Computational Neuroscience Models of Working Memory

doi:10.1017/9781108755610.023

19 - Computational Neuroscience Models of Working Memory

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press: 21 April 2023

Michael J. Frank and

Edited by

Ron Sun: Affiliation:
Rensselaer Polytechnic Institute, New York

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Summary

The focus of this chapter is on neurobiologically informed and constrained models of working memory as defined by Miller, Galanter, and Pribram (1960): the holding of goals and subgoals in mind in service of planning and executing complex behaviors. In particular, the chapter focuses on models specifically addressing critical challenges and mechanisms following from the need for rapid and selective gating of working memory contents. To start, the important computational challenges posed by the tradeoff between maintaining vs. updating are discussed, providing motivation for the rest of the chapter.After that, several seminal models that have contributed to current thinking are reviewed, including the authors’ own PBWM framework that has proven influential. Finally, several recent developments from the deep learning and neurophysiology literatures are addressed and critical questions and some directions for future progress are discussed.

Keywords

working memory computational model executive function cognitive control prefrontal cortex basal ganglia

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Type: Chapter
Information: The Cambridge Handbook of Computational Cognitive Sciences , pp. 611 - 663

DOI: https://doi.org/10.1017/9781108755610.023 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2023

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References

Adams, E. J., Nguyen, A. T., & Cowan, N. (2018). Theories of working memory: differences in definition, degree of modularity, role of attention, and purpose. Language, Speech, and Hearing Services in Schools, 49(3), 340–355. https://doi.org/10.1044/2018%20LSHSS-17-0114 CrossRef Google Scholar PubMed

Alexander, G., DeLong, M., & Strick, P. (1986 ). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience, 9, 357–381.CrossRef Google Scholar PubMed

Alexander, G. E. (1987 ). Selective neuronal discharge in monkey putamen reflects intended direction of planned limb movements. Experimental Brain Research, 67, 623–634.Google Scholar

Anderson, J. R., & Lebiere, C. (1998). The Atomic Components of Thought (1st ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar

Arbib, M. A., & Dominey, P. F. (1995 ). Modeling the roles of basal ganglia in timing and sequencing saccadic eye movements. In Houk, J. C., Davis, J. L., & Beiser, D. G. (Eds.), Models of Information Processing in the Basal Ganglia (pp. 149–162). Cambridge, MA: MIT Press.Google Scholar

Arnsten, A. F. T., Wang, M. J., & Paspalas, C. D. (2012 ). Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron, 76(1), 223–239. https://doi.org/10.1016/%20j.neuron.2012.08.038 CrossRef Google Scholar PubMed

Ashby, F. G., Ell, S. W., Valentin, V. V., & Casale, M. B. (2005). FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience, 17(11), 1728–1743. https://doi.org/10.1162/089892905774589271 Google Scholar

Baddeley, A. D. (1986). Working Memory. New York, NY: Oxford University Press.Google Scholar

Baddeley, A. D., & Hitch, G. J. (1974). Working memory. In Bower, G. (Ed.), The Psychology of Learning and Motivation (vol. VIII, pp. 47–89). New York, NY: Academic Press.Google Scholar

Badre, D., & Frank, M. J. (2012 ). Mechanisms of hierarchical reinforcement learning in cortico-striatal circuits 2: evidence from FMRI. Cerebral Cortex, 22(3), 527–536.CrossRef Google Scholar PubMed

Barak, O., & Tsodyks, M. (2014). Working models of working memory. Current Opinion in Neurobiology, 25, 20–24. https://doi.org/10.1016/j.conb.2013.10.008 CrossRef Google Scholar PubMed

Basso, M. A., & Wurtz, R. H. (2002). Neuronal activity in substantia nigra pars reticulata during target selection. Journal of Neuroscience, 22(5), 1883–1894.CrossRef Google Scholar PubMed

Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10), 7–7. https://doi.org/10.1167/9.10.7 Google Scholar

Bays, P. M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321(5890), 851–854. https://doi.org/10.1126/science.1158023 Google Scholar

Beiser, D. G., & Houk, J. C. (1998). Model of cortical-basal ganglionic processing: encoding the serial order of sensory events. Journal of Neurophysiology, 79, 3168–3188.CrossRef Google Scholar PubMed

Bhandari, A., & Badre, D. (2018). Learning and transfer of working memory gating policies. Cognition, 172, 89–100. https://doi.org/10.1016/j.cognition.2017.12.001 Google Scholar

Bogacz, R. (2013). Basal ganglia: beta oscillations. In Jaeger, D. & Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–5). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-7320-6%2082-1 Google Scholar

Botvinick, M. M., & Plaut, D. C. (2004). Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111(2), 395–429.Google Scholar

Botvinick, M. M., & Plaut, D. C. (2006). Short-term memory for serial order: a recurrent neural network model. Psychological Review, 113, 201–233.Google Scholar

Braver, T. S., & Cohen, J. D. (2000). On the control of control: the role of dopamine in regulating prefrontal function and working memory. In Monsell, S. & Driver, J. (Eds.), Control of Cognitive Processes: Attention and Performance XVIII (pp. 713–737). Cambridge, MA: MIT Press.Google Scholar

Braver, T. S., Paxton, J. L., Locke, H. S., & Barch, D. M. (2009). Flexible neural mechanisms of cognitive control within human prefrontal cortex. Proceedings of the National Academy of Sciences USA, 106(18), 7351–7356.Google Scholar

Brown, J. W., Bullock, D., & Grossberg, S. (2004). How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades. Neural Networks, 17, 471–510.Google Scholar

