The DOI matches its Crossref record; 106 of the 107 references it cites were found in a registry or an index.
The cortex as a central pattern generator
Abstract
Vertebrate spinal cord and brainstem central pattern generator (CPG) circuits share profound similarities with neocortical circuits. CPGs can produce meaningful functional output in the absence of sensory inputs. Neocortical circuits could be considered analogous to CPGs as they have rich spontaneous dynamics that, similar to CPGs, are powerfully modulated or engaged by sensory inputs, but can also generate output in their absence. We find compelling evidence for this argument at the anatomical, biophysical, developmental, dynamic and pathological levels of analysis. Although it is possible that cortical circuits are particularly plastic types of CPG ('learning CPGs'), we argue that present knowledge about CPGs is likely to foretell the basic principles of the organization and dynamic function of cortical circuits.
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
“Yuste and colleagues recently suggested that the wiring diagram of the neocortical network is analogous to pattern generator circuits [218]”
From Consensus Paper: Pathological Role of the Cerebellum in Autism (2012) · cites this paper as [218]
In the citing paper
“Even within the cortex itself, microcircuitry within different areas may be specialized: tinkered variations on a common ancestral microcircuit scaffold could potentially allow different cortical areas, such as sensory areas vs. prefrontal areas, to be configured to adopt a number of qualitatively distinct computational and learning configurations (Yuste et al., 2005; Marcus et al., 2014a,b), even”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Yuste et al., 2005
In the citing paper
“Central pattern generator circuits provide an example of the kinds of architectures that can be pre-wired into neural microcircuitry, and may have evolutionary relationships with cortical circuits (Yuste et al., 2005).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Yuste et al., 2005
Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,150
Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
The Integrative Action of the Nervous System.
Sherrington, C. (1907) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-190734120-00021 · Cited by 4,136
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
The Geometry of Biological Time
Winfree, A. T. (1980) · doi:10.1007/978-3-662-22492-2 · Cited by 3,613
Pathology of Tumours of the Nervous System
(1990) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/00005072-199001000-00011 · Cited by 3,581
The Synaptic Organization of the Brain
(2004) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195159561.001.1 · Cited by 3,574
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
Flagged first, then by citations
1 of the 107 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Sherrington, C. S. Inhibition as a Coordinative Factor (Nobel Lecture) [online] http://nobelprize.org/medicine/laureates/1932/sherrington-lecture.html (12 December 1932).
- Smith, J. C. in Neurons, Networks, and Motor Behavior (eds Stein, P. S. G., Grillner, S., Selverston, A. I. & Stuart, D. G.) 97–104 (MIT Press, Cambridge, Massachusetts, 1997).
- Ramón y Cajal, S. La Textura del Sistema Nerviosa del Hombre y los Vertebrados 1st edn (Moya, Madrid, 1899).
- Lorente de Nó, R. La corteza cerebral del ratón. Trab. Lab. Invest. Bio. (Madrid) 20, 41–78 (1922).
- Lorente de Nó, R. Studies on the structure of the cerebral cortex. J. Psychol. Neurol. 45, 381–438 (1932).
- Hebb, D. O. The Organization of Behaviour (Wiley, New York, 1949).
