Most biological models are composed of single-compartment neurons. Despite recent ﬁndings on dendritic computational properties, these features have been only rarely exploited [1]. We introduce a multi-compartment model for pyramidal neurons [2], in which bursts and dendritic input segregation allow for biologically plausible target-based learning [3]. The solution of a problem is represented as a spatio-temporal pattern of bursts, suggested to apical tufts of neurons. A store-and-recall and a nontrivial navigation tasks are learned, the latter by introducing a hierarchical architecture of layers enabling the decomposition into simpler subtasks (hierarchical imitation learning) [2]. Acknowledgements This work has been supported by the European Union Horizon 2020 Research and Innovation program under the FET Flagship Human Brain Project (grant agreement SGA3 n. 945539) and by the INFN APE Parallel and Distributed Computing Laboratory. References [1] Payeur, Guerguiev, Zenke, Richards, Naud. Nature neurosci. (2021) 24, 1010. [2] Capone, Lupo, Muratore, Paolucci. arXiv (2022) 2201.11717. [3] Muratore, Capone, Paolucci. PloS one (2021) 16(2), e0247014. Cosimo Lupo*,1, Cristiano Capone*,1, Paolo Muratore2, Pier Stanislao Paolucci1 1 INFN, Sezione di Roma, Rome, Italy — 2 SISSA, Trieste, Italy. * These authors contributed equally to the work. Burst-dependent plasticity and dendritic ampliﬁcation support target-based learning and hierarchical imitation learning Coincidence mechanism • Somatic spikes open a window • Apical spikes within the window create the coincidence • Result is a burst activity Store-and-recall task • Learn a target 3D trajectory • After many presentations: - Internal bursting activity spontaneously reproduced - Accurate output (mse ~10-2) Target-based learning Soma+distal internal bursting activity is the target, soma+proximal bursts have to reproduce it: Multi-compartment pyramidal neuron • Dendritic segregation • Apical proximal compartment for recurrent synapses from other neurons • Apical distal compartment for target and context signals Apical context as target selector • Desired target is selected by the context • Successful target recover even after apical context switch-off or corruption through noise • Basal context has no such robustness Hierarchical architecture • High network: - task-selecting output as context for low network • Low network: - task-actuation output, selected by injected context Hierarchical imitation learning • Manager/worker architecture is able to solve the task • High network learns which task to solve • Low network learns how to reach the target (button or food) • Learning on ~100 button/food examples Performances • Test is done in closed loop • Button reaching rate ~100% • Apical context crucial for hierarchical decomposition (basal context not able to properly assign the task) Button-and-food task • First reach the button and unlock it; then reach the food • Reaching food in ﬁrst place gives no reward • Button/food positions are random Contacts Email: cosimo.lupo89@gmail.com Conclusions • Target-based learning is an alternative to error-based approaches, offering several advantages • Bursts and dendritic segregation naturally support it • Target-based learning also allows to successfully implement hierarchical policies boosting Hierarchical Imitation Learning Future outlooks • Effects of sleep (replay of burst patterns and performance improvement) arXiv preprint Accepted in ICML 2022