Brown, R. G., & Marsden, C. D. (1990). Cognitive function in Parkinson’s disease: from description to theory. Trends in Neurosciences, 13, 21–29.CrossRef Google Scholar PubMed

Brown, V. J., & Bowman, E. M. (2002). Rodent models of prefrontal cortical function. Trends in Neurosciences, 25, 340–343.CrossRef Google Scholar PubMed

Burgess, N., & Hitch, G. (2005). Computational models of working memory: putting long-term memory into context. Trends in Cognitive Sciences, 9(11), 535–541. https://doi.org/10.1016/j.tics.2005.09.011 Google Scholar

Chatham, C. H., & Badre, D. (2015). Multiple gates on working memory. Current Opinion in Behavioral Sciences, 1, 23–31. https://doi.org/10.1016/j.cobeha.2014.08.001 CrossRef Google Scholar PubMed

Chatham, C. H., Frank, M., & Badre, D. (2014). Corticostriatal output gating during selection from working memory. Neuron, 81(4), 930–942.Google Scholar

Chatham, C. H., Herd, S. A., Brant, A. M., et al. (2011). From an executive network to executive control: a computational model of the n-back task. Journal of Cognitive Neuroscience, 23, 3598–3619.Google Scholar

Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012). The organization of the human striatum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 108(8), 2242–2263. https://doi.org/10.1152/%20jn.00270.2012 Google Scholar

Clascá, F., Rubio-Garrido, P., & Jabaudon, D. (2012). Unveiling the diversity of thalamocortical neuron subtypes. European Journal of Neuroscience, 35(10), 1524–1532. https://doi.org/10.1111/j.1460-9568.2012.08033.x Google Scholar

Cleeremans, A., Servan-Schreiber, D., & McClelland, J. L. (1989). Finite state automata and simple recurrent networks. Neural Computation, 1(3), 372–381.Google Scholar

Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of automatic processes: a parallel distributed processing model of the Stroop effect. Psychological Review, 97(3), 332–361.Google Scholar

Cole, M. W., Bagic, A., Kass, R., & Schneider, W. (2010). Prefrontal dynamics underlying rapid instructed task learning reverse with practice. Journal of Neuroscience, 30(42), 14245–14254.Google Scholar

Collins, A. G. E., & Frank, M. J. (2013). Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychological Review, 120(1), 190–229.Google Scholar

Collins, A. G. E., & Frank, M. J. (2014). Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychological Review, 121(3), 337–366.Google Scholar

Collins, A. G. E., & Frank, M. J. (2016). Surprise! Dopamine signals mix action, value and error. Nature Neuroscience, 19(1), 3–5. https://doi.org/10.1038/nn.4207 CrossRef Google Scholar PubMed

Courtemanche, R., Fujii, N., & Graybiel, A. M. (2003). Synchronous, focally modulated beta-band oscillations characterize local field potential activity in the striatum of awake behaving monkeys. Journal of Neuroscience, 23(37), 11741–11752.CrossRef Google Scholar PubMed

Cowan, N. (2001). The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.Google Scholar

Cowan, N. (2011). The focus of attention as observed in visual working memory tasks: making sense of competing claims. Neuropsychologia, 49(6), 1401–1406. https://doi.org/10.1016/j.neuropsychologia.2011.01.035 CrossRef Google Scholar PubMed

Cowan, N. (2017). The many faces of working memory and short-term storage. Psychonomic Bulletin & Review, 24(4), 1158–1170. https://doi.org/10.3758/s13423-016-1191-6 Google Scholar

Cowan, N. (2019). Short-term memory based on activated long-term memory: a review in response to Norris (2017). Psychological Bulletin, 145(8), 822–847. https://doi.org/10.1037/bul0000199 Google Scholar

Cowan, N., Nugent, L. D., Elliott, E. M., Ponomarev, I., & Saults, J. S. (1999). The role of attention in the development of short-term memory: age differences in the verbal span of apprehension. Child Development, 70(5), 1082–1097.CrossRef Google Scholar PubMed

Dahlin, E., Neely, A. S., Larsson, A., Backman, L., & Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science, 320(5882), 1510–1512.Google Scholar

Dayan, P. (2007). Bilinearity, rules, and prefrontal cortex. Frontiers in Computational Neuroscience, 1(1), 1–14.Google Scholar

Dayan, P. (2008). Simple substrates for complex cognition. Frontiers in Computational Neuroscience, 2(2), 255.Google Scholar

Dominey, P. F., & Arbib, M. A. (1992). Cortico-subcortical model for generation of spatially accurate sequential saccades. Cerebral Cortex, 2, 153–175.CrossRef Google Scholar PubMed

Dominey, P. F., Arbib, M., & Joseph, J.-P. (1995). A model of corticostriatal plasticity for learning oculomotor associations and sequences. Journal of Cognitive Neuroscience, 7(3), 311–336. https://doi.org/10.1162/jocn.1995.7.3.311 Google Scholar

Dunbar, K., & MacLeod, C. M. (1984). A horse race of a different color: Stroop interference patterns with transformed words. Journal of Experimental Psychology. Human Perception and Performance, 10, 622–639.Google Scholar

Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3 suppl., 1184–1191.Google Scholar

Economo, M. N., Viswanathan, S., Tasic, B., et al. (2018). Distinct descending motor cortex pathways and their roles in movement. Nature, 563(7729), 79–84. https://doi.org/10.1038/s41586-018-0642-9 Google Scholar

Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.Google Scholar

Elston, G. N. (2003). Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. Cerebral Cortex, 13(11), 1124–1138.CrossRef Google Scholar PubMed

Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. (1999). Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. Journal of Experimental Psychology. General, 128, 309–331.CrossRef Google Scholar PubMed

Ferry, A. T., Öngür, D., An, X., & Price, J. L. (2000). Prefrontal cortical projections to the striatum in macaque monkeys: evidence for an organization related to prefrontal networks. Journal of Comparative Neurology, 425(3), 447–470.Google Scholar

Flaherty, A. W., & Graybiel, A. M. (1993a). Output architecture of the primate putamen. Journal of Neuroscience, 13(8), 3222–3237.Google Scholar

Flaherty, A. W., & Graybiel, A. M. (1993b). Two input systems for body representations in the primate striatal matrix: experimental evidence in the squirrel monkey. Journal of Neuroscience, 13(3), 1120–1137.Google Scholar

Frank, M. J. (2005). When and when not to use your subthalamic nucleus: lessons from a computational model of the basal ganglia. In Seth, A. K., Prescott, T. J., & Bryson, J. J. (Eds.), Modelling Natural Action Selection: Proceedings of an International Workshop (pp. 53–60). Sussex: AISB.Google Scholar

Frank, M. J., & Badre, D. (2012). Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. Cerebral Cortex, 22(3), 509–526.Google Scholar

Frank, M. J., Loughry, B., & O’Reilly, R. C. (2001). Interactions between the frontal cortex and basal ganglia in working memory: a computational model. Cognitive, Affective, and Behavioral Neuroscience, 1, 137–160.Google Scholar

Frank, M. J., & O’Reilly, R. C. (2006). A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol. Behavioral Neuroscience, 120, 497–517.Google Scholar

Friedman, N., Miyake, A., Corley, R., Young, S., Defries, J., & Hewitt, J. (2006). Not all executive functions are related to intelligence. Psychological Science, 17(2), 172–179.CrossRef Google Scholar PubMed

Fries, W. (1984). Cortical projections to the superior colliculus in the macaque monkey: a retrograde study using horseradish peroxidase. Journal of Comparative Neurology, 230(1), 55–76. https://doi.org/10.1002/%20cne.902300106 Google Scholar

Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: the relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin & Review, 17(5), 673–679. https://doi.org/10.3758/17.5.673 CrossRef Google Scholar

Funahashi, S., Bruce, C. J., & Goldman-Rakic, P. S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. Journal of Neurophysiology, 61(2), 331–349.Google Scholar

Fusi, S., Miller, E. K., & Rigotti, M. (2016). Why neurons mix: high dimensionality for higher cognition. Current Opinion in Neurobiology, 37, 66–74. https://doi.org/10.1016/j.conb.2016.01.010 Google Scholar

Fuster, J. M., & Alexander, G. E. (1971). Neuron activity related to short-term memory. Science, 173, 652–654.Google Scholar

Gayet, S., Paffen, C. L. E., & Van der Stigchel, S. (2013). Information matching the content of visual working memory is prioritized for conscious access. Psychological Science, 24(12), 2472–2480. https://doi.org/10.1177/0956797613495882 Google Scholar

Gerfen, C. R., & Surmeier, D. J. (2011). Modulation of striatal projection systems by dopamine. Annual Review of Neuroscience, 34, 441–466.Google Scholar

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: continual prediction with LSTM. Neural Computation, 12, 2451–2471.Google Scholar

Giguere, M., & Goldman-Rakic, P. S. (1988). Mediodorsal nucleus: areal, laminar, and tangential distribution of afferents and efferents in the frontal lobe of rhesus monkeys. Journal of Comparative Neurology, 277(2), 195–213. https://doi.org/10.1002/cne.902770204 Google Scholar

Goldman-Rakic, P. S. (1995). Cellular basis of working memory. Neuron, 14(3), 477–485.Google Scholar

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.Google Scholar

Gorgoraptis, N., Catalao, R. F. G., Bays, P. M., & Husain, M. (2011). Dynamic updating of working memory resources for visual objects. Journal of Neuroscience, 31(23), 8502–8511. https://doi.org/10.1523/%20JNEUROSCI.0208-11.2011 Google Scholar

Graybiel, A. M. (1995). Building action repertoires: memory and learning functions of the basal ganglia. Current Opinion in Neurobiology, 5(6), 733–741.Google Scholar

Graybiel, A. M., Flaherty, A. W., & Gimenez-Amaya, J. M. (1991). Striosomes and matrisomes. In Bernardi, G., Carpenter, M. B., Di Chiara, G., Morelli, M., & Stanzione, P. (Eds.), The Basal Ganglia III: Proceedings of the Third Triennial Meeting of the International Basal Ganglia Society (pp. 3–12). New York, NY: Plenum Press.Google Scholar

Gruber, A. J., Dayan, P., Gutkin, B. S., & Solla, S. A. (2006). Dopamine modulation in the basal ganglia locks the gate to working memory. Journal of Computational Neuroscience, 20(2), 153–166.Google Scholar

Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., & Svoboda, K. (2017). Maintenance of persistent activity in a frontal thalamocortical loop. Nature, 545(7653), 181–186. https://doi.org/10.1038/nature22324 Google Scholar

Haber, S. N. (2003). The primate basal ganglia: parallel and integrative networks. Journal of Chemical Neuroanatomy, 26(4), 317–330.Google Scholar

Haber, S. N., & Knutson, B. (2010). The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology, 35, 4–26.Google Scholar

