1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Neuroscience
  4. Volume 46, 2023
  5. Article

Annual Review of Neuroscience

Volume 46, 2023

The Computational and Neural Bases of Context-Dependent Learning

Abstract

Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

Keyword(s): Bayesian inference, context-dependent learning, continual learning, hippocampus, learning, memory, motor cortex, neural computation, prefrontal cortex, thalamus
Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-092322-100402
2023-07-10
2024-04-27
Loading full text...

Full text loading...

/deliver/fulltext/neuro/46/1/annurev-neuro-092322-100402.html?itemId=/content/journals/10.1146/annurev-neuro-092322-100402&mimeType=html&fmt=ahah

Literature Cited

  1. Aitchison L, Jegminat J, Menendez JA, Pfister JP, Pouget A, Latham PE. 2021. Synaptic plasticity as Bayesian inference. Nat. Neurosci. 24:4565–71
     [Google Scholar]
  2. Aljundi R, Chakravarty P, Tuytelaars T. 2017. Expert gate: Lifelong learning with a network of experts. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition3366–75. Piscataway, NJ: IEEE
     [Google Scholar]
  3. Anderson MC, Hulbert JC. 2021. Active forgetting: adaptation of memory by prefrontal control. Annu. Rev. Psychol. 72:1–36
     [Google Scholar]
  4. Banai K, Ortiz JA, Oppenheimer JD, Wright BA. 2010. Learning two things at once: differential constraints on the acquisition and consolidation of perceptual learning. Neuroscience 165:2436–44
     [Google Scholar]
  5. Batsikadze G, Diekmann N, Ernst TM, Klein M, Maderwald S et al. 2022. The cerebellum contributes to context-effects during fear extinction learning: a 7T fMRI study. NeuroImage 253:119080
     [Google Scholar]
  6. Becker S. 2005. A computational principle for hippocampal learning and neurogenesis. Hippocampus 15:6722–38
     [Google Scholar]
  7. Behrens TEJ, Muller TH, Whittington JC, Mark S, Baram AB et al. 2018. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100:490–509
     [Google Scholar]
  8. Bernardi S, Benna MK, Rigotti M, Munuera J, Fusi S, Salzman CD. 2020. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183:4954–67.e21
     [Google Scholar]
  9. Botvinick MM. 2008. Hierarchical models of behavior and prefrontal function. Trends Cogn. Sci. 12:5201–8
     [Google Scholar]
  10. Bouton ME, King DA. 1983. Contextual control of the extinction of conditioned fear: tests for the associative value of the context. J. Exp. Psychol. Anim. Behav. Process. 9:3248–65
     [Google Scholar]
  11. Bradfield LA, Dezfouli A, van Holstein M, Chieng B, Balleine BW. 2015. Medial orbitofrontal cortex mediates outcome retrieval in partially observable task situations. Neuron 88:61268–80
     [Google Scholar]
  12. Braun DA, Aertsen A, Wolpert DM, Mehring C. 2009. Motor task variation induces structural learning. Curr. Biol. 19:4352–57
     [Google Scholar]
  13. Burge J, Ernst MO, Banks MS. 2008. The statistical determinants of adaptation rate in human reaching. J. Vis. 8:420.1–19
     [Google Scholar]
  14. Butter CM. 1969. Perseveration in extinction and in discrimination reversal tasks following selective frontal ablations in Macaca mulatta. Physiol. Behav. 4:2163–71
     [Google Scholar]
  15. Chan SCY, Niv Y, Norman KA. 2016. A probability distribution over latent causes, in the orbitofrontal cortex. J. Neurosci. 36:307817–28
     [Google Scholar]
  16. Chen S, He L, Huang AJ, Boehringer R, Robert V et al. 2020. A hypothalamic novelty signal modulates hippocampal memory. Nature 586:7828270–74
     [Google Scholar]
  17. Collins A, Koechlin E. 2012. Reasoning, learning, and creativity: frontal lobe function and human decision-making. PLOS Biol. 10:3e1001293
