Gao, B., Yao, R., & Li, Y. (2025) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2025.01.007 · Cited by 62 · see citing works
Tasks and their role in visual neuroscience
Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J., & Barack, D. L. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.022 · Cited by 61 · see citing works
Direct neural perturbations reveal a dynamical mechanism for robust computationPreprint
O’Shea, D. J., Duncker, L., Goo, W., Sun, X., Vyas, S., Trautmann, E. M., Diester, I., Ramakrishnan, C., Deisseroth, K. A., Sahani, M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.16.520768 · Cited by 60 · see citing works
Self-citationLatent circuit inference from heterogeneous neural responses during cognitive tasks
Langdon, C., & Engel, T. A. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01869-7 · Cited by 49 · see citing works
Building compositional tasks with shared neural subspaces
Tafazoli, S., Bouchacourt, F., Ardalan, A., Markov, N. T., Uchimura, M., Mattar, M. G., Daw, N. D., & Buschman, T. J. (2025) · Nature · doi:10.1038/s41586-025-09805-2 · Cited by 53 · see citing works
Natural language instructions induce compositional generalization in networks of neurons
Riveland, R., & Pouget, A. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01607-5 · Cited by 39 · see citing works
Dynamical constraints on neural population activity
Oby, E. R., Degenhart, A. D., Grigsby, E. M., Motiwala, A., McClain, N. T., Marino, P. J., Yu, B. M., & Batista, A. P. (2025) · Nature Neuroscience · doi:10.1038/s41593-024-01845-7 · Cited by 43 · see citing works
Recent Advances at the Interface of Neuroscience and Artificial Neural Networks
Cohen, Y., Engel, T. A., Langdon, C., Lindsay, G. W., Ott, T., Peters, M. A. K., Shine, J. M., Breton‐Provencher, V., & Ramaswamy, S. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1503-22.2022 · Cited by 38 · see citing works
Kikumoto, A., Bhandari, A., Shibata, K., & Badre, D. (2024) · Nature Communications · doi:10.1038/s41467-024-52777-6 · Cited by 33 · see citing works
De novo motor learning creates structure in neural activity that shapes adaptation
Chang, J. C., Perich, M. G., Miller, L. E., Gallego, J. Á., & Claudia Clopath, A. (2024) · Nature Communications · doi:10.1038/s41467-024-48008-7 · Cited by 26 · see citing works
ROSE: A Universal Neural Grammar
Murphy, E. (2025) · Cognitive Neuroscience · doi:10.1080/17588928.2025.2523875 · Cited by 25 · see citing works
Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · eLife · doi:10.7554/elife.88591 · Cited by 23 · see citing works
Learning shapes neural geometry in the prefrontal cortexPreprint
Wójcik, M. J., Stroud, J. P., Wasmuht, D., Makoto Kusunoki, A., Kadohisa, M., Buckley, M. J., Costa, R. P., Myers, N. E., Hunt, L. T., Duncan, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.24.538054 · Cited by 21 · see citing works
When and why does motor preparation arise in recurrent neural network models of motor control?
Schimel, M., Kao, T.-C., & Hennequin, G. (2024) · eLife · doi:10.7554/elife.89131.4 · Cited by 18 · see citing works
Signatures of task learning in neural representations
Gurnani, H., & Cayco-Gajic, N. A. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102759 · Cited by 18 · see citing works
Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex
Shah, N. P., Avansino, D. T., Kamdar, F. B., Nicolas, C., Kapitonava, A., Vargas-Irwin, C. E., Hochberg, L. R., Pandarinath, C., Shenoy, K. V., Willett, F. R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59039-z · Cited by 17 · see citing works
Self-citationHippocampal ripple diversity organizes neuronal reactivation dynamics in the offline brain
Castelli, M., Lopes‐dos‐Santos, V., Gava, G. P., Lambiotte, R., & Dupret, D. (2025) · Neuron · doi:10.1016/j.neuron.2025.09.012 · Cited by 20 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
Low, I. I., Giocomo, L. M., & Williams, A. H. (2023) · eLife · doi:10.7554/elife.86943 · Cited by 15 · see citing works
Holton, E., Braun, L., Thompson, J. A. F., Grohn, J., & Summerfield, C. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02318-y · Cited by 17 · see citing works
Trained recurrent neural networks develop phase-locked limit cycles in a working memory task
Pals, M., Macke, J. H., & Barak, O. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011852 · Cited by 15 · see citing works