Haith, A. M., Pakpoor, J., & Krakauer, J. W. (2016). Independence of movement preparation and movement initiation. Journal of Neuroscience, 36(10), 3007–3015. https://doi.org/10.1523/JNEUROSCI.3245-15.2016 Google Scholar

Hardman, C. D., Henderson, J. M., Finkelstein, D. I., Horne, M. K., Paxinos, G., & Halliday, G. M. (2002). Comparison of the basal ganglia in rats, marmosets, macaques, baboons, and humans: volume and neuronal number for the output, internal relay, and striatal modulating nuclei. Journal of Comparative Neurology, 445(3), 238–255.Google Scholar

Harris, K. D., & Shepherd, G. M. G. (2015). The neocortical circuit: themes and variations. Nature Neuroscience, 18(2), 170–181. https://doi.org/10.1038/nn.3917 CrossRef Google Scholar PubMed

Hattox, A. M., & Nelson, S. B. (2007). Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. Journal of Neurophysiology, 98, 3330–3340.CrossRef Google Scholar

Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2006). Banishing the homunculus: making working memory work. Neuroscience, 139, 105–118.Google Scholar

Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2007). Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1485), 1601–1613.Google Scholar

Herd, S. A., O’Reilly, R. C., Hazy, T. E., Chatham, C. H., Brant, A. M., & Friedman, N. P. (2014). A neural network model of individual differences in task switching abilities. Neuropsychologia, 62, 375–389. https://doi.org/10.1016/j.neuropsychologia.2014.04.014.Google Scholar

Hikida, T., Kimura, K., Wada, N., Funabiki, K., & Nakanishi, S. (2010). Distinct roles of synaptic transmission in direct and indirect striatal pathways to reward and aversive behavior. Neuron, 66, 896–907.Google Scholar

Hikosaka, O., Sakamoto, M., & Usui, S. (1989). Functional properties of monkey caudate neurons. III. Activities related to expectation of target and reward. Journal of Neurophysiology, 61(4), 814–832.CrossRef Google Scholar PubMed

Hikosaka, O., & Wurtz, R. H. (1983). Visual and oculomotor functions of monkey substantia nigra pars reticulata. III. Memory-contingent visual and saccade responses. Journal of Neurophysiology, 49(5), 1268–1284.Google Scholar

Hinton, G. E., McClelland, J. L., & Rumelhart, D. E. (1986). Distributed representations. In Rumelhart, D. E., McClelland, J. L., & P. R. Group (Eds.), Parallel Distributed Processing. Volume 1: Foundations (pp. 77–109). Cambridge, MA: MIT Press.Google Scholar

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.CrossRef Google Scholar PubMed

Houk, J. C. (2005). Agents of the mind. Biological Cybernetics, 92(6), 427–437.Google Scholar

Huang, T.-R., Hazy, T. E., Herd, S. A., & O’Reilly, R. C. (2013). Assembling old tricks for new tasks: a neural model of instructional learning and control. Journal of Cognitive Neuroscience, 25(6), 843–851.Google Scholar

Ilinsky, I. A., Jouandet, M. L., & Goldman-Rakic, P. S. (1985). Organization of the nigrothalamocortical system in the rhesus monkey. Journal of Comparative Neurology, 236(3), 315–330. https://doi.org/10.1002/%20cne.902360304 Google Scholar

Jilk, D., Lebiere, C., O’Reilly, R. C., & Anderson, J. (2008). SAL: an explicitly pluralistic cognitive architecture. Journal of Experimental & Theoretical Artificial Intelligence, 20(3), 197–218.Google Scholar

Joel, D., & Weiner, I. (2000). The connections of the dopaminergic system with the striatum in rats and primates: an analysis with respect to the functional and compartmental organization of the striatum. Neuroscience, 96, 451–474.Google Scholar

Jones, E. G. (1998a). A new view of specific and nonspecific thalamocortical connections. Advances in Neurology, 77, 49–71.Google Scholar

Jones, E. G. (1998b). Viewpoint: the core and matrix of thalamic organization. Neuroscience, 85(2), 331–345. https://doi.org/10.1016/S0306-4522(97)00581-2 Google Scholar

Jones, E. G. (2007). The Thalamus (2nd ed.). Cambridge: Cambridge University Press.Google Scholar

Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the 8th Confererence of the Cognitive Science Society (pp. 531–546). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

Jung, W. H., Jang, J. H., Park, J. W., et al. (2014). Unravelling the intrinsic functional organization of the human striatum: a parcellation and connectivity study based on resting-state fMRI. PLOS One, 9(9), e106768. https://doi.org/10.1371/%20journal.pone.0106768 Google Scholar

Kansky, K., Silver, T., Mély, D. A., et al. (2017). Schema networks: zero-shot transfer with a generative causal model of intuitive physics. arXiv:1706.04317 [cs].Google Scholar

Kimura, M., Kato, M., & Shimazaki, H. (1990). Physiological properties of projection neurons in the monkey striatum to the globus pallidus. Experimental Brain Research, 82(3), 672–676. https://doi.org/10.1007/%20bf00228811 CrossRef Google Scholar PubMed

Kravitz, A. V., Tye, L. D., & Kreitzer, A. C. (2012). Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nature Neuroscience, 15(6), 816–818.Google Scholar

Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C. (2013). Indirection and symbol-like processing in the prefrontal cortex and basal ganglia. Proceedings of the National Academy of Sciences, 110(41), 16390–16395.Google Scholar

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. Journal of Comparative Neurology, 359(1), 131–143.CrossRef Google Scholar PubMed

