- -

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory

Mostrar el registro completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/170960

Ficheros en el ítem

Metadatos del ítem

Título: Prefrontal oscillations modulate the propagation of neuronal activity required for working memory
Autor: Sherfey, Jason Ardid-Ramírez, Joan Salvador Miller, Earl K. Hasselmo, Michael E. Kopell, Nancy J.
Entidad UPV: 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
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Cognition , Working memory , Gating , Beta rhythm , Gamma rhythm , Resonance
Derechos de uso: Reconocimiento (by)
Fuente:
Neurobiology of Learning and Memory. (issn: 1074-7427 )
DOI: 10.1016/j.nlm.2020.107228
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.nlm.2020.107228
Código del Proyecto:
info:eu-repo/grantAgreement/NIH//R37MH087027/
info:eu-repo/grantAgreement/ARO//W911NF-12-R-0012-02/US/Event-Driven Game Theory for Predicting Dynamical Systems/
info:eu-repo/grantAgreement/ONR//N00014-16-1-2832/US/ONR MURI: Neural circuits underlying symbolic processing in primate cortex and basal ganglia/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735

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

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.

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

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

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

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

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

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

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

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

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

Miller, E. K. (2000). The prefontral cortex and cognitive control. Nature Reviews Neuroscience, 1(1), 59-65. doi:10.1038/35036228

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

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

Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the Brain: The Neurophysics of EEG. Oxford University Press. Google-Books-ID: fUv54as56_8C.

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.

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

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.

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

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

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

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.

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

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

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

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

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

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem