Sacramento, J., Bengio, Y., Costa, R. P. & Senn, W. Dendritic cortical microcircuits approximate the backpropagation algorithm. In Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio, S. & Wallach, H. M.) 8735–8746 (ACM, 2018).
Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A. & Naud, R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nat. Neurosci. 24, 1010–1019 (2021).
Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, e22901 (2017).
Greedy, W., Zhu, H. W., Pemberton, J., Mellor, J. & Costa, R. P. Single-phase deep learning in cortico-cortical networks. In Proc. 36th International Conference on Neural Information Processing Systems (eds Koyejo, S. et al.) 24213–24225 (ACM, 2022).
Körding, K. P. & König, P. Supervised and unsupervised learning with two sites of synaptic integration. J. Comput. Neurosci. 11, 207–215 (2001).
Magee, J. C. & Grienberger, C. Synaptic plasticity forms and functions. Annu. Rev. Neurosci. 43, 95–117 (2020).
Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).
Caporale, N. & Dan, Y. Spike timing–dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).
Artola, A., Bröcher, S. & Singer, W. Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex. Nature 347, 69–72 (1990).
Kirkwood, A., Rioult, M. G. & Bear, M. F. Experience-dependent modification of synaptic plasticity in visual cortex. Nature 381, 526–528 (1996).
Bliss, T. V. P. & Lømo, T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356 (1973).
Bienenstock, E. L., Cooper, L. N. & Munro, P. W. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982).
Abbott, L. F. & Blum, K. I. Functional significance of long-term potentiation for sequence learning and prediction. Cereb. Cortex 6, 406–416 (1996).
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G. Backpropagation and the brain. Nat. Rev. Neurosci. 21, 335–346 (2020).
Richards, B. A. & Lillicrap, T. P. Dendritic solutions to the credit assignment problem. Curr. Opin. Neurobiol. 54, 28–36 (2019).
Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).
Minsky, M. Steps toward artificial intelligence. Proc. IRE 49, 8–30 (1961).
Lansdell, B. J., Prakash, P. R. & Kording, K. P. Learning to solve the credit assignment problem. In Proceedings of the International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=ByeUBANtvB (2020).
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Meulemans, A., Carzaniga, F., Suykens, J., Sacramento, J. & Grewe, B. F. A theoretical framework for target propagation. in Proc. 34th International Conference on Neural Information Processing Systems vol. 33 (eds Larochelle, H. et al.) 20024–20036 (ACM, 2020).
Lee, D.-H., Zhang, S., Fischer, A. & Bengio, Y. Difference target propagation. In Machine Learning and Knowledge Discovery in Databases 9284 (eds Appice, A. et al.) 498–515 (Springer, 2015).
Makino, H. & Komiyama, T. Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18, 1116–1122 (2015).
Larkum, M. E., Zhu, J. J. & Sakmann, B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338–341 (1999).
Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends Neurosci. 36, 141–151 (2013).
Lafourcade, M. et al. Differential dendritic integration of long-range inputs in association cortex via subcellular changes in synaptic AMPA-to-NMDA receptor ratio. Neuron 110, 1532–1546 (2022).
Marques, T., Nguyen, J., Fioreze, G. & Petreanu, L. The functional organization of cortical feedback inputs to primary visual cortex. Nat. Neurosci. 21, 757–764 (2018).
Manita, S. et al. A top-down cortical circuit for accurate sensory perception. Neuron 86, 1304–1316 (2015).
Rockland, K. S. & Pandya, D. N. Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Res. 179, 3–20 (1979).
Coogan, T. A. & Burkhalter, A. Conserved patterns of cortico-cortical connections define areal hierarchy in rat visual cortex. Exp. Brain Res. 80, 49–53 (1990).
Cauller, L. J., Clancy, B. & Connors, B. W. Backward cortical projections to primary somatosensory cortex in rats extend long horizontal axons in layer I. J. Comp. Neurol. 390, 297–310 (1998).
Fişek, M. et al. Cortico-cortical feedback engages active dendrites in visual cortex. Nature 617, 769–776 (2023).
Naud, R. & Sprekeler, H. Sparse bursts optimize information transmission in a multiplexed neural code. Proc. Natl Acad. Sci. USA 115, E6329–E6338 (2018).
Neely, R. M., Koralek, A. C., Athalye, V. R., Costa, R. M. & Carmena, J. M. Volitional modulation of primary visual cortex activity requires the basal ganglia. Neuron 97, 1356–1368.e4 (2018).
Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E. & Carmena, J. M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17, 807–809 (2014).
Clancy, K. B. & Mrsic-Flogel, T. D. The sensory representation of causally controlled objects. Neuron 109, 677–689.e4 (2021).
Jeon, B. B., Fuchs, T., Chase, S. M. & Kuhlman, S. J. Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task. Nat. Commun. 13, 3638 (2022).
Lai, C., Tanaka, S., Harris, T. D. & Lee, A. K. Volitional activation of remote place representations with a hippocampal brain–machine interface. Science 382, 566–573 (2023).
