The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
Neural representation of time in cortico-basal ganglia circuits
Open accessAbstract
Encoding time is universally required for learning and structuring motor and cognitive actions, but how the brain keeps track of time is still not understood. We searched for time representations in cortico-basal ganglia circuits by recording from thousands of neurons in the prefrontal cortex and striatum of macaque monkeys performing a routine visuomotor task. We found that a subset of neurons exhibited time-stamp encoding strikingly similar to that required by models of reinforcement-based learning: They responded with spike activity peaks that were distributed at different time delays after single task events. Moreover, the temporal evolution of the population activity allowed robust decoding of task time by perceptron models. We suggest that time information can emerge as a byproduct of event coding in cortico-basal ganglia circuits and can serve as a critical infrastructure for behavioral learning and performance.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Consistent with previous studies23-25, we found that the activity of neurons varied substantially in different trial epochs (99% of neurons, 675/681, ANOVA p<0.05).”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [25]
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Self-organized formation of topographically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · Cited by 2,995
The role of the basal ganglia in habit formation
Yin, H. H., & Knowlton, B. J. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1919 · Cited by 2,402
Estimation of Dependences Based on Empirical Data
Vapnik, V. N. (2006) · Information science and statistics · doi:10.1007/0-387-34239-7 · Cited by 2,252
What makes us tick? Functional and neural mechanisms of interval timing
Buhusi, C. V., & Meck, W. H. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1764 · Cited by 2,152
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
Montague, P., Dayan, P., & Sejnowski, T. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-05-01936.1996 · Cited by 2,066
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- RS Sutton, AG Barto Learning and Computational Neuroscience, eds M Gabriet, JW Moore (MIT Press, Cambridge, MA), pp. 497–538 (1989).
Cited by
Sorted by citations
Items 101–200 of 245
Yin, B., Lusk, N. A., & Meck, W. H. (2017) · doi:10.1002/9781118540770.ch8 · Cited by 22
Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits
Calderon, C. B., Verguts, T., & Frank, M. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009854 · Cited by 21
Independent coding of absolute duration and distance magnitudes in the prefrontal cortex
Marcos, E., Tsujimoto, S., & Genovesio, A. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00245.2016 · Cited by 23
Estimating Scale-Invariant Future in Continuous Time
Tiganj, Z., Gershman, S. J., Sederberg, P. B., & Howard, M. W. (2019) · Neural Computation · doi:10.1162/neco_a_01171 · Cited by 21
Learning long-term motor timing/patterns on an orthogonal basis in random neural networks
Kawai, Y., Park, J., Tsuda, I., & Asada, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.04.006 · Cited by 20
Estimation of self-motion duration and distance in rodents
Kautzky, M., & Thurley, K. (2016) · Royal Society Open Science · doi:10.1098/rsos.160118 · Cited by 20
Berthet, P., Lindahl, M., Tully, P. J., Kotaleski, J. H., & Lansner, A. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00053 · Cited by 19
Contributions of Basal Ganglia Circuits to Perception, Attention, and Consciousness
Redinbaugh, M. J., & Saalmann, Y. B. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02177 · Cited by 20
Keeping time and rhythm by internal simulation of sensory stimuli and behavioral actions
de Lafuente, V., Jazayeri, M., Merchant, H., García‐Garibay, O., Cadena-Valencia, J., & Malagón, A. M. (2024) · Science Advances · doi:10.1126/sciadv.adh8185 · Cited by 26
Singh, I., Tiganj, Z., & Howard, M. W. (2018) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2018.04.008 · Cited by 17
Gu, B.-M., Kukreja, K., & Meck, W. H. (2018) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2018.05.003 · Cited by 22
Martel, A., & Apicella, P. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14741 · Cited by 17
Distinct Dynamics of Striatal and Prefrontal Neural Activity During Temporal Discrimination
Kim, J., Kim, D., & Jung, M. W. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00034 · Cited by 17
Palombo, D. J., Reid, A. G., Thavabalasingam, S., Hunsberger, R., Lee, A. C. H., & Verfaellie, M. H. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01489 · Cited by 17
Pitti, A., Quoy, M., Lavandier, C., & Boucenna, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.023 · Cited by 18
Neural population clocks: Encoding time in dynamic patterns of neural activity.