     [Google Scholar]
  18. Collins AG, Frank MJ. 2013. Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychol. Rev. 120:1190–229
     [Google Scholar]
  19. Collins AGE, Frank MJ. 2016. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition 152:160–69
     [Google Scholar]
  20. Crossley MJ, Ashby FG, Maddox WT. 2013. Erasing the engram: the unlearning of procedural skills. J. Exp. Psychol. Gen. 142:3710–41
     [Google Scholar]
  21. Dayan P, Abbott LF. 2005. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems Cambridge, MA: MIT Press
  22. Donoso M, Collins AGE, Koechlin E. 2014. Foundations of human reasoning in the prefrontal cortex. Science 344:61911481–86
     [Google Scholar]
  23. Dosher BA, Liu J, Chu W, Lu ZL 2020. Roving: the causes of interference and re-enabled learning in multi-task visual training. J. Vis. 20:69
     [Google Scholar]
  24. Echeveste R, Aitchison L, Hennequin G, Lengyel M. 2020. Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference. Nat. Neurosci. 23:91138–49
     [Google Scholar]
  25. Flesch T, Balaguer J, Dekker R, Nili H, Summerfield C. 2018. Comparing continual task learning in minds and machines. PNAS 115:44E10313–22
     [Google Scholar]
  26. Flesch T, Nagy DG, Saxe A, Summerfield C 2022. Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals. arXiv:2203.11560 [q-bio.NC]
  27. Franklin NT, Frank MJ. 2020. Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning. PLOS Comput. Biol. 16:4e1007720
     [Google Scholar]
  28. Frémaux N, Gerstner W. 2015. Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules. Front. Neural Circuits 9:85
     [Google Scholar]
  29. French RM. 1999. Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3:4128–35
     [Google Scholar]
  30. Gershman SJ, Blei DM, Niv Y. 2010. Context, learning, and extinction. Psychol. Rev. 117:1197–209
     [Google Scholar]
  31. Gershman SJ, Monfils MH, Norman KA, Niv Y 2017. The computational nature of memory modification. eLife 6:e23763
     [Google Scholar]
  32. Gershman SJ, Radulescu A, Norman KA, Niv Y. 2014. Statistical computations underlying the dynamics of memory updating. PLOS Comput. Biol. 10:11e1003939
     [Google Scholar]
  33. Grewe BF, Gründemann J, Kitch LJ, Lecoq JA, Parker JG et al. 2017. Neural ensemble dynamics underlying a long-term associative memory. Nature 543:7647670–75
     [Google Scholar]
  34. Gulli RA, Duong LR, Corrigan BW, Doucet G, Williams S et al. 2020. Context-dependent representations of objects and space in the primate hippocampus during virtual navigation. Nat. Neurosci. 23:1103–12
     [Google Scholar]
  35. Hajnal MA, Tran D, Einstein M, Martelo MV, Safaryan K et al. 2021. Continuous multiplexed population representations of task context in the mouse primary visual cortex. bioRxiv 2021.04.20.440666. https://doi.org/10.1101/2021.04.20.440666
  36. Hamm JP, Shymkiv Y, Han S, Yang W, Yuste R 2021. Cortical ensembles selective for context. PNAS 118:14e2026179118
     [Google Scholar]
  37. Haruno M, Wolpert DM, Kawato M. 2001. MOSAIC model for sensorimotor learning and control. Neural Comput. 13:102201–20
     [Google Scholar]
  38. Heald JB, Ingram JN, Flanagan JR, Wolpert DM. 2018. Multiple motor memories are learned to control different points on a tool. Nat. Hum. Behav. 2:4300–11
     [Google Scholar]
  39. Heald JB, Lengyel M, Wolpert DM. 2021. Contextual inference underlies the learning of sensorimotor repertoires. Nature 600:7889489–93
     [Google Scholar]
  40. Heald JB, Lengylel M, Wolpert DM. 2023. Contextual inference in learning and memory. Trends Cogn. Sci. 27:143–64
