Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An internal model for sensorimotor integration. Science 269, 1880–1882 (1995).
Google Scholar
Scott, S. H. Optimal feedback control and the neural basis of volitional motor control. Nat. Rev. Neurosci. 5, 532–546 (2004).
Google Scholar
Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).
Google Scholar
Friston, K. J. et al. World model learning and inference. Neural Netw. 144, 573–590 (2021).
Google Scholar
Rao, R. P. N. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87 (1999).
Google Scholar
Rajesh, P. N. R. A sensory-motor theory of the neocortex. Nat. Neurosci. 27, 1221–1235 (2024).
Google Scholar
Mathis, M. W., Mathis, A. & Uchida, N. Somatosensory cortex plays an essential role in forelimb motor adaptation in mice. Neuron 93, 1493–1503 (2017).
Google Scholar
Takei, T., Lomber, S. G., Cook, D. J. & Scott, S. H. Transient deactivation of dorsal premotor cortex or parietal area 5 impairs feedback control of the limb in macaques. Curr. Biol. 31, 1476–1487 (2021).
Google Scholar
Sternberg, R. J. A theory of adaptive intelligence and its relation to general intelligence. J. Intell. 7, 23 (2019).
Google Scholar
Grossberg, S. A path toward explainable AI and autonomous adaptive intelligence: deep learning, adaptive resonance, and models of perception, emotion, and action. Front. Neurorobot. 14, 36 (2020).
Google Scholar
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).
Google Scholar
Mathis, M. W. The neocortical column as a universal template for perception and world-model learning. Nat. Rev. Neurosci. 24, 3 (2022).
Google Scholar
Manley, J. et al. Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number. Neuron 112, 1694–1709 (2024).
Google Scholar
Siegle, J. H. et al. A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas. Nature 592, 86–92 (2021).
Google Scholar
Stevenson, I. H. & Kording, K. P. How advances in neural recording affect data analysis. Nat. Neurosci. 14, 139–142 (2011).
Google Scholar
Mathis, M. W., Rotondo, A. P., Tolias, A., Change, E. & Mathis, A. Decoding the brain: from neural representations to mechanistic models. Cell 87, 5814–5832 (2024).
Google Scholar
Lecoq, J. A., Orlova, N. & Grewe, B. F. Wide. Fast. Deep: recent advances in multiphoton microscopy of in vivo neuronal activity. J. Neurosci. 39, 9042–9052 (2019).
Google Scholar
Urai, A. E., Doiron, B., Leifer, A. M. & Churchland, A. K. Large-scale neural recordings call for new insights to link brain and behavior. Nat. Neurosci. 25, 11–19 (2022).
Google Scholar
Chen, H. & Fang, Y. Recent developments in implantable neural probe technologies. MRS Bull. 48, 484–494 (2023).
Google Scholar
Mathis, M. W. & Mathis, A. Deep learning tools for the measurement of animal behavior in neuroscience. Curr. Opin. Neurobiol. 60, 1–11 (2020).
Google Scholar
Tsutsui-Kimura, I. et al. Dopamine in the tail of the striatum facilitates avoidance in threat–reward conflicts. Nat. Neurosci. 28, 795–810 (2025).
Google Scholar
Lopes, G. et al. Creating and controlling visual environments using bonvision. eLife 10, e65541 (2021).
Google Scholar
Rosenberg, M., Zhang, T., Perona, P. & Meister, M. Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration. eLife 10, e66175 (2021).
Google Scholar
Shemesh, Y., Benjamin, A., Shoshani-Haye, K., Yizhar, O. & Chen, A. Studying dominance and aggression requires ethologically relevant paradigms. Curr. Opin. Neurobiol. 86, 102879 (2024).
Google Scholar
Hao, Y., Thomas, A. M. & Nuo, L. Fully autonomous mouse behavioral and optogenetic experiments in home-cage. eLife 10, e66112 (2020).
Google Scholar
Skyberg, R. J. & Niell, C. M. Natural visual behavior and active sensing in the mouse. Curr. Opin. Neurobiol. 86, 102882 (2024).