Sources of richness and ineffability for phenomenally conscious states
Ji, M. X., Elmoznino, E., Deane, G., Constant, A., Dumas, G., Lajoie, G., Simon, J., & Bengio, Y. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae001 · Cited by 17 · see citing works
Emergent neural dynamics and geometry for generalization in a transitive inference task
Kay, K., Biderman, N., Khajeh, R., Beirán, M., Cueva, C. J., Shohamy, D., Jensen, G., Wei, X.-X., Ferrera, V. P., & Abbott, L. F. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011954 · Cited by 14 · see citing works
Chaotic recurrent neural networks for brain modelling: A review
Mattera, A., Alfieri, V., Granato, G., & Baldassarre, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107079 · Cited by 17 · see citing works
Neural representation of action symbols in primate frontal cortex
Tian, L. Y., Gupta, K. G., Hanuska, D. J., Rouse, A. G., Eldridge, M. A. G., Schieber, M. H., Wang, X., Tenenbaum, J. B., & Freiwald, W. A. (2026) · Nature · doi:10.1038/s41586-026-10297-x · Cited by 14 · see citing works
Rethinking the centrality of brain areas in understanding functional organization
Hayden, B. Y., Heilbronner, S. R., & Yoo, S. B. M. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02166-z · Cited by 19 · see citing works
Using multi‐task experiments to test principles of hippocampal function
Han, C. Z., Donoghue, T., Cao, R., Kunz, L., Wang, S., & Jacobs, J. (2023) · Hippocampus · doi:10.1002/hipo.23540 · Cited by 13 · see citing works
Astrocytes as a mechanism for contextually-guided network dynamics and function
Gong, L., Pasqualetti, F., Papouin, T., & Ching, S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012186 · Cited by 15 · see citing works
Dynamic tracking of objects in the macaque dorsomedial frontal cortex
Rajalingham, R., Sohn, H., & Jazayeri, M. (2025) · Nature Communications · doi:10.1038/s41467-024-54688-y · Cited by 14 · see citing works
Motor cortex flexibly deploys a high-dimensional repertoire of subskillsPreprint
Amematsro, E. A., Trautmann, E. M., Marshall, N. J., Abbott, L. F., Shadlen, M. N., Wolpert, D. M., & Churchland, M. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.07.674717 · Cited by 15 · see citing works
Flow-field inference from neural data using deep recurrent networksPreprint
Kim, T. D., Luo, T. Z., Can, T., Krishnamurthy, K., Pillow, J. W., & Brody, C. D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.14.567136 · Cited by 12 · see citing works
Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanismsPreprint
Gao, R., Deistler, M., Schulz, A., Gonçalves, P. J., & Macke, J. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.21.608969 · Cited by 10 · see citing works
Considering Readout to Understand Working Memory
Myers, N. E. (2022) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01921 · Cited by 9 · see citing works
Feedback control of recurrent circuits imposes dynamical constraints on learningPreprint
Gurnani, H., Liu, W., & Brunton, B. W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.24.595772 · Cited by 10 · see citing works
Khona, M., Chandra, S., Ma, J. J., & Fiete, I. (2023) · Neural Computation · doi:10.1162/neco_a_01613 · Cited by 9 · see citing works
Interpretable deep learning for deconvolutional analysis of neural signals
Tolooshams, B., Matias, S., Wu, H., Temereanca, S., Uchida, N., Murthy, V. N., Masset, P., & Ba, D. E. (2024) · Neuron · doi:10.1016/j.neuron.2025.02.006 · Cited by 9 · see citing works
Han, J., Ma, D., Jiang, L., Zhang, Y., Dong, M., Deng, Y., Geng, Y., Huang, R., Xu, C., Zheng, X., et al. (2025) · Advanced Materials · doi:10.1002/adma.202514723 · Cited by 8 · see citing works
State and capacity in neural models of cognition and consciousness
Wang, P., Liu, H., Ilievski, F., Ganushchak, L., Zou, L., Laureys, S., & Paas, F. (2026) · Trends Open · doi:10.1016/j.treopn.2026.04.004 · Cited by 8 · see citing works
Cortical interneurons: fit for function and fit to function? Evidence from development and evolution
Keijser, J., & Sprekeler, H. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1172464 · Cited by 8 · see citing works
Contextual neural dynamics during time perception in the primate ventral premotor cortex