Krystal, J. H., Abi-Saab, W., Perry, E., et al. (2005). Preliminary evidence of attenuation of the disruptive effects of the NMDA glutamate receptor antagonist, ketamine, on working memory by pretreatment with the group II metabotropic glutamate receptor agonist, LY354740, in healthy human subjects. Psychopharmacology, 179(1), 303–309. https://doi.org/10.1007/s00213-004-1982-8 Google Scholar

Kubota, K., & Niki, H. (1971). Prefrontal cortical unit activity and delayed alternation performance in monkeys. Journal of Neurophysiology, 34(3), 337–347.Google Scholar

Kuramoto, E., Furuta, T., Nakamura, K. C., Unzai, T., Hioki, H., & Kaneko, T. (2009). Two types of thalamocortical projections from the motor thalamic nuclei of the rat: a single neuron-tracing study using viral vectors. Cerebral Cortex, 19(9), 2065–2077.Google Scholar

Kuramoto, E., Ohno, S., Furuta, T., et al. (2015). Ventral medial nucleus neurons send thalamocortical afferents more widely and more preferentially to layer 1 than neurons of the ventral anterior–ventral lateral nuclear complex in the rat. Cerebral Cortex, 25(1), 221–235. https://doi.org/10.1093/cercor/bht216 Google Scholar

Lamme, V. A. F. (2006). Towards a true neural stance on consciousness. Trends in Cognitive Sciences, 10(11), 494–501. https://doi.org/10.1016/j.tics.2006.09.001 Google Scholar

Larkum, M. E., Petro, L. S., Sachdev, R. N. S., & Muckli, L. (2018). A perspective on cortical layering and layer-spanning neuronal elements. Frontiers in Neuroanatomy, 12, 1–9. https://doi.org/10.3389/fnana.2018.00056 Google Scholar

Leichnetz, G. R., Spencer, R. F., Hardy, S. G., & Astruc, J. (1981). The prefrontal corticotectal projection in the monkey; an anterograde and retrograde horseradish peroxidase study. Neuroscience, 6(6), 1023–1041.Google Scholar

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 & 46). Journal of Comparative Neurology, 338, 360–376.Google Scholar

Logie, R. H. (2018). Scientific advance and theory integration in working memory: comment on Oberauer et al. (2018). Psychological Bulletin; Washington, 144(9), 959.Google Scholar

Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281.Google Scholar

Luck, S. J., & Vogel, E. K. (2013). Visual working memory capacity: from psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences, 17(8), 391–400. https://doi.org/10.1016/%20j.tics.2013.06.006 Google Scholar

Ma, W. J., Husain, M., & Bays, P. M. (2014). Changing concepts of working memory. Nature Neuroscience, 17(3), 347–356. https://doi.org/10.1038/nn.3655 CrossRef Google Scholar PubMed

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. https://doi.org/10.1038/nature12742 Google Scholar

Masse, N. Y., Yang, G. R., Song, H. F., Wang, X.-J., & Freedman, D. J. (2019). Circuit mechanisms for the maintenance and manipulation of information in working memory. Nature Neuroscience, 22(7), 1159–1167. https://doi.org/10.1038/s41593-019-0414-3 Google Scholar

McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11(1), 103–107.Google Scholar

Middleton, F. A., & Strick, P. L. (2000). Basal ganglia output and cognition: evidence from anatomical, behavioral, and clinical studies. Brain and Cognition, 42(2), 183–200.Google Scholar

Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.Google Scholar

Miller, E. K., & Desimone, R. (1994). Parallel neuronal mechanisms for short-term memory. Science, 263, 520–522.CrossRef Google Scholar PubMed

Miller, E. K., Erickson, C. A., & Desimone, R. (1996). Neural mechanisms of visual working memory in prefrontal cortex of the macaque. Journal of Neuroscience, 16(16), 5154–5167.Google Scholar

Miller, G. A. (1956). The Magical Number Seven, Plus Or Minus Two: Some Limits On Our Capacity For Processing Information (vol. 101). Indiana: Bobbs-Merrill.Google Scholar

Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Holt.Google Scholar

Mingus, B., Kriete, T., Herd, S., Wyatte, D., Latimer, K., & O’Reilly, R. (2011). Generalization of figure-ground segmentation from binocular to monocular vision in an embodied biological brain model. In J. Schmidhuber, K. R. Thórisson, & M. Looks (Eds.), Artificial General Intelligence (pp. 351–356). London: Springer. https://doi.org/10.1007/978-3-642-22887-2_42 Google Scholar

Mink, J. W. (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology, 50(4), 381–425.Google Scholar

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis. Cognitive Psychology, 41, 49–100.Google Scholar

Miyake, A., & Shah, P. (Eds.). (1999). Models of Working Memory: Mechanisms of Active Maintenance and Executive Control. New York, NY: Cambridge University Press.Google Scholar

Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.CrossRef Google Scholar PubMed

Moghaddam, B., & Adams, B. W. (1998). Reversal of phencyclidine effects by a group II metabotropic glutamate receptor agonist in rats. Science, 281(5381), 1349–1352. https://doi.org/10.1126/%20science.281.5381.1349 Google Scholar

Mollick, J. A., Hazy, T. E., Krueger, K. A., et al. (2020). A systems-neuroscience model of phasic dopamine. Psychological Review, 127(6), 972–1021. https://doi.org/10.1037/rev0000199 Google Scholar

