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Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

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Feature-specific prediction errors and surprise across macaque fronto-striatal circuits

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dc.contributor.author Oemisch, M. es_ES
dc.contributor.author Westendorff, S. es_ES
dc.contributor.author Azimi, Marzyeh es_ES
dc.contributor.author Hassani, Seyed Alireza es_ES
dc.contributor.author Ardid-Ramírez, Joan Salvador es_ES
dc.contributor.author Tiesinga, Paul es_ES
dc.contributor.author Womelsdorf, Thilo es_ES
dc.date.accessioned 2021-06-09T03:31:34Z
dc.date.available 2021-06-09T03:31:34Z
dc.date.issued 2019-01-11 es_ES
dc.identifier.issn 2041-1723 es_ES
dc.identifier.uri http://hdl.handle.net/10251/167598
dc.description.abstract [EN] To adjust expectations efficiently, prediction errors need to be associated with the precise features that gave rise to the unexpected outcome, but this credit assignment may be problematic if stimuli differ on multiple dimensions and it is ambiguous which feature dimension caused the outcome. Here, we report a potential solution: neurons in four recorded areas of the anterior fronto-striatal networks encode prediction errors that are specific to feature values of different dimensions of attended multidimensional stimuli. The most ubiquitous prediction error occurred for the reward-relevant dimension. Feature-specific prediction error signals a) emerge on average shortly after non-specific prediction error signals, b) arise earliest in the anterior cingulate cortex and later in dorsolateral prefrontal cortex, caudate and ventral striatum, and c) contribute to feature-based stimulus selection after learning. Thus, a widely-distributed feature-specific eligibility trace may be used to update synaptic weights for improved feature-based attention. es_ES
dc.description.sponsorship This work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (T.W.) and the Natural Sciences and Engineering Research Council of Canada (T.W.), as well as by the Brain in Action CREATE-IRTG program (M.O. and T.W.), and by grant LPDS 2012-08 from the Deutsche Akademie der Naturforscher Leopoldina (S.W.). Imaging data provided by the Duke Center for In Vivo Microscopy, an NIH Biomedical Technology Resource (NIHP41EB015897, 1S10OD010683-01). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. The authors would like to thank Hongying Wang for technical support es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Nature Communications es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Feature-specific prediction errors and surprise across macaque fronto-striatal circuits es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41467-018-08184-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften//LPDS 2012-08/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//1S10OD010683-01/US/Agilent Direct Drive 9.4T MRS%2FMRI Console/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CIHR//MOP 102482/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/HHS//P41EB015897/ 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 Oemisch, M.; Westendorff, S.; Azimi, M.; Hassani, SA.; Ardid-Ramírez, JS.; Tiesinga, P.; Womelsdorf, T. (2019). Feature-specific prediction errors and surprise across macaque fronto-striatal circuits. Nature Communications. 10:1-15. https://doi.org/10.1038/s41467-018-08184-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41467-018-08184-9 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.identifier.pmid 30635579 es_ES
dc.identifier.pmcid PMC6329800 es_ES
dc.relation.pasarela S\434971 es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
dc.contributor.funder Canadian Institutes of Health Research es_ES
dc.contributor.funder U.S. Department of Health and Human Services es_ES
dc.contributor.funder Natural Sciences and Engineering Research Council of Canada es_ES
dc.contributor.funder Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften es_ES
dc.description.references Farashahi, S., Rowe, K., Aslami, Z., Lee, D. & Soltani, A. Feature-based learning improves adaptability without compromising precision. Nat. Commun. 8, 1768 (2017). es_ES