Díaz, H., Bayones, L., Álvarez, M., Andrade-Ortega, B., Valero, S., Zainos, A., Romo, R., & Rossi-Pool, R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2420356122 · Cited by 7 · see citing works
A posture subspace in the primary motor cortex
Marino, P. J., Bahureksa, L., Fisac, C. F., Oby, E. R., Smoulder, A. L., Motiwala, A., Degenhart, A. D., Grigsby, E. M., Joiner, W. M., Chase, S. M., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.07.030 · Cited by 8 · see citing works
Kikumoto, A., Bhandari, A., Shibata, K., & Badre, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.09.544428 · Cited by 8 · see citing works
Remapping in a recurrent neural network model of navigation and context inferencePreprint
Low, I. I. C., Giocomo, L. M., & Williams, A. H. (2023) · eLife · doi:10.7554/elife.86943.2 · Cited by 8 · see citing works
Hocker, D. L., Constantinople, C. M., & Savin, C. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01029-3 · Cited by 7 · see citing works
Mouille, A., Gaillard, C., Ästrand, E., Wardak, C., Hamed, S. B., & Hamed, S. B. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003353 · Cited by 7 · see citing works
Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks
Tolmachev, P., & Engel, T. A. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01127-2 · Cited by 7 · see citing works
Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks
Clark, D. G., Marschall, O., van Meegen, A., & Litwin-Kumar, A. (2025) · Physical Review X · doi:10.1103/2jt7-c8cq · Cited by 8 · see citing works
Practice reshapes the geometry and dynamics of task-tailored representations
Kikumoto, A., Shibata, K., Nishio, T., & Badre, D. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf125 · Cited by 8 · see citing works
Identifying Interpretable Latent Factors with Sparse Component AnalysisPreprint
Zimnik, A. J., Ames, K. C., An, X., Driscoll, L. N., Lara, A. H., Russo, A. A., Susoy, V., Cunningham, J. P., Paninski, L., Churchland, M. M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.05.578988 · Cited by 7 · see citing works
Self-citationBrain-like learning with exponentiated gradientsPreprint
Cornford, J. H., Pogodin, R., Ghosh, A., Sheng, K., Bicknell, B. A., Codol, O., Clark, B. A., Lajoie, G., & Richards, B. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.25.620272 · Cited by 7 · see citing works
Intelligent analysis algorithm for power engineering data based on improved BiLSTM
Xu, Y., Yang, J., & Cai, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-99409-7 · Cited by 6 · see citing works
What neuroscience can tell AI about learning in continuously changing environments
Durstewitz, D., Averbeck, B. B., & Koppe, G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01146-z · Cited by 6 · see citing works
Neural population geometry and optimal coding of tasks with shared latent structure
Wakhloo, A. J., Slatton, W., & Chung, S. (2026) · Nature Neuroscience · doi:10.1038/s41593-025-02183-y · Cited by 10 · see citing works
When and why does motor preparation arise in recurrent neural network models of motor control?
Schimel, M., Kao, T.-C., & Hennequin, G. (2023) · eLife · doi:10.7554/elife.89131 · Cited by 22 · see citing works
Brain-like neural dynamics for behavioral control develop through reinforcement learningPreprint
Codol, O., Krishna, N. H., Lajoie, G., & Perich, M. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.04.616712 · Cited by 7 · see citing works
Disinhibition of a recurrent attractor gates a persistent goal signal for navigationPreprint
Lanz, A. J., Kathman, N. D., Hao, E., Ermentrout, B., & Nagel, K. I. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.07.681003 · Cited by 6 · see citing works
Nair, A., Vinograd, A., Liu, M., Mountoufaris, G., Linderman, S. W., & Anderson, D. J. (2025) · Neuron · doi:10.1016/j.neuron.2025.11.003 · Cited by 5 · see citing works
Rarely categorical, highly separable representations along the cortical hierarchy
Posani, L., Wang, S., Muscinelli, S. P., Paninski, L., & Fusi, S. (2026) · Nature · doi:10.1038/s41586-026-10668-4 · Cited by 34 · see citing works
Structural knowledge: from brain to artificial intelligence
Yu, Y., Yan, Y., & Jin, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11270-2 · Cited by 6 · see citing works
Structured flexibility in recurrent neural networks via neuromodulation
Costacurta, J. C., Bhandarkar, S., Zoltowski, D. M., & Linderman, S. W. (2024) · neural information processing systems · doi:10.52202/079017-0062 · Cited by 6 · see citing works