Zhou, S., & Buonomano, D. V. (2022) · Behavioral Neuroscience · doi:10.1037/bne0000515 · Cited by 18
Learned Timing of Motor Behavior in the Smooth Eye Movement Region of the Frontal Eye Fields
Li, J. X., & Lisberger, S. G. (2011) · Neuron · doi:10.1016/j.neuron.2010.11.043 · Cited by 15
Altered Perceptual Sensitivity to Kinematic Invariants in Parkinson's Disease
Dayan, E., Inzelberg, R., & Flash, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030369 · Cited by 15
Interference between Space and Time Estimations: From Behavior to Neurons
Marcos, E., & Genovesio, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00631 · Cited by 15
Compressed Timeline of Recent Experience in Monkey lPFCPreprint
Tiganj, Z., Cromer, J. A., Roy, J. E., Miller, E. K., & Howard, M. W. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3155928 · Cited by 15
Routing states transition during oscillatory bursts and attentional selection
Boroujeni, K. B., & Womelsdorf, T. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.012 · Cited by 16
Neural mechanisms of human temporal fear conditioning
Harnett, N. G., Shumen, J. R., Wagle, P. A., Wood, K. H., Wheelock, M. D., Baños, J. H., & Knight, D. C. (2016) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2016.09.019 · Cited by 14
Baldi, P., Alhassen, W., Chen, S., Nguyen, H. N., Khoudari, M., & Alachkar, A. (2021) · Journal of Neuroscience Research · doi:10.1002/jnr.24919 · Cited by 12
Medial entorhinal cortex mediates learning of context-dependent interval timing behavior
Bigus, E. R., Lee, H., Bowler, J. C., Shi, J., & Heys, J. G. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01683-7 · Cited by 20
Control Principles of Neural Dynamics Revealed by the Neurobiology of Timing
Stine, G. M., & Jazayeri, M. (2025) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-091724-015512 · Cited by 12
Contribution of cerebellar loops to action timing
Raghavan, R. T., Prevosto, V., & Sommer, M. A. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.01.008 · Cited by 14
Context-Dependent Duration Signals in the Primate Prefrontal Cortex
Genovesio, A., Seitz, L. K., Tsujimoto, S., & Wise, S. P. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv156 · Cited by 13
Magnitude Estimation with Noisy Integrators Linked by an Adaptive Reference
Thurley, K. (2016) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2016.00006 · Cited by 12
Diverse Time Encoding Strategies Within the Medial Premotor Areas of the Primate
Merchant, H., Mendoza, G., Pérez, O., Betancourt, A., García-Saldivar, P., & Prado, L. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-60183-5_7 · Cited by 12
Interval Timing as a Computational Pathway From Early Life Adversity to Affective Disorders
Harhen, N. C., & Bornstein, A. M. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12701 · Cited by 15
Buonomano, D. V., & Laje, R. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-385948-8.00006-2 · Cited by 14
Isolating Event-related Neuronal Responses by Deconvolution
Ghazizadeh, A., Fields, H. L., & Ambroggi, F. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00389.2010 · Cited by 12
Re-evaluating rhythmic attentional switching: Spurious oscillations from shuffling-in-timePreprint
Brookshire, G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.07.443101 · Cited by 12
Generation of Scale-Invariant Sequential Activity in Linear Recurrent Networks
Liu, Y., & Howard, M. W. (2020) · Neural Computation · doi:10.1162/neco_a_01288 · Cited by 11
The influence of emotion on temporal context models
Lohnas, L. J., & Howard, M. W. (2024) · Cognition & Emotion · doi:10.1080/02699931.2024.2371075 · Cited by 14
Sources of bias and uncertainty in a visual temporal individuation task
Martini, P. (2012) · Attention Perception & Psychophysics · doi:10.3758/s13414-012-0384-y · Cited by 11
Cilia in the Striatum Mediate Timing-Dependent Functions
Alhassen, W., Alhassen, S., Chen, J., Monfared, R. V., & Alachkar, A. (2022) · Molecular Neurobiology · doi:10.1007/s12035-022-03095-9 · Cited by 12
Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning
Affan, R. O., Bright, I. M., Pemberton, L. N., Cruzado, N. A., Scott, B. B., & Howard, M. W. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00234.2024 · Cited by 12
Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals
Shinomoto, S., Omi, T., Mita, A., Mushiake, H., Shima, K., Matsuzaka, Y., & Tanji, J. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00029 · Cited by 11
Evidence Accumulation in a Laplace Domain Decision Space
Howard, M. W., Luzardo, A., & Tiganj, Z. (2018) · Computational Brain & Behavior · doi:10.1007/s42113-018-0016-2 · Cited by 13
A subcortical circuit for time and action: insights from animal research
Doyère, V., & Massioui, N. E. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.02.008 · Cited by 10
Scanning a compressed ordered representation of the future.