dc.description.references Hikosaka, O., Ghazizadeh, A., Griggs, W. & Amita, H. Parallel basal ganglia circuits for decision making. J. Neural Transm. 1–15 (2017). https://doi.org/10.1007/s00702-017-1691-1 es_ES
dc.description.references Leong, Y. C., Radulescu, A., Daniel, R., DeWoskin, V. & Niv, Y. Dynamic Interaction between reinforcement learning and attention in multidimensional environments. Neuron 93, 451–463 (2017). es_ES
dc.description.references Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015). es_ES
dc.description.references Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction. Vol. 135. Cambridge: MIT Press (1998). es_ES
dc.description.references Gottlieb, J. Attention, learning and the value of information. Neuron 76, 281–295 (2012). es_ES
dc.description.references Pearce, J. & Hall, G. A model for Pavlovian learning: variation in the effectiveness of conditioned but not unconditioned stimuli. Psychol. Rev. 87, 532–552 (1980). es_ES
dc.description.references Daddaoua, N., Lopes, M. & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci. Rep. 6, 1–15 (2016). es_ES
dc.description.references Dayan, P., Kakade, S. & Montague, P. R. Learning and selective attention. Nat. Neurosci. 3, 1218–1223 (2000). es_ES
dc.description.references Hassani, S. A. et al. A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque. Sci. Rep. 7, 1–19 (2017). es_ES
dc.description.references Wilson, R. C. & Niv, Y. Inferring relevance in a changing world. Front. Hum. Neurosci. 5, 1–14 (2012). es_ES
dc.description.references Kruschke, J. K. & Hullinger, R. A. Evolution of attention in learning. Comput. Models Condition. (2010). https://doi.org/10.1017/CBO9780511760402.002 es_ES
dc.description.references Asaad, W. F., Lauro, P. M., Perge, J. A. & Eskandar, E. N. Prefrontal neurons encode a solution to the credit assignment problem. J. Neurosci. 37, 3311–3316 (2017). es_ES
dc.description.references Haber, S. N. & Knutson, B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology 35, 4–26 (2010). es_ES
dc.description.references Dias, R., Robbins, T. W. & Roberts, A. C. Dissociation in prefrontal cortex of affective and attentional shifts. Nature 380, 69–72 (1996). es_ES
dc.description.references Glimcher, P. W. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc. Natl Acad. Sci. USA 108 Suppl, 15647–15654 (2011). es_ES
dc.description.references Bichot, N. P., Heard, M. T., DeGennaro, E. M. & Desimone, R. A source for feature-based attention in the prefrontal cortex. Neuron 88, 832–844 (2015). es_ES
dc.description.references Kaping, D., Vinck, M., Hutchison, R. M., Everling, S. & Womelsdorf, T. Specific contributions of ventromedial, anterior cingulate, and lateral prefrontal cortex for attentional selection and stimulus valuation. PLoS Biol. 9, e1001224 (2011). es_ES
dc.description.references Alexander, W. H. & Brown, J. W. Hierarchical error representation: a computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Comput. 27, 2354–2410 (2015). es_ES
dc.description.references Sutton, R. S. & Barto, A. G. Reinforcement Learning: an Introduction. 322 (1998). https://doi.org/10.1109/TNN.1998.712192 es_ES
dc.description.references Roelfsema, P. R. & van Ooyen, A. Attention-gated reinforcement learning of internal representations for classification. Neural Comput. 17, 2176–2214 (2005). es_ES
dc.description.references Rombouts, J. O., Bohte, S. M. & Roelfsema, P. R. How attention can create synaptic tags for the learning of working memories in sequential tasks. PLoS Comput. Biol. 11, 1–34 (2015). es_ES
dc.description.references Balcarras, M., Ardid, S., Kaping, D., Everling, S. & Womelsdorf, T. Attentional selection can be predicted by reinforcement learning of task-relevant stimulus features weighted by value-independent stickiness. J. Cogn. Neurosci. 28, 333–349 (2016). es_ES
dc.description.references Smith, A. C. et al. Dynamic analysis of learning in behavioral experiments. J. Neurosci. 24, 447–461 (2004). es_ES
dc.description.references Kennerley, S. W., Behrens, T. E. J. & Wallis, J. D. Double dissociation of value computations in orbitofrontal and anterior cingulate neurons. Nat. Neurosci. 14, 1581–1589 (2011). es_ES
dc.description.references Asaad, W. F. & Eskandar, E. N. Encoding of both positive and negative reward prediction errors by neurons of the primate lateral prefrontal cortex and caudate nucleus. J. Neurosci. 31, 17772–17787 (2011). es_ES
