Mostrar el registro sencillo del ítem
dc.contributor.author | Sherfey, Jason | es_ES |
dc.contributor.author | Ardid-Ramírez, Joan Salvador | es_ES |
dc.contributor.author | Miller, Earl K. | es_ES |
dc.contributor.author | Hasselmo, Michael E. | es_ES |
dc.contributor.author | Kopell, Nancy J. | es_ES |
dc.date.accessioned | 2021-07-30T03:31:11Z | |
dc.date.available | 2021-07-30T03:31:11Z | |
dc.date.issued | 2020-09 | es_ES |
dc.identifier.issn | 1074-7427 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/170960 | |
dc.description.abstract | [EN] Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma -frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex. | es_ES |
dc.description.sponsorship | This work was supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02 to N. K., the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832 to M. H. and E. M., the National Institute of Mental Health under award number NIMH R37MH087027 to E. M., and The MIT Picower Institute Faculty Innovation Fund to E. M. We would like to acknowledge Joachim Hass and Michelle McCarthy for early discussions of our modeling results, as well as Andre Bastos and Mikael Lundqvist for discussions relating our modeling work to their experiments. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Neurobiology of Learning and Memory | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Cognition | es_ES |
dc.subject | Working memory | es_ES |
dc.subject | Gating | es_ES |
dc.subject | Beta rhythm | es_ES |
dc.subject | Gamma rhythm | es_ES |
dc.subject | Resonance | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.title | Prefrontal oscillations modulate the propagation of neuronal activity required for working memory | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.nlm.2020.107228 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/NIH//R37MH087027/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ARO//W911NF-12-R-0012-02/US/Event-Driven Game Theory for Predicting Dynamical Systems/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ONR//N00014-16-1-2832/US/ONR MURI: Neural circuits underlying symbolic processing in primate cortex and basal ganglia/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres | es_ES |
dc.description.bibliographicCitation | Sherfey, J.; Ardid-Ramírez, JS.; Miller, EK.; Hasselmo, ME.; Kopell, NJ. (2020). Prefrontal oscillations modulate the propagation of neuronal activity required for working memory. Neurobiology of Learning and Memory. 173:1-13. https://doi.org/10.1016/j.nlm.2020.107228 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.nlm.2020.107228 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 13 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 173 | es_ES |
dc.identifier.pmid | 32561459 | es_ES |
dc.identifier.pmcid | PMC7429344 | es_ES |
dc.relation.pasarela | S\434964 | es_ES |
dc.contributor.funder | Office of Naval Research | es_ES |
dc.contributor.funder | Army Research Office, EEUU | es_ES |
dc.contributor.funder | National Institute of Mental Health, EEUU | es_ES |
dc.description.references | Adams, N. E., Sherfey, J. S., Kopell, N. J., Whittington, M. A., & LeBeau, F. E. N. (2017). Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity. eneuro, 4(1), ENEURO.0313-16.2017. doi:10.1523/eneuro.0313-16.2017 | es_ES |
dc.description.references | Akam, T., & Kullmann, D. M. (2010). Oscillations and Filtering Networks Support Flexible Routing of Information. Neuron, 67(2), 308-320. doi:10.1016/j.neuron.2010.06.019 | es_ES |
dc.description.references | Amiez, C., Joseph, J.-P., & Procyk, E. (2005). Anterior cingulate error-related activity is modulated by predicted reward. European Journal of Neuroscience, 21(12), 3447-3452. doi:10.1111/j.1460-9568.2005.04170.x | es_ES |
dc.description.references | Ardid, S., Sherfey, J. S., McCarthy, M. M., Hass, J., Pittman-Polletta, B. R., & Kopell, N. (2019). Biased competition in the absence of input bias revealed through corticostriatal computation. Proceedings of the National Academy of Sciences, 116(17), 8564-8569. doi:10.1073/pnas.1812535116 | es_ES |
dc.description.references | Ardid, S., & Wang, X.-J. (2013). A Tweaking Principle for Executive Control: Neuronal Circuit Mechanism for Rule-Based Task Switching and Conflict Resolution. Journal of Neuroscience, 33(50), 19504-19517. doi:10.1523/jneurosci.1356-13.2013 | es_ES |
dc.description.references | Ardid, S., Wang, X.-J., & Compte, A. (2007). An Integrated Microcircuit Model of Attentional Processing in the Neocortex. Journal of Neuroscience, 27(32), 8486-8495. doi:10.1523/jneurosci.1145-07.2007 | es_ES |
dc.description.references | Ardid, S., Wang, X.-J., Gomez-Cabrero, D., & Compte, A. (2010). Reconciling Coherent Oscillation with Modulationof Irregular Spiking Activity in Selective Attention:Gamma-Range Synchronization between Sensoryand Executive Cortical Areas. Journal of Neuroscience, 30(8), 2856-2870. doi:10.1523/jneurosci.4222-09.2010 | es_ES |
dc.description.references | Baddeley, A. D. and Hitch, G. (1974). Working Memory. In Bower, G.H., editor, Psychology of Learning and Motivation, volume 8, pages 47–89. Academic Press. | es_ES |
dc.description.references | Badre, D., & Frank, M. J. (2011). Mechanisms of Hierarchical Reinforcement Learning in Cortico-Striatal Circuits 2: Evidence from fMRI. Cerebral Cortex, 22(3), 527-536. doi:10.1093/cercor/bhr117 | es_ES |
dc.description.references | Barbas, H. (2015). General Cortical and Special Prefrontal Connections: Principles from Structure to Function. Annual Review of Neuroscience, 38(1), 269-289. doi:10.1146/annurev-neuro-071714-033936 | es_ES |
dc.description.references | Bhandari, A., & Badre, D. (2018). Learning and transfer of working memory gating policies. Cognition, 172, 89-100. doi:10.1016/j.cognition.2017.12.001 | es_ES |
dc.description.references | Brette, R., & Guigon, E. (2003). Reliability of Spike Timing Is a General Property of Spiking Model Neurons. Neural Computation, 15(2), 279-308. doi:10.1162/089976603762552924 | es_ES |
dc.description.references | Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908 | es_ES |
dc.description.references | Brincat, S. L., & Miller, E. K. (2016). Prefrontal Cortex Networks Shift from External to Internal Modes during Learning. Journal of Neuroscience, 36(37), 9739-9754. doi:10.1523/jneurosci.0274-16.2016 | es_ES |
dc.description.references | Buschman, T. J., Denovellis, E. L., Diogo, C., Bullock, D., & Miller, E. K. (2012). Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex. Neuron, 76(4), 838-846. doi:10.1016/j.neuron.2012.09.029 | es_ES |
dc.description.references | Cannon, J., McCarthy, M. M., Lee, S., Lee, J., Börgers, C., Whittington, M. A., & Kopell, N. (2013). Neurosystems: brain rhythms and cognitive processing. European Journal of Neuroscience, 39(5), 705-719. doi:10.1111/ejn.12453 | es_ES |
dc.description.references | Cho, R. Y., Konecky, R. O., & Carter, C. S. (2006). Impairments in frontal cortical synchrony and cognitive control in schizophrenia. Proceedings of the National Academy of Sciences, 103(52), 19878-19883. doi:10.1073/pnas.0609440103 | es_ES |
dc.description.references | Compte, A. (2000). Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model. Cerebral Cortex, 10(9), 910-923. doi:10.1093/cercor/10.9.910 | es_ES |
dc.description.references | DeFelipe, J. (1997). Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. Journal of Chemical Neuroanatomy, 14(1), 1-19. doi:10.1016/s0891-0618(97)10013-8 | es_ES |
dc.description.references | Douglas, R. J., & Martin, K. A. C. (2004). NEURONAL CIRCUITS OF THE NEOCORTEX. Annual Review of Neuroscience, 27(1), 419-451. doi:10.1146/annurev.neuro.27.070203.144152 | es_ES |