Google Scholar
Palatucci, M., Pomerleau, D. A., Hinton, G. E. & Mitchell, T. M. Zero-shot learning with semantic output codes. Proceedings of the Advances in Neural Information Processing Systems Vol. 22 (2009).
El-Gaby, M. et al. A cellular basis for mapping behavioural structure. Nature 636, 671–680 (2024).
Google Scholar
Thorndike, E.L. Animal Intelligence: An Experimental Study of the Associative Processes in Animals (Columbia University Press, 1898).
Hunt, G. R. Manufacture and use of hook-tools by New Caledonian crows. Nature 379, 249–251 (1996).
Google Scholar
Rutz, C. & St. Clair, J. J. The evolutionary origins and ecological context of tool use in New Caledonian crows. Behav. Processes 89, 153–165 (2012).
Google Scholar
Dyer, F. C. & Seeley, T. D. Dance dialects and foraging range in three Asian honey bee species. Behav. Ecol. Sociobiol. 28, 227–233 (1991).
Google Scholar
Alem, S. et al. Associative mechanisms allow for social learning and cultural transmission of string pulling in an insect. PLoS Biol. 14, e1002564 (2016).
Google Scholar
Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428–1432 (2018).
Google Scholar
Kaminski, J., Call, J. & Fischer, J. Word learning in a domestic dog: evidence for ‘fast mapping’. Science 304, 1682–1683 (2004).
Google Scholar
Beyret, B. et al. The animal-AI environment: training and testing animal-like artificial cognition. Preprint at arXiv https://doi.org/10.48550/arXiv.1909.07483 (2019).
Williams, A. H. et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98, 1099–1115 (2017).
Google Scholar
Sorscher, B., Ganguli, S. & Sompolinsky, H. Neural representational geometry underlies few-shot concept learning. Proc. Natl Acad. Sci. USA 119, e2200800119 (2022).
Google Scholar
Schneider, S., Lee, J. H. & Mathis, M. W. Learnable latent embeddings for joint behavioural and neural analysis. Nature 617, 360–368 (2023).
Google Scholar
McDougle, S. D., Bond, K. M. & Taylor, J. A. Explicit and implicit processes constitute the fast and slow processes of sensori-motor learning. J. Neurosci. 35, 9568–9579 (2015).
Google Scholar
Krakauer, J. W. & Mazzoni, P. Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21, 636–644 (2011).
Google Scholar
Izawa, J. & Shadmehr, R. Learning from sensory and reward prediction errors during motor adaptation. PLoS Comput. Biol. 7, e1002012 (2011).
Google Scholar
Stavisky, S. D., Kao, J. C., Ryu, S. I. & Shenoy, K. V. Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model. J. Neurosci. 37, 1721–1732 (2017).
Google Scholar
Shadmehr, R. & Mussa-Ivaldi, F. A. Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994).
Google Scholar
Bizzi, D. E., Accornero, N., Chapple, W. & Hogan, N. Arm trajectory formation in monkeys. Exp. Brain Res. 46, 139–143 (2013).
Google Scholar
DeWolf, T., Schneider, S., Soubiran, P., Roggenbach, A. & Mathis, M. W. Neuro-musculoskeletal modeling reveals muscle-level neural dynamics of adaptive learning in sensorimotor cortex. Preprint at bioRxiv https://doi.org/10.1101/2024.09.11.612513 (2024)
Ray Li, C.-S., Padoa-Schioppa, C. & Bizzi, E. Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron 30, 593–607 (2001).
Google Scholar
Sun, X. et al. Cortical preparatory activity indexes learned motor memories. Nature 602, 274–279 (2022).
Google Scholar
Meyer, T. & Rust, N. C. Single-exposure visual memory judgments are reflected in inferotemporal cortex. eLife 7, e32259 (2018).
Google Scholar
Meirhaeghe, N., Sohn, H. & Jazayeri, M. A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex. Neuron 109, 2995–3011 (2021).
Google Scholar
Keller, G. B., Bonhoeffer, T. & Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74, 809–815 (2012).
Google Scholar
Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014).
Google Scholar
Vendrell-Llopis, N., Fang, C., Qü, A. J., Costa, R. M. & Carmena, J. M. Diverse operant control of different motor cortex populations during learning. Curr. Biol. 32, 1616–1622 (2021).