     [Google Scholar]
  41. Hikosaka O, Takikawa Y, Kawagoe R. 2000. Role of the basal ganglia in the control of purposive saccadic eye movements. Physiol. Rev. 80:3953–78
     [Google Scholar]
  42. Holt W, Maren S 1999. Muscimol inactivation of the dorsal hippocampus impairs contextual retrieval of fear memory. J. Neurosci. 19:209054–62
     [Google Scholar]
  43. Hopfield JJ. 1982. Neural networks and physical systems with emergent collective computational abilities. PNAS 79:82554–58
     [Google Scholar]
  44. Howard MW, Kahana MJ. 2002. A distributed representation of temporal context. J. Math. Psychol. 46:3269–99
     [Google Scholar]
  45. Jacobs R, Jordan M, Nowlan S, Hinton G. 1991. Adaptive mixtures of local experts. Neural Comput. 3:179–87
     [Google Scholar]
  46. Jerfel G, Grant E, Griffiths T, Heller KA. 2019. Reconciling meta-learning and continual learning with online mixtures of tasks. Adv. Neural Inf. Process. Syst. 32:9119–30
     [Google Scholar]
  47. Jezek K, Henriksen EJ, Treves A, Moser EI, Moser MB. 2011. Theta-paced flickering between place-cell maps in the hippocampus. Nature 478:7368246–49
     [Google Scholar]
  48. Julian JB, Doeller CF. 2021. Remapping and realignment in the human hippocampal formation predict context-dependent spatial behavior. Nat. Neurosci. 24:6863–72
     [Google Scholar]
  49. Káli S, Dayan P. 2004. Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions. Nat. Neurosci. 7:3286–94
     [Google Scholar]
  50. Kao TC, Jensen KT, Bernacchia A, Hennequin G. 2021a. Natural continual learning: success is a journey, not (just) a destination. arXiv:2106.08085 [cs.LG]
  51. Kao TC, Sadabadi MS, Hennequin G. 2021b. Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model. Neuron 109:91567–1581.e12
     [Google Scholar]
  52. Keisler A, Shadmehr R. 2010. A shared resource between declarative memory and motor memory. J. Neurosci. 30:4414817–23
     [Google Scholar]
  53. Kessler S, Parker-Holder J, Ball P, Zohren S, Roberts SJ. 2021. Same state, different task: continual reinforcement learning without interference. arXiv:2106.02940 [cs.LG]
  54. Kim JJ, Fanselow MS. 1992. Modality-specific retrograde amnesia of fear. Science 256:5057675–77
     [Google Scholar]
  55. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G et al. 2017. Overcoming catastrophic forgetting in neural networks. PNAS 114:133521–26
     [Google Scholar]
  56. Klaus A, da Silva JA, Costa RM. 2019. What, if, and when to move: basal ganglia circuits and self-paced action initiation. Annu. Rev. Neurosci. 42:459–83
     [Google Scholar]
  57. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:7553436–44
     [Google Scholar]
  58. Lengyel G. 2022. A common probabilistic framework explaining learning and generalization in perceptual and statistical learning. PhD thesis, Central European Univ. Vienna:
  59. Li Z, Hoiem D. 2017. Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40:122935–47
     [Google Scholar]
  60. Lisman JE, Grace AA. 2005. The hippocampal-VTA loop: controlling the entry of information into long-term memory. Neuron 46:5703–13
     [Google Scholar]
  61. Logiaco L, Abbott LF, Escola S. 2021. Thalamic control of cortical dynamics in a model of flexible motor sequencing. Cell Rep. 35:9109090
     [Google Scholar]
  62. Lopez-Paz D, Ranzato M. 2017. Gradient episodic memory for continual learning. Adv. Neural Inf. Process. Syst. 30:6467–76
     [Google Scholar]
  63. Mante V, Sussillo D, Shenoy KV, Newsome WT. 2013. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503:747478–84
     [Google Scholar]