Monchi, O., Petrides, M., Strafella, A. P., Worsley, K. J., & Doyon, J. (2006). Functional role of the basal ganglia in the planning and execution of actions. Annals of Neurology, 59(2), 257–264.Google Scholar

Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16(5), 1936–1947.Google Scholar

Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain, 120 (Pt 4), 701–722.Google Scholar

Moustafa, A. A., Sherman, S. J., & Frank, M. J. (2008). A dopaminergic basis for working memory, learning, and attentional shifting in Parkinson’s Disease. Neuropsychologia, 46, 3144–3156.Google Scholar

Münkle, M. C., Waldvogel, H. J., & Faull, R. L. M. (2000). The distribution of calbindin, calretinin and parvalbumin immunoreactivity in the human thalamus. Journal of Chemical Neuroanatomy, 19(3), 155–173. https://doi.org/10.1016/S0891-0618(00)00060-0 Google Scholar

Nassar, M. R., Helmers, J. C., & Frank, M. J. (2018). Chunking as a rational strategy for data compression in visual working memory. Psychological Review, 125(4), 486–511. https://doi.org/10.1037/%20rev0000101 Google Scholar

Newell, A., & Simon, H. (1956). The logic theory machine: a complex information processing system. IRE Transactions on Information Theory, 2(3), 61–79. https://doi.org/10.1109/TIT.1956.1056797 Google Scholar

Nyberg, L., Andersson, M., Forsgren, L., et al. (2009). Striatal dopamine D2 binding is related to frontal BOLD response during updating of long-term memory representations. NeuroImage, 46(4), 1194–1199.Google Scholar

Oberauer, K., Lewandowsky, S., Awh, E., et al. (2018a). Benchmarks for models of short-term and working memory. Psychological Bulletin, 144(9), 885–958. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000153 Google Scholar

Oberauer, K., Lewandowsky, S., Awh, E., et al. (2018b). Benchmarks provide common ground for model development: reply to Logie (2018) and Vandierendonck (2018). Psychological Bulletin, 144(9), 972–977. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000165Google Scholar

Öngür, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10(3), 206–219.Google Scholar

O’Reilly, R. C. (1996). Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8(5), 895–938. https://doi.org/10.1162/neco.1996.8.5.895 Google Scholar

O’Reilly, R. C. (2006). Biologically based computational models of high-level cognition. Science, 314(5796), 91–94.Google Scholar

O’Reilly, R. C., Braver, T. S., & Cohen, J. D. (1999). A biologically based computational model of working memory. In Miyake, A. & Shah, P. (Eds.), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 375–411). New York, NY: Cambridge University Press.Google Scholar

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.Google Scholar

O’Reilly, R. C., Frank, M. J., Hazy, T. E., & Watz, B. (2007). PVLV: the primary value and learned value Pavlovian learning algorithm. Behavioral Neuroscience, 121(1), 31–49.Google Scholar

O’Reilly, R. C., Hazy, T. E., & Herd, S. A. (2016). The Leabra cognitive architecture: how to play 20 principles with nature and win! In Chipman, S. (Ed.), Oxford Handbook of Cognitive Science. Oxford: Oxford University Press.Google Scholar

O’Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E., & Contributors. (2012). Computational Cognitive Neuroscience. Wiki Book, 1st ed. Available from:https://compcogneuro.org Google Scholar

O’Reilly, R. C., Nair, A., Russin, J. L., & Herd, S. A. (2020). How sequential interactive processing within frontostriatal loops supports a continuum of habitual to controlled processing. Frontiers in Psychology, 11, 380. https://doi.org/10.3389/fpsyg.2020.00380 Google Scholar

O’Reilly, R. C., Noelle, D. C., Braver, T. S., & Cohen, J. D. (2002). Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. Cerebral Cortex, 12, 246–257.Google Scholar

O’Reilly, R. C., Petrov, A. A., Cohen, J. D., Lebiere, C. J., Herd, S. A., & Kriete, T. (2014). How limited systematicity emerges: a computational cognitive neuroscience approach. In Calvo, I. P. & Symons, J. (Eds.), The Architecture of Cognition: Rethinking Fodor and Pylyshyn’s Systematicity Challenge. Cambridge, MA: MIT Press.Google Scholar

O’Reilly, R. C., Russin, J. L., & Herd, S. A. (2019). Computational models of motivated frontal function. In D’Esposito, M. & Grafman, J. (Eds.), Handbook of Clinical Neurology (vol. 163, pp. 317–332). Amsterdam: Elsevier.Google Scholar

O’Reilly, R. C., Russin, J. L., Zolfaghar, M., & Rohrlich, J. (2020). Deep predictive learning in neocortex and pulvinar. arXiv:2006.14800 [q-bio]Google Scholar

Pakkenberg, B., & Gundersen, H. J. (1997). Neocortical neuron number in humans: effect of sex and age. Journal of Comparative Neurology, 384(2), 312–320.Google Scholar

Pauli, W. M., O’Reilly, R. C., Yarkoni, T., & Wager, T. D. (2016). Regional specialization within the human striatum for diverse psychological functions. Proceedings of the National Academy of Sciences, 113(7), 1907–1912. https://doi.org/10.1073/pnas.1507610113 Google Scholar

Pertzov, Y., Bays, P. M., Joseph, S., & Husain, M. (2013). Rapid forgetting prevented by retrospective attention cues. Journal of Experimental Psychology. Human Perception and Performance, 39(5), 1224–1231. https://doi.org/10.1037/a0030947 Google Scholar

