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  Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule

Saponati, M., & Vinck, M. (2023). Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule. Nature Communications, 14: 4985. doi:10.1038/s41467-023-40651-w.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Saponati, Matteo1, 2, Author
Vinck, Martin1, 2, Author                 
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1Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, ou_2074314              
2Vinck Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society, Deutschordenstraße 46, 60528 Frankfurt, DE, ou_3381242              

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 Abstract: Intelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

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 Dates: 2023-08-21
 Publication Status: Published online
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 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41467-023-40651-w
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Title: Nature Communications
Source Genre: Journal
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Pages: - Volume / Issue: 14 Sequence Number: 4985 Start / End Page: - Identifier: ISSN: 2041-1723