  64. Markus EJ, Qin YL, Leonard B, Skaggs WE, McNaughton BL, Barnes CA. 1995. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. J. Neurosci. 15:117079–94
     [Google Scholar]
  65. Marton TF, Seifikar H, Luongo FJ, Lee AT, Sohal VS. 2018. Roles of prefrontal cortex and mediodorsal thalamus in task engagement and behavioral flexibility. J. Neurosci. 38:102569–78
     [Google Scholar]
  66. Masse NY, Grant GD, Freedman DJ. 2018. Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization. PNAS 115:44E10467–75
     [Google Scholar]
  67. McNamara CG, Tejero-Cantero Á, Trouche S, Campo-Urriza N, Dupret D. 2014. Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence. Nat. Neurosci. 17:121658–60
     [Google Scholar]
  68. Nagabandi A, Finn C, Levine S. 2018. Deep online learning via meta-learning: continual adaptation for model-based RL. arXiv:1812.07671 [cs.LG]
  69. Napier RM, Macrae M, Kehoe EJ. 1992. Rapid reacquisition in conditioning of the rabbit's nictitating membrane response. J. Exp. Psychol. Anim. Behav. Process. 18:2182–92
     [Google Scholar]
  70. Nassar MR, McGuire JT, Ritz H, Kable JW. 2019. Dissociable forms of uncertainty-driven representational change across the human brain. J. Neurosci. 39:91688–98
     [Google Scholar]
  71. Oby ER, Golub MD, Hennig JA, Degenhart AD, Tyler-Kabara EC et al. 2019. New neural activity patterns emerge with long-term learning. PNAS 116:3015210–15
     [Google Scholar]
  72. Oh Y, Schweighofer N. 2019. Minimizing precision-weighted sensory prediction errors via memory formation and switching in motor adaptation. J. Neurosci. 39:469237–50
     [Google Scholar]
  73. O'Keefe J, Nadel L. 1978. The Hippocampus as a Cognitive Map Oxford, UK: Clarendon Press
  74. Orsini CA, Yan C, Maren S 2013. Ensemble coding of context-dependent fear memory in the amygdala. Front. Behav. Neurosci. 7:199
     [Google Scholar]
  75. Palacios-Filardo J, Mellor J. 2019. Neuromodulation of hippocampal long-term synaptic plasticity. Curr. Opin. Neurobiol. 54:37–43
     [Google Scholar]
  76. Parnaudeau S, O'Neill PK, Bolkan SS, Ward RD, Abbas AI et al. 2013. Inhibition of mediodorsal thalamus disrupts thalamofrontal connectivity and cognition. Neuron 77:61151–62
     [Google Scholar]
  77. Parnaudeau S, Taylor K, Bolkan SS, Ward RD, Balsam PD, Kellendonk C. 2015. Mediodorsal thalamus hypofunction impairs flexible goal-directed behavior. Biol. Psych. 77:5445–53
     [Google Scholar]
  78. Pfeiffer BE. 2020. The content of hippocampal “replay”. Hippocampus 30:16–18
     [Google Scholar]
  79. Piray P, Daw ND. 2020. A simple model for learning in volatile environments. PLOS Comput. Biol. 16:7e1007963
     [Google Scholar]
  80. Pisupati S, Niv Y. 2022. The challenges of lifelong learning in biological and artificial systems. Trends Cogn. Sci. 26:121051–53
     [Google Scholar]
  81. Plitt MH, Giocomo LM. 2021. Experience-dependent contextual codes in the hippocampus. Nat. Neurosci. 24:5705–14
     [Google Scholar]
  82. Podlaski WF, Agnes EJ, Vogels TP 2020. Context-modular memory networks support high-capacity, flexible, and robust associative memories. bioRxiv 2020.01.08.898528. https://doi.org/10.1101/2020.01.08.898528
  83. Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH. 2017. iCaRL: incremental classifier and representation learning. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition2001–10. Piscataway, NJ: IEEE
     [Google Scholar]
  84. Redish AD, Jensen S, Johnson A, Kurth-Nelson Z. 2007. Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. Psychol. Rev. 114:3784–805
     [Google Scholar]