Phillips, J. W., Schulmann, A., Hara, E., et al. (2019). A repeated molecular architecture across thalamic pathways. Nature Neuroscience, 22(11), 1925–1935. https://doi.org/10.1038/s41593-019-0483-3 Google Scholar

Plenz, D., & Wickens, J. R. (2010). The striatal skeleton: medium spiny projection neurons and their lateral connections. In Steiner, H. & Tseng, K. Y. (Eds.), Handbook of Basal Ganglia Structure and Function (pp. 99–112). New York, NY: Academic Press.Google Scholar

Rac-Lubashevsky, R., & Frank, M. J. (2020). Analogous computations in working memory input, output and motor gating: electrophysiological and computational modeling evidence. bioRxiv, 2020.12.21.423791. https://doi.org/10.1101/2020.12.21.423791 Google Scholar

Ramaswamy, S., & Markram, H. (2015). Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron. Frontiers in Cellular Neuroscience, 9, 1–9. https://doi.org/10.3389/fncel.2015.00233 Google Scholar

Rao, S. G., Williams, G. V., & Goldman-Rakic, P. S. (1999). Isodirectional tuning of adjacent interneurons and pyramidal cells during working memory: evidence for microcolumnar organization in PFC. Journal of Neurophysiology, 81(4), 1903–1916.Google Scholar

Redondo, R. L., & Morris, R. G. M. (2011). Making memories last: the synaptic tagging and capture hypothesis. Nature Reviews Neuroscience, 12(1), 17–30. https://doi.org/10.1038/nrn2963 Google Scholar

Rikhye, R. V., Gilra, A., & Halassa, M. M. (2018). Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nature Neuroscience, 21(12), 1753–1763. https://doi.org/10.1038/s41593-018-0269-z Google Scholar

Roberts, B. M., Shaffer, C. L., Seymour, P. A., Schmidt, C. J., Williams, G. V., & Castner, S. A. (2010). Glycine transporter inhibition reverses ketamine-induced working memory deficits. NeuroReport, 21(5), 390–394. https://doi.org/10.1097/WNR.0b013e3283381a4e Google Scholar

Robinson, A. J., & Fallside, F. (1987). The utility driven dynamic error propagation network (Tech. Rep. No. CUED/F-INFENG/TR.1). Cambridge: Cambridge University Engineering Department.Google Scholar

Rougier, N. P., Noelle, D., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and the flexibility of cognitive control: rules without symbols. Proceedings of the National Academy of Sciences, 102(20), 7338–7343.Google Scholar

Rougier, N. P., & O‘Reilly, R. C. (2002). Learning representations in a gated prefrontal cortex model of dynamic task switching. Cognitive Science, 26, 503–520.CrossRef Google Scholar

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(9), 533–536.Google Scholar

Sanders, H., Berends, M., Major, G., Goldman, M. S., & Lisman, J. E. (2013). NMDA and GABAB (KIR) conductances: the “perfect couple” for bistability. Journal of Neuroscience, 33(2), 424–429. https://doi.org/10.1523/JNEUROSCI.1854-12.2013 Google Scholar

Schmidhuber, J., Gers, F., & Eck, D. (2002). Learning nonregular languages: a comparison of simple recurrent networks and LSTM. Neural Computation, 14(9), 2039–2042.Google Scholar

Schmidt, R., Ruiz, M. H., Kilavik, B. E., Lundqvist, M., Starr, P. A., & Aron, A. R. (2019). Beta oscillations in working memory, executive control of movement and thought, and sensorimotor function. Journal of Neuroscience, 39(42), 8231–8238. https://doi.org/10.1523/JNEUROSCI.1163-19.2019 Google Scholar

Schroll, H., Vitay, J., & Hamker, F. H. (2012). Working memory and response selection: a computational account of interactions among cortico-basalganglio-thalamic loops. Neural Networks, 26, 59–74. https://doi.org/10.1016/j.neunet.2011.10.008 Google Scholar

Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.Google Scholar

Seamans, J. K., & Yang, C. R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in Neurobiology, 74(1), 1–57.Google Scholar

Seth, A. K., Dienes, Z., Cleeremans, A., Overgaard, M., & Pessoa, L. (2008). Measuring consciousness: relating behavioural and neurophysiological approaches. Trends in Cognitive Sciences, 12(8), 314–321. https://doi.org/10.1016/j.tics.2008.04.008 CrossRef Google Scholar PubMed

Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190.Google Scholar

Sommer, M. A., & Wurtz, R. H. (2000). Composition and topographic organization of signals sent from the frontal eye field to the superior colliculus. Journal of Neurophysiology, 83(4), 1979–2001.Google Scholar

Stelzel, C., Basten, U., Montag, C., Reuter, M., & Fiebach, C. J. (2010). Frontostriatal involvement in task switching depends on genetic differences in D2 receptor density. Journal of Neuroscience, 30(42), 14205–14212.Google Scholar

Stocco, A., Lebiere, C., & Anderson, J. (2010). Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychological Review, 117, 541–574.Google Scholar

Stokes, M. G. (2015). ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends in Cognitive Sciences, 19(7), 394–405. https://doi.org/10.1016/j.tics.2015.05.004 Google Scholar

Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013). Dynamic coding for cognitive control in prefrontal cortex. Neuron, 78(2), 364–375. https://doi.org/10.1016/j.neuron.2013.01.039 Google Scholar

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662.Google Scholar

Tanibuchi, I., Kitano, H., & Jinnai, K. (2009a). Substantia nigra output to prefrontal cortex via thalamus in monkeys. I. Electrophysiological identification of thalamic relay neurons. Journal of Neurophysiology, 102(5), 2933–2945.Google Scholar