Tiganj, Z., Singh, I., Esfahani, Z. G., & Howard, M. W. (2022) · Journal of Experimental Psychology General · doi:10.1037/xge0001243 · Cited by 9
A Population-Based Model of the Temporal Memory in the Hippocampus
Oprisan, S. A., Buhusi, M. C., & Buhusi, C. V. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00521 · Cited by 11
Martinez, R. H., Lansner, A., & Herman, P. A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220161 · Cited by 11
From Duration and Distance Comparisons to Goal Encoding in Prefrontal Cortex
Genovesio, A., & Tsujimoto, S. (2014) · Advances in experimental medicine and biology · doi:10.1007/978-1-4939-1782-2_10 · Cited by 10
Parker, N. F., Baidya, A., Cox, J., Haetzel, L. M., Zhukovskaya, A., Murugan, M., Engelhard, B., Goldman, M. S., & Witten, I. B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/725382 · Cited by 10
Cognitive computation using neural representations of time and space in the Laplace domainPreprint
Howard, M. W., & Hasselmo, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11668 · Cited by 19
Caudate Nucleus in Retrieval of Trace Eyeblink Conditioning after Consolidation
Flores, L. C., & Disterhoft, J. F. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2326-12.2013 · Cited by 9
Ramping activity in the striatum
Ponzi, A. P. D., & Wickens, J. R. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.902741 · Cited by 10
Tort, A. B. L., Komorowski, R. W., Kopell, N., & Eichenbaum, H. (2011) · Learning & Memory · doi:10.1101/lm.2307711 · Cited by 8
Anti-Hebbian plasticity drives sequence learning in striatum
Vignoud, G., Venance, L., & Touboul, J. D. (2024) · Communications Biology · doi:10.1038/s42003-024-06203-8 · Cited by 7
Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds
Howard, M. W., Esfahani, Z. G., Le, B., & Sederberg, P. B. (2024) · Computational Brain & Behavior · doi:10.1007/s42113-024-00230-8 · Cited by 11
The brain and the subjective experience of time. A voxel based symptom-lesion mapping study
Trojano, L., Caccavale, M., Bellis, F. D., & Crisci, C. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.04.031 · Cited by 7
The Fast Spiking Subpopulation of Striatal Neurons Coding for Temporal Cognition of Movements
Shen, B., Wang, Z., & Wang, X. (2017) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2017.00406 · Cited by 8
Parietal Cortex Signals Come Unstuck in Time
Cook, E. P., & Pack, C. C. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001414 · Cited by 7
Keeping time and rhythm by replaying a sensory-motor engramPreprint
de Lafuente, V., Jazayeri, M., Merchant, H., Gracía-Garibay, O., Cadena-Valencia, J., & Malagón, A. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.03.474812 · Cited by 7
Huang, C., Lin, C., Lin, Y., Tsai, H.-Y., & Tseng, M. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab301 · Cited by 6
Oscillation/Coincidence-Detection Models of Reward-Related Timing in Corticostriatal Circuits
Yin, B., Shi, Z., Wang, Y., & Meck, W. H. (2022) · Timing & Time Perception · doi:10.1163/22134468-bja10057 · Cited by 7
The dorsolateral striatum encodes a temporal basis for the organization of behavior
Rodrigues, F. S., Monteiro, T., Motiwala, A., & Paton, J. J. (2024) · Neuron · doi:10.1016/j.neuron.2024.10.022 · Cited by 5
Reinforcement learning and dopamine in the striatum: A modeling perspective
Wanjerkhede, S. M., Bapi, R. S., & Mytri, V. D. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.02.061 · Cited by 5
The Contribution of Premotor Cortico-Striatal Projections to the Execution of Serial Order Sequences
Sánchez-Fuentes, A., Ramírez-Armenta, K. I., Verma-Rodríguez, A. K., Díaz-Hernández, E., Aguilar-Palomares, A., Ramírez‐Jarquín, J. O., & Tecuapetla, F. (2021) · eNeuro · doi:10.1523/eneuro.0173-21.2021 · Cited by 6