  85. Remington ED, Egger SW, Narain D, Wang J, Jazayeri M. 2018a. A dynamical systems perspective on flexible motor timing. Trends Cogn. Sci. 22:10938–52
     [Google Scholar]
  86. Remington ED, Narain D, Hosseini EA, Jazayeri M. 2018b. Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics. Neuron 98:51005–19.e5
     [Google Scholar]
  87. Rescorla RA. 2004. Spontaneous recovery. Learn. Mem. 11:5501–9
     [Google Scholar]
  88. Rescorla RA, Heth CD. 1975. Reinstatement of fear to an extinguished conditioned stimulus. J. Exp. Psychol. Anim. Behav. Process. 1:188–96
     [Google Scholar]
  89. Rescorla RA, Wagner AR. 1972. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. Classical Conditioning II: Current Theory and Research A Black, W Prokasy 64–99. New York: Appleton-Century-Crofts
     [Google Scholar]
  90. Rikhye RV, Wimmer RD, Halassa MM. 2018. Toward an integrative theory of thalamic function. Annu. Rev. Neurosci. 41:163–83
     [Google Scholar]
  91. Rolnick D, Ahuja A, Schwarz J, Lillicrap T, Wayne G. 2019. Experience replay for continual learning. Adv. Neural Inf. Process. Syst. 32:348–58
     [Google Scholar]
  92. Rusu AA, Rabinowitz NC, Desjardins G, Soyer H, Kirkpatrick J et al. 2016. Progressive neural networks. arXiv:1606.04671 [cs.LG]
  93. Saez A, Rigotti M, Ostojic S, Fusi S, Salzman CD. 2015. Abstract context representations in primate amygdala and prefrontal cortex. Neuron 87:4869–81
     [Google Scholar]
  94. Sanders H, Wilson MA, Gershman SJ 2020. Hippocampal remapping as hidden state inference. eLife 9:e51140
     [Google Scholar]
  95. Savin C, Dayan P, Lengyel M. 2014. Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3. PLOS Comput. Biol. 10:2e1003489
     [Google Scholar]
  96. Schuck NW, Cai MB, Wilson RC, Niv Y. 2016. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91:61402–12
     [Google Scholar]
  97. Schuck NW, Niv Y. 2019. Sequential replay of nonspatial task states in the human hippocampus. Science 364:6447eaaw5181
     [Google Scholar]
  98. Shallice T, Cipolotti L. 2018. The prefrontal cortex and neurological impairments of active thought. Annu. Rev. Psychol. 69:157–80
     [Google Scholar]
  99. Shin H, Lee JK, Kim J, Kim J 2017. Continual learning with deep generative replay. arXiv:1705.08690 [cs.AI]
  100. Singh S. 1991. The efficient learning of multiple task sequences. Adv. Neural Inf. Process. Syst. 4:251–58
     [Google Scholar]
  101. Smith MA, Ghazizadeh A, Shadmehr R. 2006. Interacting adaptive processes with different timescales underlie short-term motor learning. PLOS Biol. 4:6e179
     [Google Scholar]
  102. Sohn H, Narain D, Meirhaeghe N, Jazayeri M. 2019. Bayesian computation through cortical latent dynamics. Neuron 103:5934–47.e5
     [Google Scholar]
  103. Soltani A, Koechlin E. 2022. Computational models of adaptive behavior and prefrontal cortex. Neuropsychopharmacology 47:158–71
     [Google Scholar]
  104. Steiner KM, Gisbertz Y, Chang DI, Koch B, Uslar E et al. 2019. Extinction and renewal of conditioned eyeblink responses in focal cerebellar disease. Cerebellum 18:2166–77
     [Google Scholar]
  105. Stroud JP, Porter MA, Hennequin G, Vogels TP. 2018. Motor primitives in space and time via targeted gain modulation in cortical networks. Nat. Neurosci. 21:121774–83
     [Google Scholar]
  106. Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. 2021. Optimal information loading into working memory in prefrontal cortex. bioRxiv 2021.11.16.468360. https://doi.org/10.1101/2021.11.16.468360
  107. Sun X, O'Shea DJ, Golub MD, Trautmann EM, Vyas S et al. 2022. Cortical preparatory activity indexes learned motor memories. Nature 602:7896274–79
     [Google Scholar]