Google Scholar
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017).
Google Scholar
Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C. & Dosovitskiy, A. Do vision transformers see like convolutional neural networks? Preprint at arXiv https://doi.org/10.48550/arXiv.2108.08810 (2021).
Mountcastle, V. B. The Mindful Brain: Cortical Organization and the Group-selective Theory of Higher Brain Function (MIT Press, 1978).
Green, J. et al. A cell-type-specific error-correction signal in the posterior parietal cortex. Nature 620, 366–373 (2023).
Google Scholar
Wilmes, K. A., Petrovici, M. A., Sachidhanandam, S. & Senn, W. Uncertainty-modulated prediction errors in cortical microcircuits. eLife 13, RP95127 (2024).
Google Scholar
Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A. & Keller, G. B. A sensorimotor circuit in mouse cortex for visual flow predictions. Neuron 95, 1420–1432 (2017).
Google Scholar
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
Google Scholar
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B. & Uchida, N. Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature 482, 85–88 (2012).
Google Scholar
Gershman, S. J. et al. Explaining dopamine through prediction errors and beyond. Nat. Neurosci. 27, 1645–1655 (2024).
Google Scholar
Eshel, N. et al. Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525, 243–246 (2015).
Google Scholar
Tsai, M. C. et al. Hierarchy of prediction errors shapes the learning of context-dependent sensory representations. Preprint at bioRxiv https://doi.org/10.1101/2024.09.30.615819 (2024).
Palidis, D. J., McGregor, H. R., Vo, A., MacDonald, P. A. & Gribble, P. L. Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference. J. Neurophysiol. 126, 47–67 (2021).
Google Scholar
Gallego, J. A., Perich, M. G., Miller, L. E. & Solla, S. A. Neural manifolds for the control of movement. Neuron 94, 978–984 (2017).
Google Scholar
Perich, M. G., Gallego, J. A. & Miller, L. E. A neural population mechanism for rapid learning. Neuron 100, 964–976 (2017).
Google Scholar
Pandarinath, C. et al. Inferring single-trial neural population dynamics using sequential auto-encoders. Nat. Methods 15, 805–815 (2017).
Google Scholar
Hurwitz, C. L., Kudryashova, N. N., Onken, A. & Hennig, M. H. Building population models for large-scale neural recordings: Opportunities and pitfalls. Curr. Opin. Neurobiol. 70, 64–73 (2021).
Google Scholar
Paninski, L. Maximum likelihood estimation of cascade point-process neural encoding models. Network 15, 243–262 (2004).
Google Scholar
Jonathan, W. et al. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999 (2008).
Google Scholar
Balzani, E., Lakshminarasimhan, K. J., Angelaki, D. E. & Savin, C. Efficient estimation of neural tuning during naturalistic behavior. Proceedings of the Advances in Neural Information Processing Systems Vol. 33, 12604–12614 (2020).
Jazayeri, M. & Afraz, A. Navigating the neural space in search of the neural code. Neuron 93, 1003–1014 (2017).
Google Scholar
Churchland, M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
Google Scholar
Sani, O. G., Pesaran, B. & Shanechi, M. Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks. Nat. Neurosci. 27, 2033–2045 (2024).
Google Scholar
Sani, O. G., Abbaspourazad, H., Wong, Y. T., Pesaran, B. & Shanechi, M. M. Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification. Nat. Neurosci. 24, 140–149 (2020).
Google Scholar
Mathis, M. W. & Mathis, A. Joint modelling of brain and behaviour dynamics with artificial intelligence. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-025-00996-1 (2025).
Keshtkaran, M. R. et al. A large-scale neural network training framework for generalized estimation of single-trial population dynamics. Nat. Methods 19, 1572–1577 (2022).
Google Scholar
Azabou, M. et al. A unified, scalable framework for neural population decoding. Proceedings of the 37th Conference on Neural Information Processing Systems (2023).
Ye, S., Lauer, J., Zhou, M., Mathis, A. & Mathis, M. W. Amadeusgpt: a natural language interface for interactive animal behavioral analysis. Proceedings of the 37th International Conference on Neural Information Processing Systems (2023).