Control of Functional Connectivity in Cerebral Cortex by Basal Ganglia Mediated SynchronizationPreprint
Pouzzner, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00779 · Cited by 6
Time, Control, and the Nervous System
Haimerl, C., Rodrigues, F. S., & Paton, J. J. (2025) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-112723-025348 · Cited by 6
Parkinson bradykinesia correlates with EEG background frequency and perceptual forward projection
Beudel, M., Roosma, E., Manzanera, O. E. M., van Laar, T., Maurits, N. M., & de Jong, B. M. (2015) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2015.05.004 · Cited by 5
Learning Temporal Intervals in Neural Dynamics
Durán, B., & Sandamirskaya, Y. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2676839 · Cited by 5
Locally distributed abstraction of temporal distance in human parietal cortexPreprint
Ye, Q., Hu, Y., Ku, Y., Appiah, K., & Kwok, S. C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/249904 · Cited by 5
Bigus, E. R., Lee, H., Bowler, J. C., Shi, J., & Heys, J. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.18.524598 · Cited by 5
Centanino, V., Fortunato, G., & Bueti, D. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003704 · Cited by 4
Welcome, M. O., & Pereverzev, V. A. (2013) · InTech eBooks · doi:10.5772/54284 · Cited by 4
Avila, E., Flierman, N. A., Holland, P. J., Roelfsema, P. R., Frens, M. A., Badura, A., & Zeeuw, C. I. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.26.437236 · Cited by 4
A neuronal population clock for interval timing in Drosophila
Kropf, J., Talbot, C. B., & Miesenböck, G. (2026) · Current Biology · doi:10.1016/j.cub.2025.12.032 · Cited by 3
Continuous Attractor Networks for Laplace Neural Manifolds
Daniels, B. C., & Howard, M. W. (2025) · Computational Brain & Behavior · doi:10.1007/s42113-024-00234-4 · Cited by 7
Independence and coherence in temporal sequence computation across the fronto-parietal network
Imamura, H., Imamura, F., Hira, R., Isomura, Y., & Hira, R. (2026) · Nature Communications · doi:10.1038/s41467-026-73999-w · Cited by 5
Ferguson, L. A., Matamales, M., Nolan, C. R., Balleine, B. W., & Bertran‐Gonzalez, J. (2024) · iScience · doi:10.1016/j.isci.2024.109274 · Cited by 3
Tread softly and carry a clock's tick
Paton, J. J., & Lau, B. (2015) · Nature Neuroscience · doi:10.1038/nn.3959 · Cited by 3
Human Processing of Short Temporal Intervals as Revealed by an ERP Waveform Analysis
Nakajima, Y., & Takeichi, H. (2011) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2011.00074 · Cited by 3
Vn, D., Kozlov, M. K., & Ea, C. (2015) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-015-0216-3 · Cited by 3
Bonardi, C., Cheung, T. H. C., Mondragón, E., & Tam, S. K. E. (2016) · Wiley eBooks · doi:10.1002/9781118650813.ch14 · Cited by 3
Different time encoding strategies within the medial premotor areas of the primatePreprint
Merchant, H., Mendoza, G., Pérez, O., Betancourt, A., García-Saldivar, P., & Prado, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.28.526038 · Cited by 3
A model for cortical activity sequencesPreprint
Lehr, A. B., Erzmann, F., Michaelis, C., Nowak, J., Gail, A., Kumar, A., & Tetzlaff, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.25.581959 · Cited by 3
Pinto-Correia, B., Caldeira-Bernardo, P., & Remondes, M. (2024) · eLife · doi:10.7554/elife.96515 · Cited by 3
Area-specific encoding of temporal information in the neocortex
Garvert, A. C., Bieler, M., Witoelar, A., & Vervaeke, K. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115363 · Cited by 2
Marková, L., Bareš, M., Lungu, O., & Filip, P. (2020) · The Cerebellum · doi:10.1007/s12311-019-01100-x · Cited by 2