  108. Sutton RS, Barto AG. 2018. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press. , 2nd ed..
  109. Takeuchi T, Duszkiewicz AJ, Sonneborn A, Spooner PA, Yamasaki M et al. 2016. Locus coeruleus and dopaminergic consolidation of everyday memory. Nature 537:7620357–62
     [Google Scholar]
  110. Teh YW, Jordan MI, Beal MJ, Blei DM. 2006. Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101:4761566–81
     [Google Scholar]
  111. Teyler TJ, Rudy JW. 2007. The hippocampal indexing theory and episodic memory: updating the index. Hippocampus 17:121158–69
     [Google Scholar]
  112. Tsuda B, Tye KM, Siegelmann HT, Sejnowski TJ. 2020. A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex. PNAS 117:4729872–82
     [Google Scholar]
  113. van de Ven GM, Tuytelaars T, Tolias AS. 2022. Three types of incremental learning. Nat. Mach. Intell. 4:1185–97
     [Google Scholar]
  114. Vyas S, Golub MD, Sussillo D, Shenoy KV. 2020. Computation through neural population dynamics. Annu. Rev. Neurosci. 43:249–75
     [Google Scholar]
  115. Wallis JD, Anderson KC, Miller EK. 2001. Single neurons in prefrontal cortex encode abstract rules. Nature 411:6840953–56
     [Google Scholar]
  116. Wang J, Narain D, Hosseini EA, Jazayeri M. 2018. Flexible timing by temporal scaling of cortical responses. Nat. Neurosci. 21:1102–10
     [Google Scholar]
  117. Wikenheiser AM, Schoenbaum G. 2016. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat. Rev. Neurosci. 17:8513–23
     [Google Scholar]
  118. Wilson RC, Takahashi YK, Schoenbaum G, Niv Y. 2014. Orbitofrontal cortex as a cognitive map of task space. Neuron 81:2267–79
     [Google Scholar]
  119. Wolpert DM, Kawato M. 1998. Multiple paired forward and inverse models for motor control. Neural Netw. 11:7–81317–29
     [Google Scholar]
  120. Wolpert DM, Miall RC, Kawato M. 1998. Internal models in the cerebellum. Trends Cogn. Sci. 2:9338–47
     [Google Scholar]
  121. Wu Z, Litwin-Kumar A, Shamash P, Taylor A, Axel R, Shadlen MN 2020. Context-dependent decision making in a premotor circuit. Neuron 106:2316–28.e6
     [Google Scholar]
  122. Xie A, Harrison J, Finn C. 2021. Deep reinforcement learning amidst continual structured non-stationarity. PMLR 139:11393–403
     [Google Scholar]
  123. Xu M, Ding W, Zhu J, Liu Z, Chen B, Zhao D 2020. Task-agnostic online reinforcement learning with an infinite mixture of Gaussian processes. Adv. Neural Inf. Process. Syst. 33:6429–40
     [Google Scholar]
  124. Yamins DL, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ. 2014. Performance-optimized hierarchical models predict neural responses in higher visual cortex. PNAS 111:238619–24
     [Google Scholar]
  125. Yu AJ, Dayan P. 2005. Uncertainty, neuromodulation, and attention. Neuron 46:4681–92
     [Google Scholar]
  126. Zacks JM, Speer NK, Swallow KM, Braver TS, Reynolds JR. 2007. Event perception: a mind-brain perspective. Psychol. Bull. 133:2273–93
     [Google Scholar]
  127. Zeng G, Chen Y, Cui B, Yu S 2019. Continual learning of context-dependent processing in neural networks. Nat. Mach. Intell. 1:8364–72
     [Google Scholar]
  128. Zenke F, Poole B, Ganguli S. 2017. Continual learning through synaptic intelligence. PMLR 70:3987–95
     [Google Scholar]
  129. Zhang JY, Kuai SG, Xiao LQ, Klein SA, Levi DM, Yu C. 2008. Stimulus coding rules for perceptual learning. PLOS Biol. 6:8e197
     [Google Scholar]
/content/journals/10.1146/annurev-neuro-092322-100402
Loading
The Computational and Neural Bases of Context-Dependent Learning
Annual Review of Neuroscience 46, 233 (2023); https://doi.org/10.1146/annurev-neuro-092322-100402
/content/journals/10.1146/annurev-neuro-092322-100402
/content/journals/10.1146/annurev-neuro-092322-100402
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error