Cited by
Sorted by citations
Items 201–300 of 353
A synergistic view of autonomous cognitive systems
Minai, A. A., Perdoor, M. C., Byadarhaly, K. V., Vasa, S., & Iyer, L. (2010) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2010.5596356 · Cited by 11
Ayali, A., Couzin-Fuchs, E., Ben‐Jacob, E., & Cohen, A. H. (2007) · Neuroscience · doi:10.1016/j.neuroscience.2007.04.014 · Cited by 11
Toma, A. I., Dima, V., Alexe, A., Rusu, L., Nemeş, A. F., Gonț, B. F., Arghirescu, A., Necula, A. I., Fieraru, A., & Stoiciu, R. P. (2023) · Life · doi:10.3390/life14010046 · Cited by 10
Mutual Information and Information Gating in Synfire Chains
Xiao, Z.-C., Wang, B., Sornborger, A. T., & Tao, L. (2018) · Entropy · doi:10.3390/e20020102 · Cited by 9
Miskovic, V., Lynn, S. J., Bagg, J. O., Pouliot, J. J., Ríos, M., & Wackermann, J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.01.014 · Cited by 12
Geometric and functional architecture of visceral sensory microcircuitry
Negishi, Y., & Kawai, Y. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0294-5 · Cited by 10
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
Zhang, X., Xiao, Z., Zhang, Q., & Pan, W. (2024) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10886438 · Cited by 13
Multiple states in ongoing neural activity in the rat visual cortex
Konno, D., Nishimoto, S., Suzuki, T., Ikegaya, Y., & Matsumoto, N. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256791 · Cited by 9
Ngouonkadi, E. B. M., Fotsin, H. B., & Louodop, P. (2014) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127414500692 · Cited by 9
Red neural por defecto y enfermedad de Alzheimer
Fernando Vergara, E., & Behrens, M. I. (2013) · Revista médica de Chile · doi:10.4067/s0034-98872013000300014 · Cited by 9
Yao, J., Hou, R., Fan, H., Liu, J., Chen, Z., Hou, J., Cheng, Q., & Li, C. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113756 · Cited by 7
Diversity of Up-State Voltage Transitions During Different Network States
Vautrelle, N., Carrillo‐Reid, L., & Bargas, J. (2008) · Humana Press eBooks · doi:10.1007/978-1-60327-252-0_5 · Cited by 8
Winlove, C. I. P., & Roberts, A. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00779.2010 · Cited by 8
Koh, N., Ma, Z., Sarup, A., Kristl, A. C., Agrios, M., Young, M., & Miri, A. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02093-z · Cited by 11
Learning and encoding motor primitives for limb actions in a brain-like computation approach
Sun, Y., Shi, H., & Wang, F. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.051 · Cited by 7
Long-Term Effects of Early Life Seizures on Endogenous Local Network Activity of the Mouse Neocortex
Rigas, P., Sigalas, C., Nikita, M., Kaplanian, A., Armaos, K., Leontiadis, L. J., Zlatanos, C., Kapogiannatou, A., Peta, C., Katri, A., et al. (2018) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2018.00043 · Cited by 8
Synaptic delays shape dynamics and function in multimodal neural motifs
Qie, X., Zang, J., Liu, S., & SHILNIKOV, A. L. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0233640 · Cited by 6
History of the Application of the Phase Resetting Curve to Neurons Coupled in a Pulsatile Manner
Canavier, C. C., & Achuthan, S. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0739-3_4 · Cited by 6
Fate of Duplicated Neural Structures
Seoane, L. F. (2020) · Entropy · doi:10.3390/e22090928 · Cited by 7
Deep Intelligence: What AI Should Learn from Nature’s Imagination
Minai, A. A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10124-9 · Cited by 5
Effect of Mastication on Human Brain Activity
Sakamoto, K., Nakata, H., Yumoto, M., & Kakigi, R. (2010) · Anti-aging Medicine · doi:10.3793/jaam.7.153 · Cited by 3
Learning Robot Control Using a Hierarchical SOM-Based Encoding
Pierris, G., & Dahl, T. S. (2017) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2657744 · Cited by 5
Modeling and analysis of a new locomotion control neural networks
Liu, Q., & Wang, J. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0758-x · Cited by 6
Online estimation of biophysical neural networks.Preprint
Burghi, T. B., & Sepulchre, R. J. (2021) · arXiv (Cornell University) · Cited by 5
Kumar, R., Hp, K., Sporn, K., Salman, S. G., Phadke, R. A., Sarnala, S., Vaja, S., Lee, N. J., & Lee, N. J. (2026) · European Spine Journal · doi:10.1007/s00586-026-10215-9 · Cited by 4
Kim, J., Gilbert, E. T., Arndt, K. C., Huang, H., Oleniacz, P. W., Jiang, S., Kimbrough, I. F., Sontheimer, H. W., English, D. F., & Jia, X. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116696 · Cited by 4
Brain Rhythms Provide a Framework for Neural Syntax
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0006 · Cited by 3
Optical Imaging of Neurons in the Crab Stomatogastric Ganglion with Voltage-sensitive Dyes