Liu, M., Huang, G., Zhao, K., & Fu, X. (2023) · Neuroscience · doi:10.1016/j.neuroscience.2023.02.019 · Cited by 2
Pinto-Correia, B., Caldeira-Bernardo, P., & Remondes, M. (2025) · eLife · doi:10.7554/elife.96515.3 · Cited by 2
Jiang, H., Cai, J., Santos-Pata, D., Zhu, X., Deng, Z., Li, C., Luo, R., Shi, L., Cai, Y., Wang, R., et al. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1223-23.2025 · Cited by 3
Martel, A., & Apicella, P. (2024) · Physiological Reports · doi:10.14814/phy2.70037 · Cited by 2
Salcman, M. (2011) · Neurosurgery · doi:10.1227/neu.0b013e3182283425 · Cited by 2
Gigi, I., Senatore, R., & Marcelli, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.24.461656 · Cited by 2
Singh, I., Tiganj, Z., & Howard, M. W. (2018) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/rdyz2 · Cited by 1
Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes
Wernecke, H. O., Lehr, A. B., & Kumar, A. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1506-25.2026 · Cited by 1
The inhibitory network of the striatum at the edge of chaos
Ponzi, A. P. D., & Wickens, J. R. (2013) · BMC Neuroscience · doi:10.1186/1471-2202-14-s1-o19 · Cited by 1
Possible mechanisms for the effects of neuromodulators on the perception of time intervals
Silkis, I. G. (2012) · Neurochemical Journal · doi:10.1134/s1819712412020109 · Cited by 1
How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules
Zhang, K. (2014) · Neural Computation · doi:10.1162/neco_a_00618 · Cited by 1
Reward Processing Alters the Time Perception Networks in Patients with Major Depressive Disorder
Altınok, S., Vatansever, G., Apaydın, N., Üstün, S., Kale, E. H., Çelikağ, İ., Devrimci-Özgüven, H., Başkak, B., & Çíçek, M. (2023) · Timing & Time Perception · doi:10.1163/22134468-bja10073 · Cited by 1
Estimating Time and Rhythm by Predicting External Stimuli
Salgado-Ménez, M., Espinoza-Monroy, M., Malagón, A. M., Mercado, K., & de Lafuente, V. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-60183-5_9 · Cited by 1
Deep Reinforcement Learning with Time-Scale Invariant Memory
Kabir, M. R., Mochizuki-Freeman, J., & Tiganj, Z. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i2.32124 · Cited by 2
Sanchez-Vazquez, C., Ávila-Costa, M. R., & Cervantes-Pérez, F. (2014) · Journal of Applied Research and Technology · doi:10.1016/s1665-6423(14)71636-0 · Cited by 1
Cannabinoids and the cerebellum: a potential role in the development of psychosis
Skosnik, P. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511706080.014 · Cited by 1
An investigation of the neural mechanisms of interval timing behaviour
Torres, L. V. (2012) · Nottingham ePrints (University of Nottingham) · Cited by 1
Metadata
- Authors
- Jin, D. Z., Fujii, N., & Graybiel, A. M.
- Affiliations
- Pennsylvania State University (US)
- RIKEN Center for Brain Science (JP)
- McGovern Institute for Brain Research (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-11-10
- Volume
- 106
- Issue
- 45
- Pages
- 19156-19161
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2776432
- OpenAlex
- W2156530149
- Cited by
- 246
- References
- 49
- Citations by year
- 2026: 13 · 2025: 11 · 2024: 20 · 2023: 8 · 2022: 16 · 2021: 11 · 2020: 16 · 2019: 14 · 2018: 20 · 2017: 12 · 2016: 17 · 2015: 14 · 2014: 20 · 2013: 11 · 2012: 18
- FWCI
- 6.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Eye Institute (EY12848, R01EY012848)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- cortico-basal ganglia circuits, prefrontal cortex, striatum, reinforcement learning, macaque, visuomotor task, event coding
- MeSH terms
- Algorithms, Animals, Basal Ganglia, Brain Mapping, Electrophysiology, Macaca mulatta, Models, Psychological, Neural Pathways, Psychomotor Performance, Saccades, Time, Prefrontal Cortex
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