Castro, P. S. et al. Discovering symbolic cognitive models from human and animal behavior. Preprint at bioRxiv https://doi.org/10.1101/2025.02.05.636732 (2025).
Zhang, Y. et al. Towards a ‘universal translator’ for neural dynamics at single-cell, single-spike resolution. Preprint at arXiv https://doi.org/10.48550/arXiv.2407.14668 (2024).
Benchetrit, Y., Banville, H. J. & King, J.-R. Brain decoding: toward real-time reconstruction of visual perception. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.19812 (2023).
Wang, E. Y. et al. Foundation model of neural activity predicts response to new stimulus types and anatomy. Preprint at bioRxiv https://doi.org/10.1101/2023.03.21.533548 (2024).
McCloskey, M. & Cohen, N. J. Catastrophic interference in connectionist networks: the sequential learning problem. Psychol. Learn. Motiv. 24, 109–165 (1989).
Google Scholar
Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan J. (eds). Continual learning via local module composition. In Proceedings of the Advances in Neural Information Processing Systems Vol. 34, 30298–30312 (Curran Associates, 2021).
Wallach, H. et al. (eds). Random path selection for continual learning. Proceedings of the Advances in Neural Information Processing Systems Vol. 32 (Curran Associates, 2019).
Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521–3526 (2016).
Google Scholar
Ovsianas, A., Ramapuram, J., Busbridge, D., Dhekane, E. G. & Webb, R. Elastic weight consolidation improves the robustness of self-supervised learning methods under transfer. Preprint at arXiv https://doi.org/10.48550/arXiv.2210.16365 (2022).
Wang, L. et al. Memory replay with data compression for continual learning. Preprint at arXiv https://doi.org/10.48550/arXiv.2202.06592 (2022).
Ye, S. et al. SuperAnimal pretrained pose estimation models for behavioral analysis. Nat. Commun. 15, 5165 (2024).
Google Scholar
Wang, L. et al. Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Preprint at arXiv https://doi.org/10.48550/arXiv.2308.14991 (2023).
Wang, L., Zhang, X., Su, H. & Zhu, J. A comprehensive survey of continual learning: theory, method and application. IEEE Trans. Pattern Anal. Mach. Intell. 46, 5362–5383 (2024).
Google Scholar
Nguyen, C. V., Li, Y., Bui, T. D. & Turner, R. E. Variational continual learning. Proceedings of the International Conference on Learning Representations (2018).
Zenke, F., Poole, B. & Ganguli, S. Continual learning through synaptic intelligence. Proc. Mach. Learn. Res. 70, 3987–3995 (2017).
Google Scholar
Roscow, E., Chua, R., Costa, R. P., Jones, M. W. & Lepora, N. F. Learning offline: memory replay in biological and artificial reinforcement learning. Trends Neurosci. 44, 808–821 (2021).
Google Scholar
Lin, L. Self-improving reactive agents based on reinforcement learning, planning and teaching. Mach. Learn. 8, 293–321 (1992).
Google Scholar
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Google Scholar
Packer, C. et al. MemGPT: towards LLMs as operating systems. Preprint at arXiv https://doi.org/10.48550/arXiv.2310.08560 (2023).
Wang, G. et al. Voyager: an open-ended embodied agent with large language models. Preprint at arXiv https://doi.org/10.48550/arXiv.2305.16291 (2023).
Keller, G. B. & Sterzer, P. Predictive processing: a circuit approach to psychosis. Ann. Rev. Neurosci. 47, 85–101 (2024).
Google Scholar
Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at arXiv https://doi.org/10.48550/arXiv.2108.07258 (2021).
Gemini Team Google et al. Gemini: a family of highly capable multimodal models. Preprint at arXiv https://doi.org/10.48550/arXiv.2312.11805 (2024).
DeepSeek-AI et al. DeepSeek-v3 technical report. Preprint at arXiv https://doi.org/10.48550/arXiv.2412.19437 (2025).
Alayrac, J.-B. et al. Flamingo: a visual language model for few-shot learning. Adv. Neural Inform. Process. Syst. 35, 23716–23736 (2022).
Li, F. et al. LLaVA-NeXT-Interleave: tackling multi-image, video, and 3D in large multimodal models. Preprint at arXiv https://doi.org/10.48550/arXiv.2407.07895 (2024).