Stein, W., Städele, C., & András, P. (2011) · Journal of Visualized Experiments · doi:10.3791/2567 · Cited by 4
Traub, R. D., Whittington, M. A., & Hall, S. P. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00078 · Cited by 4
Nazhestkin, I. A., & Svarnik, О. E. (2022) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2105128 · Cited by 4
Intrinsic Activity and Consciousness
Raichle, M. E. (2011) · Research and perspectives in neurosciences · doi:10.1007/978-3-642-18015-6_9 · Cited by 4
Learning in chaotic recurrent neural networks
Abbott, L. F., & Sussillo, D. (2009) · Cited by 4
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake RobotPreprint
Liu, X., Gasoto, R., Onal, C. D., & Fu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04059 · Cited by 5
Modeling and control of swimming gaits for fish-like robots using coupled nonlinear oscillators
Zhou, C. (2012) · doi:10.32657/10356/48049 · Cited by 4
Singh, A. K., Sridharan, K. S., Wu, S. S. X., Tirosh, O., & Raghavan, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110270 · Cited by 3
Electric-field-coupled oscillators for collective electrochemical perception in biohybrid robotics
Kernbach, S. (2022) · Bioinspiration & Biomimetics · doi:10.1088/1748-3190/ac93d8 · Cited by 3
PhaseTrack: Physics-Based Motion Tracking via Phase-Guided Motion Generation
Zheng, R., Mou, C., & Hu, R. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104333 · Cited by 3
Experimental assessment of gait with rhythmic auditory perturbations
Forner‐Cordero, A., Itiki, C., de Souza, R. S., Lourenço, J. C., & Krebs, H. I. (2014) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2014.6913790 · Cited by 3
Blocking NK1 receptors disrupts the sequential and temporal organization of chain grooming in rats
Favila, N., GURNEY, K. N., & Overton, P. G. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108716 · Cited by 3
van Dijk, D., & van der Velde, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.113 · Cited by 4
Comparative studies provide evidence for neural reuse
Katz, P. S. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10001032 · Cited by 3
Visualizing search behavior with adaptive discriminations
Cook, R. G., & Qadri, M. A. J. (2013) · Behavioural Processes · doi:10.1016/j.beproc.2013.12.007 · Cited by 3
Barking seizure: Acute episodes of barking in a 75-year-old previously healthy man
Harandi, A. A., Kalanie, H., Asadollahi, M., Fatehi, F., Pakdaman, H., & Gharagozli, K. (2012) · Seizure · doi:10.1016/j.seizure.2012.02.003 · Cited by 3
Achuthan, S., Chandrasekaran, L., & Canavier, C. C. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0739-3_18 · Cited by 3
Effect of muscle relaxation in the foot on simultaneous muscle contraction in the contralateral hand
Kato, K., & Kanosue, K. (2016) · Neuroscience Letters · doi:10.1016/j.neulet.2016.09.019 · Cited by 3
Lake, J. (2017) · Cited by 3
Visual cortex neurons phase-lock selectively to subsets of LFP oscillations
Swindale, N. V., & Spacek, M. A. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00496.2018 · Cited by 3
Pacemaker and Network Mechanisms of Neural Rhythm Generation
Nowotny, T., & Mikhail, I. (2011) · InTech eBooks · doi:10.5772/13252 · Cited by 3
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0001 · Cited by 4
Arousal gating motor events in NFLE – A window on the boundary between physiology and pathology
Terzaghi, M., & Manni, R. (2012) · Sleep Medicine · doi:10.1016/j.sleep.2012.01.007 · Cited by 3
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
Bernert, A., Rühl, P., Schönherr, R., & Heinemann, S. H. (2026) · Molecular Brain · doi:10.1186/s13041-026-01279-1 · Cited by 2
Corte, L. F. L. (2008) · Occupational Therapy in Mental Health · doi:10.1080/01642120802257764 · Cited by 2
Berbel, B., Latorre, R., & Varona, P. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127378 · Cited by 2
A segmented motion synthesis method for robotic task-oriented locomotion imitation system
Shi, H., He, Z., Zhan, J., & Hwang, K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114152 · Cited by 2
Synergistic organization of action: A computational model
Byadarhaly, K. V., Perdoor, M. C., & Minai, A. A. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033610 · Cited by 2
Online learning of sensorimotor interactions using a neural network with time-delayed inputs
Stoelen, M. F., Bonsignorio, F., Balaguer, C., Marocco, ., & Cangelosi, A. (2012) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2012.6400857 · Cited by 2
Internally Organized Cell Assembly Trajectories
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0007 · Cited by 1
Caressed by music: Related preferences for velocity of touch and tempo of music?