Tanibuchi, I., Kitano, H., & Jinnai, K. (2009b). Substantia nigra output to prefrontal cortex via thalamus in monkeys. II. Activity of thalamic relay neurons in delayed conditional go/no-go discrimination task. Journal of Neurophysiology, 102(5116), 2946–2954.Google Scholar

Todd, M. T., Niv, Y., & Cohen, J. D. (2008). Learning to use working memory in partially observable environments through dopaminergic reinforcement. In Koller, D. (Ed.), Advances in Neural Information Processing Systems (NIPS) (vol. 21). Red Hook, NY: Curran Associates.Google Scholar

Uylings, H., Groenewegen, H., & Kolb, B. (2003). Do rats have a prefrontal cortex? Behavioural Brain Research, 146(1–2), 3–17.Google Scholar

van Moorselaar, D., Theeuwes, J., & Olivers, C. N. L. (2014). In competition for the attentional template: can multiple items within visual working memory guide attention? Journal of Experimental Psychology. Human Perception and Performance, 40(4), 1450–1464. https://doi.org/10.1037/a0036229 Google Scholar

Vandierendonck, A. (2018). Working memory benchmarks: a missed opportunity. Comment on Oberauer et al. (2018). Psychological Bulletin, 144(9), 963–971. https://doi.org/colorado.idm.oclc.org/10.1037/bul0000159 Google Scholar

Vinyals, O., Babuschkin, I., Czarnecki, W. M., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z Google Scholar

Voytek, B., & Knight, R. T. (2010). Prefrontal cortex and basal ganglia contributions to visual working memory. Proceedings of the National Academy of Sciences, 107(42), 18167–18172.Google Scholar

Wang, M., Yang, Y., Wang, C.-J., et al. (2013). NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex. Neuron, 77(4), 736–749. https://doi.org/10.1016/j.neuron.2012.12.032 Google Scholar

Wang, X.-J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences, 24(8), 455–463.Google Scholar

Wang, Y., Markram, H., Goodman, P. H., Berger, T. K., Ma, J., & Goldman-Rakic, P. S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nature Neuroscience, 9(4), 534–542.Google Scholar

Watanabe, Y., & Funahashi, S. (2012). Thalamic mediodorsal nucleus and working memory. Neuroscience & Biobehavioral Reviews, 36(1), 134–142. https://doi.org/10.1016/j.neubiorev.2011.05.003 Google Scholar

Watanabe, Y., Takeda, K., & Funahashi, S. (2009). Population vector analysis of primate mediodorsal thalamic activity during oculomotor delayed-response performance. Cerebral Cortex, 19, 1313–1321.Google Scholar

Wei, Z., Wang, X.-J., & Wang, D.-H. (2012). From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization. Journal of Neuroscience, 32(33), 11228–11240.Google Scholar

Werbos, P. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences. (Unpublished doctoral dissertation). Cambridge, MA: Harvard University Press.Google Scholar

Werbos, P. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550–1560. https://doi.org/10.1109/5.58337 Google Scholar

Whittington, J. C. R., & Bogacz, R. (2019). Theories of error back-propagation in the brain. Trends in Cognitive Sciences, 23(3), 235–250. https://doi.org/10.1016/j.tics.2018.12.005 Google Scholar

Wickens, J. R., Alexander, M. E., & Miller, R. (1991). Two dynamic modes of striatal function under dopaminergic-cholinergic control: simulation and analysis of a model. Synapse, 8(1), 1–12. https://doi.org/10.1002/syn.890080102 Google Scholar

Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4(12), 1120–1135. https://doi.org/10.1167/4.12.11 Google Scholar

Williams, A., & Phillips, J. (2020). Transfer reinforcement learning using output-gated working memory. Proceedings of the AAAI Conference on Artificial Intelligence, 34(2), 1324–1331. https://doi.org/10.1609/aaai.v34i02.5488 Google Scholar

Williams, R. J., & Zipser, D. (1992). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Chauvin, Y. & Rumelhart, D. E. (Eds.), Backpropagation: Theory, Architectures and Applications. Hillsdale, NJ: Erlbaum.Google Scholar

Winnubst, J., Bas, E., Ferreira, T. A., et al. (2019). Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain. Cell, 179(1), 268–281.e13. https://doi.org/10.1016/j.cell.2019.07.042 Google Scholar

Wyder, M. T., Massoglia, D. P., & Stanford, T. R. (2004). Contextual modulation of central thalamic delay-period activity: representation of visual and saccadic goals. Journal of Neurophysiology, 91(6), 2628–2648.Google Scholar

Yehene, E., Meiran, N., & Soroker, N. (2008). Basal ganglia play a unique role in task switching within the frontal-subcortical circuits: evidence from patients with focal lesions. Journal of Cognitive Neuroscience, 20, 1079–1093.Google Scholar

Yttri, E. A., & Dudman, J. T. (2016). Opponent and bidirectional control of movement velocity in the basal ganglia. Nature, 533(7603), 402–406. https://doi.org/10.1038/nature17639 Google Scholar

Zalocusky, K. A., Ramakrishnan, C., Lerner, T. N., Davidson, T. J., Knutson, B., & Deisseroth, K. (2016). Nucleus accumbens D2R cells signal prior outcomes and control risky decision-making. Nature, 531(7596), 642–646. https://doi.org/10.1038/nature17400 Google Scholar

Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233–235.Google Scholar

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