Wang, L., Chen, X., Zhao, J. & Kaiming, H. Scaling proprioceptive-visual learning with heterogeneous pre-trained transformers. Preprint at arXiv https://doi.org/10.48550/arXiv.2409.20537 (2024).
Bordes, F. et al. An introduction to vision-language modeling. Preprint at arXiv https://doi.org/10.48550/arXiv.2405.17247 (2024).
Graybiel, A. M. & Grafton, S. T. The striatum: where skills and habits meet. Cold Spring Harb. Perspect. Biol. 7, a021691 (2015).
Google Scholar
Gummadi, M., Kent, C., Schmeckpeper, K. & Eaton, E. A metacognitive approach to out-of-distribution detection for segmentation. Preprint at arXiv https://doi.org/10.48550/arXiv.2311.07578 (2023).
Mirzaei, H. & Mathis, M. W. Adversarially robust out-of-distribution detection using Lyapunov-stabilized embeddings. Proceedings of the 13th International Conference on Learning Representations (ICLR) (2025).
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K. & Müller, K.-R. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning Vol. 11700 (Springer Nature, 2019).
Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?”: explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (2016).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems Vol. 30, 4768–4777 (Curran Associates, 2017).
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. Proceedings of the 34th International Conference on Machine Learning Vol. 70, 3319–3328 (2017).
Schneider, S., Laiz, R. G., Filippova, A., Frey, M. & Mackenzie, W. M. Time-series attribution maps with regularized contrastive learning. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) (2025).
Lotter, W., Kreiman, G. & Cox, D. Deep predictive coding networks for video prediction and unsupervised learning. Preprint at arXiv https://doi.org/10.48550/arXiv.1605.08104 (2017).
Assran, M. et al. Self-supervised learning from images with a joint-embedding predictive architecture. Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 15619–15629 (2023).
Hausmann, S. B., Vargas, A. M., Mathis, A. & Mathis, M. W. Measuring and modeling the motor system with machine learning. Curr. Opin. Neurobiol. 70, 11–23 (2021).
Google Scholar
Jordan, R. & Keller, G. B. The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity. eLife 12, RP85111 (2023).
Google Scholar
Lim, L., Mi, D., Llorca, A. & Marín, O. Development and functional diversification of cortical interneurons. Neuron 100, 294–313 (2018).
Google Scholar
Wu, H., Xiong, W.-C. & Mei, L. To build a synapse: signaling pathways in neuromuscular junction assembly. Development 137, 1017–1033 (2010).
Google Scholar
Gou, J., Yu, B., Maybank, S. J. & Tao, D. Knowledge distillation: a survey. Int. J. Comput. Vis. 129, 1789–1819 (2020).
Google Scholar
Dasen, J. S. & Jessell, T. M. Hox networks and the origins of motor neuron diversity. Curr. Top. Dev. Biol. 88, 169–200 (2009).
Google Scholar
Shuvaev, S. A., Lachi, D., Koulakov, A. A. & Zador, A. M. Encoding innate ability through a genomic bottleneck. Proc. Natl Acad. Sci. USA 121, e2409160121 (2024).
Google Scholar
Muller, L. E., Churchland, P. S. & Sejnowski, T. J. Transformers and cortical waves: encoders for pulling in context across time. Trends Neurosci. 47, 788–802 (2024).
Google Scholar
Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659–1671 (1996).
Google Scholar
Roy, K., Jaiswal, A. R. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019).
Google Scholar
Eliasmith, C. et al. A large-scale model of the functioning brain. Science 338, 1202–1205 (2012).
Google Scholar
Blouw, P., Solodkin, E., Thagard, P. & Eliasmith, C. Concepts as semantic pointers: a framework and computational model. Cogn. Sci. 40, 1128–1162 (2016).
Google Scholar
He, X.-Y. et al. An efficient knowledge transfer strategy for spiking neural networks from static to event domain. Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38, 512–520 (AAAI Press, 2024).
Wunderlich, T. C. & Pehle, C. Event-based backpropagation can compute exact gradients for spiking neural networks. Sci. Rep. 11, 12829 (2021).
Google Scholar
