Beyond spiking networks: the computational advantages of dendritic amplification and input segregation

[2211.02553] Beyond spiking networks: the computational advantages of dendritic amplification and input segregation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Neurons and Cognition arXiv:2211.02553 (q-bio) [Submitted on 4 Nov 2022] Title: Beyond spiking networks: the computational advantages of dendritic amplification and input segregation Authors: Cristiano Capone , Cosimo Lupo , Paolo Muratore , Pier Stanislao Paolucci View a PDF of the paper titled Beyond spiking networks: the computational advantages of dendritic amplification and input segregation, by Cristiano Capone and 3 other authors View PDF HTML (experimental) Abstract: The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons, and cannot achieve the state-of-the-art performances in machine learning. Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons. However, these approaches require propagating errors with a fine spatio-temporal structure to the neurons, which is unlikely to be feasible in a biological network. To relax this assumption, we suggest that bursts and dendritic input segregation provide a natural support for biologically plausible target-based learning, which does not require error propagation. We propose a pyramidal neuron model composed of three separated compartments. A coincidence mechanism between the basal and the apical compartments allows for generating high-frequency bursts of spikes. This architecture allows for a burst-dependent learning rule, based on the comparison between the target bursting activity triggered by the teaching signal and the one caused by the recurrent connections, providing the support for target-based learning. We show that this framework can be used to efficiently solve spatio-temporal tasks, such as the store and recall of 3D trajectories. Finally, we suggest that this neuronal architecture naturally allows for orchestrating ``hierarchical imitation learning'', enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks. This can be implemented in a two-level network, where the high-network acts as a ``manager'' and produces the contextual signal for the low-network, the ``worker''. Comments: arXiv admin note: substantial text overlap with arXiv:2201.11717 Subjects: Neurons and Cognition (q-bio.NC) Cite as: arXiv:2211.02553 [q-bio.NC] (or arXiv:2211.02553v1 [q-bio.NC] for this version) https://doi.org/10.48550/arXiv.2211.02553 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proc. Natl. Acad. Sci. U.S.A. 120, e2220743120 (2023) Related DOI : https://doi.org/10.1073/pnas.2220743120 Focus to learn more DOI(s) linking to related resources Submission history From: Cristiano Capone [ view email ] [v1] Fri, 4 Nov 2022 16:20:15 UTC (4,528 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond spiking networks: the computational advantages of dendritic amplification and input segregation, by Cristiano Capone and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.NC < prev | next > new | recent | 2022-11 Change to browse by: q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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