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Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
Google Scholar
Gershman, S. J. & Uchida, N. Believing in dopamine. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-019-0220-7 (2019).
Wood, A. N. New roles for dopamine in motor skill acquisition: lessons from primates, rodents, and songbirds. J. Neurophysiol. 125, 2361–2374 (2021).
Google Scholar
Blain, B. & Sharot, T. Intrinsic reward: potential cognitive and neural mechanisms. Curr. Opin. Behav. Sci. 39, 113–118 (2021).
Google Scholar
Hisey, E., Kearney, M. G. & Mooney, R. A common neural circuit mechanism for internally guided and externally reinforced forms of motor learning. Nat. Neurosci. 21, 589–597 (2018).
Google Scholar
Thorndike, E. L. The Elements of Psychology (Seiler, 1905).
Markowitz, J. E. et al. Spontaneous behaviour is structured by reinforcement without explicit reward. Nature 614, 108–117 (2023).
Google Scholar
Doupe, A. J. & Kuhl, P. K. Birdsong and human speech: common themes and mechanisms. Annu. Rev. Neurosci. 22, 567–631 (1999).
Google Scholar
Sakata, J. T., Woolley, S. C., Fay, R. R. & Popper, A. N. The Neuroethology of Birdsong (Springer Nature, 2020).
Mooney, R. Birdsong. Curr. Biol. 32, R1090–R1094 (2022).
Google Scholar
Derégnaucourt, S., Mitra, P. P., Fehér, O., Pytte, C. & Tchernichovski, O. How sleep affects the developmental learning of bird song. Nature 433, 710–716 (2005).
Google Scholar
Goffinet, J., Brudner, S., Mooney, R. & Pearson, J. Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires. eLife 10, e67855 (2021).
Google Scholar
Brudner, S., Pearson, J. & Mooney, R. Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance. PLoS Comput. Biol. 19, e1011051 (2023).
Google Scholar
Eales, L. A. Song learning in zebra finches: some effects of song model availability on what is learnt and when. Anim. Behav. 33, 1293–1300 (1985).
Google Scholar
Price, P. H. Developmental determinants of structure in zebra finch song. J. Comp. Physiol. Psychol. 93, 260–277 (1979).
Google Scholar
Singh Alvarado, J. et al. Neural dynamics underlying birdsong practice and performance. Nature 599, 635–639 (2021).
Google Scholar
Person, A. L., Gale, S. D., Farries, M. A. & Perkel, D. J. Organization of the songbird basal ganglia, including area X. J. Comp. Neurol. 508, 840–866 (2008).
Google Scholar
Feenders, G. et al. Molecular mapping of movement-associated areas in the avian brain: a motor theory for vocal learning origin. PLoS ONE 3, e1768 (2008).
Google Scholar
Doya, K. & Sejnowski, T. A novel reinforcement model of birdsong vocalization learning. In Proc. Advances in Neural Information Processing Systems Vol. 7 (eds Tesauro, G. et al.) 101–108 (MIT, 1994).
Fee, M. S. & Goldberg, J. H. A hypothesis for basal ganglia-dependent reinforcement learning in the songbird. Neuroscience 198, 152–170 (2011).
Google Scholar
Duffy, A., Latimer, K. W., Goldberg, J. H., Fairhall, A. L. & Gadagkar, V. Dopamine neurons evaluate natural fluctuations in performance quality. Cell Rep 38, 110574 (2022).
Google Scholar
Gadagkar, V. et al. Dopamine neurons encode performance error in singing birds. Science 354, 1278–1282 (2016).
Google Scholar
Xiao, L. et al. A basal ganglia circuit sufficient to guide birdsong learning. Neuron 98, 208–221.e5 (2018).
Google Scholar
Mohebi, A., Collins, V. L. & Berke, J. D. Accumbens cholinergic interneurons dynamically promote dopamine release and enable motivation. eLife 12, e85011 (2023).
Google Scholar
Liu, C. et al. An action potential initiation mechanism in distal axons for the control of dopamine release. Science https://doi.org/10.1126/science.abn0532 (2022).
Kramer, P. F. et al. Synaptic-like axo-axonal transmission from striatal cholinergic interneurons onto dopaminergic fibers. Neuron 110, 2949–2960.e4 (2022).
Google Scholar
Sun, F. et al. Next-generation GRAB sensors for monitoring dopaminergic activity in vivo. Nat. Methods 17, 1156–1166 (2020).