Sailer, U., Zucknick, M., & Laeng, B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1135988 · Cited by 2
Exploiting Spherical Projections To Generate Human-Like Wrist Pointing Movements
Tiseo, C., Charitos, S., & Mistry, M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629550 · Cited by 2
Frequently Asked Questions for: The Atoms of Neural ComputationPreprint
Marcus, G., Marblestone, A. H., & Dean, T. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/010983 · Cited by 2
A model of reward-modulated motor learning with parallelcortical and basal ganglia pathwaysPreprint
Pyle, R., & Rosenbaum, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.03304 · Cited by 2
The brain as a dynamically active organPreprint
Brembs, B. (2020) · doi:10.31234/osf.io/j37av · Cited by 3
(2022) · Wiley eBooks · doi:10.1002/9781119477204.ch1 · Cited by 2
Selective direct motor cortical influence during naturalistic climbing in micePreprint
Koh, N., Ma, Z., Sarup, A., Kristl, A. C., Agrios, M., Young, M., & Miri, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.18.545509 · Cited by 2
Targeted stimulation of motor cortex neural ensembles drives learned movementsPreprint
Chen, Q., Wu, A., Yu, B., Chae, S., Tan, Z., Ramot, A., Aljadeff, J., & Komiyama, T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.06.631504 · Cited by 2
Rhythmic Pattern Generation in Invertebrates
Prinz, A. A. (2018) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190456757.013.17 · Cited by 1
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0003 · Cited by 1
Svarnik, О. E. (2022) · Experimental Psychology (Russia) · doi:10.17759/exppsy.2022150103 · Cited by 1
Molkov, Y. I., Borgmann, A., Koizumi, H., Hama, N., Zhang, R., & Smith, J. C. (2024) · eLife · doi:10.7554/elife.101959.4 · Cited by 1
Self-citationToma, A. I., Dima, V., Rusu, L., Necula, A. I., Stoiciu, R. P., Andrășoaie, L. N., Mirea, A., & Bivoleanu, A. R. (2026) · Life · doi:10.3390/life16010081 · Cited by 1
Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits
Alvarez, J. L., Morrison, K., & Curto, C. (2026) · Neural Computation · doi:10.1162/neco.a.1492 · Cited by 5
Targeted stimulation of motor cortex ensembles drives learned movements
Chen, Q., Wu, A., Yu, B., Chae, S., Tan, Z., Ramot, A., Aljadeff, J., & Komiyama, T. (2026) · Nature · doi:10.1038/s41586-026-11174-3 · Cited by 4
Comparison of Normal and Parkinsonian Microcircuit Dynamics in the Rodent Striatum
Jáidar, O., Carrillo‐Reid, L., & Bargas, J. (2012) · InTech eBooks · doi:10.5772/21036 · Cited by 1
Possible Role or Respiratory Pacemaker Neurons in the Generation of Different Breathing Patterns
Pea-Ortega, F. (2011) · InTech eBooks · doi:10.5772/13849 · Cited by 1
A hierarchical model of synergistic motor control
Byadarhaly, K. V., & Minai, A. A. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706902 · Cited by 1
A Neural Model for Motor Synergies
Perdoor, M. C. (2010) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 1
Afzal, M. F., & Minai, A. A. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727451 · Cited by 1
Causation and Logic in Neuroscience
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0002 · Cited by 1
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0010 · Cited by 1
Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks
Bourahmah, J., Sakurai, A., Katz, P. S., & SHILNIKOV, A. L. (2024) · Brain Sciences · doi:10.3390/brainsci14050468 · Cited by 1
Pacemaker Neurons and Neuronal Networks in Health and Disease
Peña‐Ortega, F. (2012) · InTech eBooks · doi:10.5772/50264 · Cited by 1
Carrillo-Medina, J. L., & Latorre, R. (2018) · Scientific Reports · doi:10.1038/s41598-018-34757-1 · Cited by 1