Mitani, A., Dong, M. & Komiyama, T. Brain–computer interface with inhibitory neurons reveals subtype-specific strategies. Curr. Biol. 28, 77–83.e4 (2018).
Koralek, A. C., Jin, X., Long II, J. D., Costa, R. M. & Carmena, J. M. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331–335 (2012).
Voigts, J. & Harnett, M. T. Somatic and dendritic encoding of spatial variables in retrosplenial cortex differs during 2D navigation. Neuron 105, 237–245.e4 (2019).
Beaulieu-Laroche, L., Toloza, E. H. S., Brown, N. J. & Harnett, M. T. Widespread and highly correlated somato-dendritic activity in cortical layer 5 neurons. Neuron 103, 235–241.e4 (2019).
Peters, A. J., Lee, J., Hedrick, N. G., O’Neil, K. & Komiyama, T. Reorganization of corticospinal output during motor learning. Nat. Neurosci. 20, 1133–1141 (2017).
Francioni, V., Padamsey, Z. & Rochefort, N. L. High and asymmetric somato-dendritic coupling of V1 layer 5 neurons independent of visual stimulation and locomotion. eLife 8, e49145 (2019).
Padamsey, Z., Katsanevaki, D., Dupuy, N. & Rochefort, N. L. Neocortex saves energy by reducing coding precision during food scarcity. Neuron 110, 280–296.e10 (2022).
Rupprecht, P. et al. A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging. Nat. Neurosci. 24, 1324–1337 (2021).
Kerlin, A. et al. Functional clustering of dendritic activity during decision-making. eLife 8, e46966 (2019).
Hill, D. N., Varga, Z., Jia, H., Sakmann, B. & Konnerth, A. Multibranch activity in basal and tuft dendrites during firing of layer 5 cortical neurons in vivo. Proc. Natl Acad. Sci. USA 110, 13618–13623 (2013).
Grienberger, C., Chen, X. & Konnerth, A. NMDA receptor-dependent multidendrite Ca2+ spikes required for hippocampal burst firing in vivo. Neuron 81, 1274–1281 (2014).
Otor, Y. et al. Dynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior. Science 376, 267–275 (2022).
Helmchen, F., Svoboda, K., Denk, W. & Tank, D. W. In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nat. Neurosci. 2, 989–996 (1999).
Francioni, V. & Harnett, M. T. Rethinking single neuron electrical compartmentalization: dendritic contributions to network computation in vivo. Neuroscience 489, 185–199 (2022).
Xu, N. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).
Abs, E. et al. Learning-related plasticity in dendrite-targeting layer 1 interneurons. Neuron 100, 684–699.e6 (2018).
Palmer, L. M. et al. The cellular basis of GABAB-mediated interhemispheric inhibition. Science 335, 989–993 (2012).
Keller, A. J., Roth, M. M. & Scanziani, M. Feedback generates a second receptive field in neurons of the visual cortex. Nature 582, 545–549 (2020).
Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L. & Zhu, J. J. The organization of two new cortical interneuronal circuits. Nat. Neurosci. 16, 210–8 (2013).
Reinhold, K. et al. Striatum supports fast learning but not memory recall. Nature 643, 458–467 (2025).
Meulemans, A. et al. Credit assignment in neural networks through deep feedback control. In Advances in Neural Information Processing Systems 34 (eds Ranzato, M. et al.) 4674–4687 (Curran Associates, 2021).
Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).
Athalye, V. R., Carmena, J. M. & Costa, R. M. Neural reinforcement: re-entering and refining neural dynamics leading to desirable outcomes. Curr. Opin. Neurobiol. 60, 145–154 (2020).
Gershman, S. J. et al. Explaining dopamine through prediction errors and beyond. Nat. Neurosci. 27, 1645–1655 (2024).
Kasahara, K., DaSalla, C. S., Honda, M. & Hanakawa, T. Basal ganglia–cortical connectivity underlies self-regulation of brain oscillations in humans. Commun. Biol. 5, 712 (2022).
Mikulasch, F. A., Rudelt, L., Wibral, M. & Priesemann, V. Where is the error? Hierarchical predictive coding through dendritic error computation. Trends Neurosci. 46, 45–59 (2023).
Branco, T. & Häusser, M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr. Opin. Neurobiol. 20, 494–502 (2010).
Marques, T. et al. A role for mouse primary visual cortex in motion perception. Curr. Biol. 28, 1703–1713.e6 (2018).
You 游文愷, W.-K. & Mysore, S. P. Dynamics of visual perceptual decision-making in freely behaving mice. eNeuro 9, 0161-21.2022 (2022).
Guizar-Sicairos, M., Thurman, S. T. & Fienup, J. R. Efficient subpixel image registration algorithms. Opt. Lett. 33, 156–158 (2008).
Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).
Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv https://doi.org/10.1101/061507 (2017).
Keemink, S. W. FISSA: a neuropil decontamination toolbox for calcium imaging signals. Sci. Rep. 8, 3493 (2018).
Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S. & Sundararajan, S. A dual coordinate descent method for large-scale linear SVM. in Proc. 25th International Conference on Machine Learning 408–415 (ACM, 2008).