Google Scholar
Ko, D. & Wanat, M. J. Phasic dopamine transmission reflects initiation vigor and exerted effort in an action- and region-specific manner. J. Neurosci. 36, 2202–2211 (2016).
Google Scholar
Panigrahi, B. et al. Dopamine is required for the neural representation and control of movement vigor. Cell 162, 1418–1430 (2015).
Google Scholar
Roeser, A. et al. Dopaminergic error signals retune to social feedback during courtship. Nature 623, 375–380 (2023).
Google Scholar
Bottjer, S. W., Halsema, K. A., Brown, S. A. & Miesner, E. A. Axonal connections of a forebrain nucleus involved with vocal learning in zebra finches. J. Comp. Neurol. 279, 312–326 (1989).
Google Scholar
Chantranupong, L. et al. Dopamine and glutamate regulate striatal acetylcholine in decision-making. Nature 621, 577–585 (2023).
Google Scholar
Krok, A. C. et al. Intrinsic dopamine and acetylcholine dynamics in the striatum of mice. Nature 621, 543–549 (2023).
Google Scholar
Jing, M. et al. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods 17, 1139–1146 (2020).
Google Scholar
Zingg, B. et al. AAV-mediated anterograde transsynaptic tagging: mapping corticocollicular input-defined neural pathways for defense behaviors. Neuron 93, 33–47 (2017).
Google Scholar
Kozhevnikov, A. A. & Fee, M. S. Singing-related activity of identified HVC neurons in the zebra finch. J. Neurophysiol. 97, 4271–4283 (2007).
Google Scholar
Goldberg, J. H. & Fee, M. S. Singing-related neural activity distinguishes four classes of putative striatal neurons in the songbird basal ganglia. J. Neurophysiol. 103, 2002–2014 (2010).
Google Scholar
Tumer, E. C. & Brainard, M. S. Performance variability enables adaptive plasticity of ‘crystallized’ adult birdsong. Nature 450, 1240–1244 (2007).
Google Scholar
Fiete, I. R., Fee, M. S. & Seung, H. S. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J. Neurophysiol. 98, 2038–2057 (2007).
Google Scholar
Farries, M. A. & Fairhall, A. L. Reinforcement learning with modulated spike timing dependent synaptic plasticity. J. Neurophysiol. 98, 3648–3665 (2007).
Google Scholar
Long, M. A. & Fee, M. S. Using temperature to analyse temporal dynamics in the songbird motor pathway. Nature 456, 189–194 (2008).
Google Scholar
Ding, L. & Perkel, D. J. Dopamine modulates excitability of spiny neurons in the avian basal ganglia. J. Neurosci. 22, 5210–5218 (2002).
Google Scholar
Kubikova, L., Wada, K. & Jarvis, E. D. Dopamine receptors in a songbird brain. J. Comp. Neurol. 518, 741–769 (2010).
Google Scholar
Richfield, E. K., Penney, J. B. & Young, A. B. Anatomical and affinity state comparisons between dopamine D1 and D2 receptors in the rat central nervous system. Neuroscience 30, 767–777 (1989).
Google Scholar
Kearney, M. G., Warren, T. L., Hisey, E., Qi, J. & Mooney, R. Discrete evaluative and premotor circuits enable vocal learning in songbirds. Neuron 104, 559–575.e6 (2019).
Google Scholar
Hamaguchi, K., Tschida, K. A., Yoon, I., Donald, B. R. & Mooney, R. Auditory synapses to song premotor neurons are gated off during vocalization in zebra finches. eLife 3, e01833 (2014).
Google Scholar
Hamaguchi, K. & Mooney, R. Recurrent interactions between the input and output of a songbird cortico-basal ganglia pathway are implicated in vocal sequence variability. J. Neurosci. 32, 11671–11687 (2012).
Google Scholar
Dan Foresee, F. & Hagan, M. T. Gauss-Newton approximation to Bayesian learning. In Proc. International Conference on Neural Networks 1930–1935 (IEEE, 1997).
Bates, D., Machler, M., Bolker, B. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 58, 267–288 (1996).
Google Scholar
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar
Lopes, G. et al. Bonsai: an event-based framework for processing and controlling data streams. Front. Neuroinform. 9, 7 (2015).
Google Scholar
Pennington, Z. T. et al. ezTrack: an open-source video analysis pipeline for the investigation of animal behavior. Sci. Rep. 9, 19979 (2019).
Google Scholar