dc.description.references | Durstewitz, D., & Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Networks, 15(4-6), 561-572. doi:10.1016/s0893-6080(02)00049-7 | es_ES |
dc.description.references | Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Dopamine-Mediated Stabilization of Delay-Period Activity in a Network Model of Prefrontal Cortex. Journal of Neurophysiology, 83(3), 1733-1750. doi:10.1152/jn.2000.83.3.1733 | es_ES |
dc.description.references | Frank, M. J., & Badre, D. (2011). Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis. Cerebral Cortex, 22(3), 509-526. doi:10.1093/cercor/bhr114 | es_ES |
dc.description.references | FRANK, M. J., LOUGHRY, B., & O’REILLY, R. C. (2001). Interactions between frontal cortex and basal ganglia in working memory: A computational model. Cognitive, Affective, & Behavioral Neuroscience, 1(2), 137-160. doi:10.3758/cabn.1.2.137 | es_ES |
dc.description.references | Hasselmo, M. E., & Stern, C. E. (2018). A network model of behavioural performance in a rule learning task. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1744), 20170275. doi:10.1098/rstb.2017.0275 | es_ES |
dc.description.references | Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735 | es_ES |
dc.description.references | Kaski, S., & Kohonen, T. (1994). Winner-take-all networks for physiological models of competitive learning. Neural Networks, 7(6-7), 973-984. doi:10.1016/s0893-6080(05)80154-6 | es_ES |
dc.description.references | Kerns, J. G., Cohen, J. D., MacDonald, A.W., Cho, R.Y., Stenger, V.A., and Carter, C.S. (2004). Anterior cingulate conflict monitoring and adjustments in control. Science (New York, N.Y.), 303(5660):1023–1026. | es_ES |
dc.description.references | Komorowski, R. W., Garcia, C. G., Wilson, A., Hattori, S., Howard, M. W., & Eichenbaum, H. (2013). Ventral Hippocampal Neurons Are Shaped by Experience to Represent Behaviorally Relevant Contexts. Journal of Neuroscience, 33(18), 8079-8087. doi:10.1523/jneurosci.5458-12.2013 | es_ES |
dc.description.references | Kriete, T., & Noelle, D. C. (2011). Generalisation benefits of output gating in a model of prefrontal cortex. Connection Science, 23(2), 119-129. doi:10.1080/09540091.2011.569881 | es_ES |
dc.description.references | Kritzer, M. F., & Goldman-Rakic, P. S. (1995). Intrinsic circuit organization of the major layers and sublayers of the dorsolateral prefrontal cortex in the rhesus monkey. The Journal of Comparative Neurology, 359(1), 131-143. doi:10.1002/cne.903590109 | es_ES |
dc.description.references | Levitt, J. B., Lewis, D. A., Yoshioka, T., & Lund, J. S. (1993). Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46). The Journal of Comparative Neurology, 338(3), 360-376. doi:10.1002/cne.903380304 | es_ES |
dc.description.references | Lundqvist, M., Compte, A., & Lansner, A. (2010). Bistable, Irregular Firing and Population Oscillations in a Modular Attractor Memory Network. PLoS Computational Biology, 6(6), e1000803. doi:10.1371/journal.pcbi.1000803 | es_ES |
dc.description.references | Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L., & Miller, E. K. (2018). Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nature Communications, 9(1). doi:10.1038/s41467-017-02791-8 | es_ES |
dc.description.references | Lundqvist, M., Rose, J., Herman, P., Brincat, S. L., Buschman, T. J., & Miller, E. K. (2016). Gamma and Beta Bursts Underlie Working Memory. Neuron, 90(1), 152-164. doi:10.1016/j.neuron.2016.02.028 | es_ES |
dc.description.references | Mante, V., Sussillo, D., Shenoy, K. V., & Newsome, W. T. (2013). Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature, 503(7474), 78-84. doi:10.1038/nature12742 | es_ES |