dc.description.references Hayden, B. Y., Heilbronner, S. R., Pearson, J. M. & Platt, M. L. Surprise signals in anterior cingulate cortex: neuronal encoding of unsigned reward prediction errors driving adjustment in behavior. J. Neurosci. 31, 4178–4187 (2011). es_ES
dc.description.references Schultz, W. Dopamine reward prediction error coding. Dialogues Clin. Neurosci. 18, 23–32 (2016). es_ES
dc.description.references Izquierdo, A., Brigman, J. L., Radke, A. K., Rudebeck, P. H. & Holmes, A. The neural basis of reversal learning: an updated perspective. Neuroscience 345, 12–26 (2017). es_ES
dc.description.references Fiorillo, C. D., Tobler, P. N. & Schultz, W. Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299, 1898–1902 (2003). es_ES
dc.description.references Schultz, W. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27 (1998). es_ES
dc.description.references Ardid, S. et al. Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J. Neurosci. 35, 2975–2991 (2015). es_ES
dc.description.references Berke, J. D. Uncoordinated firing rate changes of striatal fast-spiking interneurons during behavioural task performance. J. Neurosci. 28, 10075–10080 (2008). es_ES
dc.description.references Lansink, C. S., Goltstein, P. M., Lankelma, J. V. & Pennartz, C. M. A. Fast-spiking interneurons of the rat ventral striatum: temporal coordination of activity with principal cells and responsiveness to reward. Eur. J. Neurosci. 32, 494–508 (2010). es_ES
dc.description.references Kawaguchi, Y. Physiological, morphological, and histochemical characterization of three classes of interneurons in rat neostriatum. J. Neurosci. 13, 4908–4923 (1993). es_ES
dc.description.references Shen, C. et al. Anterior cingulate cortex cells identify process-specific errors of attentional control prior to transient prefrontal-cingulate inhibition. Cereb. Cortex 25, 2213–2228 (2015). es_ES
dc.description.references Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016). es_ES
dc.description.references Quilodran, R., Rothé, M. & Procyk, E. Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron 57, 314–325 (2008). es_ES
dc.description.references Kennerley, S. W., Dahmubed, A. F., Lara, A. H. & Wallis, J. D. Neurons in the frontal lobe encode the value of multiple decision variables. J. Cogn. Neurosci. 21, 1162–1178 (2009). es_ES
dc.description.references Womelsdorf, T., Johnston, K., Vinck, M. & Everling, S. Theta-activity in anterior cingulate cortex predicts task rules and their adjustments following errors. Proc. Natl Acad. Sci. 107, 5248–5253 (2010). es_ES
dc.description.references Oemisch, M., Westendorff, S., Everling, S. & Womelsdorf, T. Interareal spike-train correlations of anterior cingulate and dorsal prefrontal cortex during attention shifts. J. Neurosci. 35, 13076–13089 (2015). es_ES
dc.description.references Voloh, B., Valiante, T. A., Everling, S. & Womelsdorf, T. Theta-gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts. Proc. Natl Acad. Sci. USA 112, 8457–8462 (2015). es_ES
dc.description.references Westendorff, S., Kaping, D., Everling, S. & Womelsdorf, T. Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior. J. Neurophysiol. 116, 796–811 (2016). es_ES
dc.description.references Womelsdorf, T. & Everling, S. Long-range attention networks: circuit motifs underlying endogenously controlled stimulus selection. Trends Neurosci. 38, 682–700 (2015). es_ES
dc.description.references Medalla, M. & Barbas, H. Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control. Neuron 61, 609–620 (2009). es_ES
dc.description.references Antzoulatos, E. G. & Miller, E. K. Increases in functional connectivity between prefrontal cortex and striatum during category learning. Neuron 83, 216–225 (2014). es_ES
dc.description.references Womelsdorf, T., Ardid, S., Everling, S. & Valiante, T. A. Burst firing synchronizes prefrontal and anterior cingulate cortex during attentional control. Curr. Biol. 1–9 (2014). https://doi.org/10.1016/j.cub.2014.09.046 es_ES
dc.description.references Hunt, L. T. & Hayden, B. Y. A distributed, hierarchical and recurrent framework for reward-based choice. Nat. Rev. Neurosci. 18, 172–182 (2017). es_ES
dc.description.references Kable, J. W. & Glimcher, P. W. The neurobiology of decision: consensus and controversy. Neuron 63, 733–745 (2009). es_ES