Battista, A., Padoa‐Schioppa, C., & Wang, X.-J. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.010 · Cited by 6 · see citing works
Expressive architectures enhance interpretability of dynamics-based neural population models
Sedler, A. R., Versteeg, C., & Pandarinath, C. (2023) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.73987 · Cited by 5 · see citing works
When and why does motor preparation arise in recurrent neural network models of motor control?Preprint
Schimel, M., Kao, T.-C., & Hennequin, G. (2024) · bioRxiv · doi:10.1101/2023.04.03.535429 · Cited by 31 · see citing works
When and why does motor preparation arise in recurrent neural network models of motor control?Preprint
Schimel, M., Kao, T.-C., & Hennequin, G. (2023) · eLife · doi:10.7554/elife.89131.1 · Cited by 5 · see citing works
Feature interference as a neuronal basis for the behavioural cost of task uncertaintyPreprint
Xue, C., Markman, S. K., Chen, R., Kramer, L. E., & Cohen, M. R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.04.583375 · Cited by 5 · see citing works
Park, J., Holmes, C. D., & Snyder, L. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.25.640211 · Cited by 7 · see citing works
Modeling attention and binding in the brain through bidirectional recurrent gating
Salehi, S., Lei, J., Benjamin, A. S., Müller, K., & Kording, K. P. (2026) · Nature Communications · doi:10.1038/s41467-026-72146-9 · Cited by 6 · see citing works
How to be a realist about computational neuroscience
Williams, D. J. (2025) · Synthese · doi:10.1007/s11229-025-04952-5 · Cited by 4 · see citing works
Noradrenaline drives learning across scales of time and neurobiological organisation
O’Callaghan, C. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.07.003 · Cited by 6 · see citing works
Learning shapes neural geometry in the primate prefrontal cortex
Wójcik, M. J., Stroud, J. P., Wasmuht, D., Kusunoki, M., Kadohisa, M., Buckley, M. J., Costa, R. P., Myers, N. E., Hunt, L. T., Duncan, J., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02333-w · Cited by 28 · see citing works
Effects of noise and metabolic cost on cortical task representations
Stroud, J. P., Wójcik, M. J., Jensen, K. T., Makoto Kusunoki, A., Kadohisa, M., Buckley, M. J., Duncan, J., Stokes, M. G., & Lengyel, M. (2025) · eLife · doi:10.7554/elife.94961.2 · Cited by 4 · see citing works
Noel, J., Balzani, E., Savin, C., & Angelaki, D. E. (2024) · Nature Communications · doi:10.1038/s41467-024-50203-5 · Cited by 4 · see citing works
Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2024) · eLife · doi:10.7554/elife.88591.4 · Cited by 4 · see citing works
Soo, W. W. M., Goudar, V., & Wang, X.-J. (2023) · neural information processing systems · doi:10.52202/075280-1391 · Cited by 4 · see citing works
Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.17.528969 · Cited by 4 · see citing works
Codol, O., Michaels, J. A., Kashefi, M., Pruszynski, J. A., & Gribble, P. L. (2023) · eLife · doi:10.7554/elife.88591.1 · Cited by 4 · see citing works
Chericoni, A., Fine, J. M., Ismail, T. S., Salazar, G. D., Franch, M. C., Mickiewicz, E. A., Chavez, A. G., Bartoli, E., Paulo, D. L., Provenza, N. R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.17.643774 · Cited by 4 · see citing works
A geometric foundation for word meaning in the brainPreprint
Zhu, H., Franch, M. C., Mickiewicz, E. A., Belanger, J. L., Cowan, R. L., Katlowitz, K. A., Chavez, A. G. L., Chericoni, A., Paulo, D. L., Yan, X., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702241 · Cited by 4 · see citing works
Remapping in a recurrent neural network model of navigation and context inference
Low, I. I. C., Giocomo, L. M., & Williams, A. H. (2023) · doi:10.1101/2023.01.25.525596 · Cited by 3 · see citing works
Balanced state of networks of winner-take-all units
Pang, R. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013081 · Cited by 4 · see citing works
Osako, Y., Heller, G. R., Ährlund‐Richter, S., Buschman, T. J., & Sur, M. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02410-0 · Cited by 5 · see citing works
Delay-induced multiple firing resonance in a coupled neuronal motif
Tan, H., & Xian, S. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-10954-2 · Cited by 3 · see citing works
Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex
Bowler, J. C., Azhar, D., Jensen, C. M., Lee, H., & Heys, J. G. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02409-7 · Cited by 3 · see citing works
Dynamical motifs for computations in language
Labancová, K., & Kazanina, N. (2025) · Cognitive Neuroscience · doi:10.1080/17588928.2025.2581573 · Cited by 3 · see citing works
Cortical interneurons: fit for function and fit to function? Evidence from development and evolutionPreprint
Keijser, J., & Sprekeler, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.23.529671 · Cited by 3 · see citing works
Effects of noise and metabolic cost on cortical task representationsPreprint
Stroud, J. P., Wójcik, M. J., Jensen, K. T., Kusunoki, M., Kadohisa, M., Buckley, M. J., Duncan, J., Stokes, M. G., & Lengyel, M. (2024) · eLife · doi:10.7554/elife.94961 · Cited by 6 · see citing works
When and why modular representations emergePreprint
Johnston, W. J., & Fusi, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.30.615925 · Cited by 3 · see citing works
Attractor-based models for sequences and pattern generation in neural circuitsPreprint
Alvarez, J. L., Morrison, K., & Curto, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.642121 · Cited by 2 · see citing works
Neural dynamics hierarchy in motor cortex and striatum across naturalistic behaviorsPreprint
Xing, D., Glaser, J. I., & Miri, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.629049 · Cited by 8 · see citing works
Predictive learning enables compositional representationsPreprint
Boeshertz, G., & Claudia Clopath, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.26.678731 · Cited by 3 · see citing works
A theory of multi-task computation and task selectionPreprint
Marschall, O., Clark, D. G., & Litwin-Kumar, A. (2025) · eLife · doi:10.64898/2025.12.12.693832 · Cited by 3 · see citing works
Where physics and biology meet
Marshall, W. F., Baum, B., Fairhall, A. L., Heisenberg, C., Koslover, E. F., Liu, A. J., Mao, Y., Mogilner, A., Nelson, C. M., Paluch, E. K., et al. (2024) · Current Biology · doi:10.1016/j.cub.2024.08.022 · Cited by 2 · see citing works
Embodied Sensorimotor Control: Computational Modeling of the Neural Control of Movement
Almani, M. N., Lazzari, J., Walker, J. D., & Saxena, S. (2026) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-102723-020454 · Cited by 4 · see citing works
Distributionally robust geometric joint chance-constrained optimization: Neurodynamic approaches
Valli, A., Tassouli, S., & Lisser, A. (2026) · Computers & Operations Research · doi:10.1016/j.cor.2026.107484 · Cited by 2 · see citing works
Granule cells reorient cortical trajectories to separate contexts
Garcia-Garcia, M. G., Wójcik, M. J., Thota, S., Drake, L., Otchere, A., Akinwale, O., Ramos, L., Costa, R. P., & Wagner, M. J. (2026) · Nature · doi:10.1038/s41586-026-10946-1 · Cited by 2 · see citing works
Multitasking via baseline control in recurrent neural networks
Ogawa, S., Fumarola, F., & Mazzucato, L. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304394120 · Cited by 2 · see citing works
Transients versus network interactions give rise to multistability through trapping mechanism
Rossi, K. L., Medeiros, E. S., Ashwin, P., & Feudel, U. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0249997 · Cited by 2 · see citing works
- Authors
- Driscoll, L., Shenoy, K., & Sussillo, D.
- Affiliations
- Stanford University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Driscoll, Laura
- Venue
- Nature Neuroscience
- Publisher
- openRxiv
- Published
- 2022-08-15
- Volume
- 27
- Issue
- 7
- Pages
- 1349-1363
- ISSN
- 1097-6256 (ISSN-L)
- PMC
- PMC11239504
- OpenAlex
- W4400462656
- Cited by
- 217
- References
- 62
- Citations by year
- 2026: 78 · 2025: 58 · 2024: 45 · 2023: 31 · 2022: 4
- FWCI
- 26.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Nature Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Simons Foundation (543049, 1155867, 543045)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- recurrent neural networks, multi-task learning, modular computation, ring attractor, attractor dynamics, decision boundaries, transfer learning
- MeSH terms
- Algorithms, Animals, Learning, Models, Neurological, Nerve Net, Neurons, Neural Networks, Computer
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