dc.description.references | Melrose, R. J., Poulin, R. M., & Stern, C. E. (2007). An fMRI investigation of the role of the basal ganglia in reasoning. Brain Research, 1142, 146-158. doi:10.1016/j.brainres.2007.01.060 | es_ES |
dc.description.references | Miller, E. K. (2000). The prefontral cortex and cognitive control. Nature Reviews Neuroscience, 1(1), 59-65. doi:10.1038/35036228 | es_ES |
dc.description.references | O’Reilly, R. C., & Frank, M. J. (2006). Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia. Neural Computation, 18(2), 283-328. doi:10.1162/089976606775093909 | es_ES |
dc.description.references | Parnaudeau, S., O’Neill, P.-K., Bolkan, S. S., Ward, R. D., Abbas, A. I., Roth, B. L., … Kellendonk, C. (2013). Inhibition of Mediodorsal Thalamus Disrupts Thalamofrontal Connectivity and Cognition. Neuron, 77(6), 1151-1162. doi:10.1016/j.neuron.2013.01.038 | es_ES |
dc.description.references | Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the Brain: The Neurophysics of EEG. Oxford University Press. Google-Books-ID: fUv54as56_8C. | es_ES |
dc.description.references | Renart, A., Rocha, J. d. l., Bartho, P., Hollender, L., Parga, N., Reyes, A., Harris, K. D. (2010). The Asynchronous State in Cortical Circuits. Science, 327(5965):587–590. | es_ES |
dc.description.references | Richardson, M. J. E., Brunel, N., & Hakim, V. (2003). From Subthreshold to Firing-Rate Resonance. Journal of Neurophysiology, 89(5), 2538-2554. doi:10.1152/jn.00955.2002 | es_ES |
dc.description.references | Rotstein, H. G. (2017). Spiking Resonances In Models With The Same Slow Resonant And Fast Amplifying Currents But Different Subthreshold Dynamic Properties. bioRxiv, page 128611. | es_ES |
dc.description.references | Seamans, J. K., Lapish, C. C., & Durstewitz, D. (2008). Comparing the prefrontal cortex of rats and primates: Insights from electrophysiology. Neurotoxicity Research, 14(2-3), 249-262. doi:10.1007/bf03033814 | es_ES |
dc.description.references | Shen, Z., Popov, V., Delahay, A. B., & Reder, L. M. (2017). Item strength affects working memory capacity. Memory & Cognition, 46(2), 204-215. doi:10.3758/s13421-017-0758-4 | es_ES |
dc.description.references | Sherfey, J. S., Ardid, S., Hass, J., Hasselmo, M. E., & Kopell, N. J. (2018). Flexible resonance in prefrontal networks with strong feedback inhibition. PLOS Computational Biology, 14(8), e1006357. doi:10.1371/journal.pcbi.1006357 | es_ES |
dc.description.references | Sherfey, J. S., Soplata, A. E., Ardid, S., Roberts, E. A., Stanley, D. A., Pittman-Polletta, B.R., and Kopell, N.J. (2018b). DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation. Frontiers in Neuroinformatics, 12. | es_ES |
dc.description.references | Siegel, M., Warden, M. R., & Miller, E. K. (2009). Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106(50), 21341-21346. doi:10.1073/pnas.0908193106 | es_ES |
dc.description.references | Tegnér, J., Compte, A., & Wang, X.-J. (2002). The dynamical stability of reverberatory neural circuits. Biological Cybernetics, 87(5-6), 471-481. doi:10.1007/s00422-002-0363-9 | es_ES |
dc.description.references | Tzur, G., & Berger, A. (2009). Fast and slow brain rhythms in rule/expectation violation tasks: Focusing on evaluation processes by excluding motor action. Behavioural Brain Research, 198(2), 420-428. doi:10.1016/j.bbr.2008.11.041 | es_ES |
dc.description.references | Zhu, H., Paschalidis, I. C., Chang, A., Stern, C. E., & Hasselmo, M. E. (2020). A neural circuit model for a contextual association task inspired by recommender systems. Hippocampus, 30(4), 384-395. doi:10.1002/hipo.23194 | es_ES |
dc.description.references | Zhu, H., Paschalidis, I. C., & Hasselmo, M. E. (2018). Neural circuits for learning context-dependent associations of stimuli. Neural Networks, 107, 48-60. doi:10.1016/j.neunet.2018.07.018 | es_ES |