dc.description.references Badre, D. & Nee, D. E. Frontal cortex and the hierarchical control of behavior. Trends Cogn. Sci. 22, 170–188 (2018). es_ES
dc.description.references Tian, J. et al. Distributed and mixed information in monosynaptic inputs to dopamine neurons. Neuron 1374–1389 (2016). https://doi.org/10.1016/j.neuron.2016.08.018 es_ES
dc.description.references den Ouden, H. E. M., Kok, P. & de Lange, F. P. How prediction errors shape perception, attention, and motivation. Front. Psychol. 3, 1–12 (2012). es_ES
dc.description.references Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013). es_ES
dc.description.references Genovesio, A., Wise, S. P. & Passingham, R. E. Prefrontal—parietal function: from foraging to foresight. Trends Cogn. Sci. 18, 72–81 (2014). es_ES
dc.description.references Donahue, C. H. & Lee, D. Dynamic routing of task-relevant signals for decision making in dorsolateral prefrontal cortex. Nat. Neurosci. 18, 1–9 (2015). es_ES
dc.description.references Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013). es_ES
dc.description.references Berke, J. D. Functional properties of striatal fast-spiking interneurons. Front. Syst. Neurosci. 5, 1–7 (2011). es_ES
dc.description.references Hennequin, G., Agnes, E. J. & Vogels, T. P. Inhibitory plasticity: balance, control, and codependence. Annu. Rev. Neurosci. 40, 557–579 (2017). es_ES
dc.description.references Wilson, F. A., O’Scalaidhe, S. P. & Goldman-Rakic, P. S. Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. Proc. Natl Acad. Sci. 91, 4009–4013 (1994). es_ES
dc.description.references Lee, K. et al. Parvalbumin interneurons modulate striatal output and enhance performance during associative learning. Neuron 93, 1451–1463.e4 (2017). es_ES
dc.description.references Vogels, T. P., Sprekeler, H., Zenke, F., Clopath, C. & Gerstner, W. Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science 334, 1569–1573 (2011). es_ES
dc.description.references Le Pelley, M. E., Mitchell, C. J., Beesley, T., George, D. N. & Wills, A. J. Attention and associative learning in humans: an integrative review. Psychol. Bull. 142, 1111–1140 (2016). es_ES
dc.description.references Courville, A. C., Daw, N. D. & Touretzky, D. S. Bayesian theories of conditioning in a changing world. Trends Cogn. Sci. 10, 294–300 (2006). es_ES
dc.description.references Gottlieb, J., Hayhoe, M., Hikosaka, O. & Rangel, A. Attention, reward, and information seeking. J. Neurosci. 34, 15497–15504 (2014). es_ES
dc.description.references Rusch, T., Korn, C. W. & Gläscher, J. A two-way street between attention and learning. Neuron 93, 256–258 (2017). es_ES
dc.description.references Takahashi, Y. K., Langdon, A. J., Niv, Y. & Schoenbaum, G. Temporal specificity of reward prediction errors signaled by putative dopamine neurons in rat VTA depends on ventral striatum. Neuron 91, 182–193 (2016). es_ES
dc.description.references Watabe-Uchida, M., Eshel, N. & Uchida, N. Neural circuitry of reward prediction error. Annu. Rev. Neurosci. 40, 373–394 (2017). es_ES
dc.description.references Krauzlis, R. J., Bollimunta, A., Arcizet, F. & Wang, L. Attention as an effect not a cause. Trends Cogn. Sci. 18, 457–464 (2014). es_ES
dc.description.references Lovejoy, L. P. & Krauzlis, R. J. Inactivation of primate superior colliculus impairs covert selection of signals for perceptual judgments. Nat. Neurosci. 13, 261–266 (2010). es_ES
dc.description.references Rasmussen, D., Voelker, A. & Eliasmith, C. A neural model of hierarchical reinforcement learning. PLoS ONE 12, e0180234 (2017). es_ES
dc.description.references Fusi, S., Asaad, W. F., Miller, E. K. & Wang, X. J. A neural circuit model of flexible sensorimotor mapping: learning and forgetting on multiple timescales. Neuron 54, 319–333 (2007). es_ES
dc.description.references Roelfsema, P. R. & Holtmaat, A. Control of synaptic plasticity in deep cortical networks. Nat. Rev. Neurosci. 19, 166–180 (2018). es_ES
dc.description.references Calabrese, E. et al. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage 117, 408–416 (2015). es_ES
dc.description.references Bakker, R., Tiesinga, P. & Kötter, R. The scalable brain atlas: instant web-based access to public brain atlases and related content. Neuroinformatics 13, 353–366 (2015). es_ES


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