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• W2894938705 abstract "Abstract Spontaneous firing sequences are ubiquitous in cortical networks, but their roles in cellular and network-level computations remain unexplored. In the hippocampus, such sequences, conventionally called preplay, have been hypothesized to participate in learning and memory. Here, we present a computational model for encoding input sequence patterns into internal network states based on the propagation of preplay sequences in recurrent neuronal networks. The model instantiates two synaptic pathways in cortical neurons, one for proximal dendrite-somatic interactions to generate intrinsic preplay sequences and the other for distal dendritic processing of extrinsic signals. The core dendritic computation is the maximization of matching between patterned activities in the two compartments through nonlinear spike generation. The model performs robust single-trial learning with long-term stability and independence that are modulated by the plasticity of dendrite-targeted inhibition. Our results demonstrate that dendritic computation enables somatic spontaneous firing sequences to act as templates for rapid and stable memory formation." @default.
• W2894938705 created "2018-10-12" @default.
• W2894938705 creator A5006335559 @default.
• W2894938705 creator A5073758227 @default.
• W2894938705 date "2018-10-11" @default.
• W2894938705 modified "2023-10-06" @default.
• W2894938705 title "Dendritic processing of spontaneous neuronal sequences for single-trial learning" @default.
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• W2894938705 doi "https://doi.org/10.1038/s41598-018-33513-9" @default.
• W2894938705 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6181986" @default.
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• W2894938705 hasPublicationYear "2018" @default.
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