Bahramianl, A., Nouril, A., Towhidkhah, F., & Jafari, S. (2019) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme49163.2019.9030396 · Cited by 1
A Geometric Milieu Inside the Brain
Tozzi, A., Yurkin, A., & Peters, J. F. (2021) · Foundations of Science · doi:10.1007/s10699-021-09798-x · Cited by 2
Park, C., & Rubin, J. E. (2022) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/21m1431679 · Cited by 1
Cognition, Motor Control and Other Aspects of Autism: A Pragmatic Review
Howell, J. W., & Heck, D. H. (2015) · Contemporary clinical neuroscience · doi:10.1007/978-1-4939-2190-4_19 · Cited by 1
Invertebrate Pattern Generation: Overview
Farzan Nadim, A. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_755 · Cited by 1
Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory
Raichman, N., Rubinsky, L., Idelson, M. S., Baruchi, I., Volman, V., & Ben‐Jacob, E. (2009) · World Scientific lecture notes in complex systems · doi:10.1142/9789812838803_0012 · Cited by 1
Molkov, Y. I., Borgmann, A., Koizumi, H., Hama, N., Zhang, R., & Smith, J. C. (2024) · eLife · doi:10.7554/elife.101959.1 · Cited by 1
Self-citationHansen, E. C. A., Sanderson, N., Nourin, S., Candat, V., Curto, C., & Sumbre, G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.26.684564 · Cited by 1
Distributed population codes in sensory and memory representations of the neocortex
Munk, M. H. J. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511541650.009 · Cited by 1
Corticostriatal Plasticity, Learning and Choice
Stoetzner, C. R. (2011) · Deep Blue (University of Michigan)
Stochastic Amplification in Neural Networks
Hidalgo, J., Seoane, L. F., Cortés, J. M., & Muñoz, M. A. (2014) · Trends in mathematics · doi:10.1007/978-3-319-08138-0_9
Computação por assembleias neurais em redes neurais pulsadas.
Ribeiro, J. H. R. (2011) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.3.2011.tde-16032012-112119
Possible Actions of Gap Junctional Coupling in Reward-Seeking Behaviors
Velázquez, J. L. P. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-415901-3.00015-3
Correction: Probing the Dynamics of Identified Neurons with a Data-Driven Modeling Approach
Nowotny, T., Levi, R., & Selverston, A. I. (2012) · PLoS ONE · doi:10.1371/annotation/08b55d6b-7872-40a1-9988-b4bd1c849b8a
A Unified Framework for Information Coding: Oscillations, Memory, and Zombie ModesPreprint
Sornborger, A. T., & Tao, L. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.1116
Adaptive neurocomputation with spiking semiconductor neurons
Zhao, L. (2014) · The University of Bath Online Publications Store (The University of Bath)
Metadata
- Authors
- Yuste, R., MacLean, J. N., Smith, J., & Lansner, A.
- Affiliations
- Columbia University (US)
- National Institutes of Health (US)
- Stockholm University (SE)
- Corresponding author
- Yuste, Rafael
- Venue
- Nature Reviews Neuroscience
- Published
- 2005-06-01
- Volume
- 6
- Issue
- 6
- Pages
- 477-483
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W1481508412
- Cited by
- 355
- References
- 107
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 14 · 2023: 16 · 2022: 14 · 2021: 11 · 2020: 12 · 2019: 34 · 2018: 16 · 2017: 13 · 2016: 17 · 2015: 15 · 2014: 21 · 2013: 10 · 2012: 20
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- 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 › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- central pattern generator, neocortex, cortical circuits, spinal cord, brainstem, spontaneous activity, cortical plasticity
- MeSH terms
- Animals, Cerebral Cortex, Humans, Nerve Net
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