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- W3086624272 abstract "Abstract How does spontaneous activity during development prepare cortico-cortical connections for sensory input? We here analyse the development of sequence memory, an intrinsic feature of recurrent networks that supports temporal perception. We use a recurrent neural network model with homeostatic and spike-timing-dependent plasticity (STDP). This model has been shown to learn specific sequences from structured input. We show that development even under unstructured input increases unspecific sequence memory. Moreover, networks “pre-shaped” by such unstructured input subsequently learn specific sequences faster. The key structural substrate is the emergence of strong and directed synapses due to STDP and synaptic competition. These construct self-amplifying preferential paths of activity, which can quickly encode new input sequences. Our results suggest that memory traces are not printed on a tabula rasa , but instead harness building blocks already present in the brain." @default.
- W3086624272 created "2020-09-21" @default.
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- W3086624272 date "2020-09-16" @default.
- W3086624272 modified "2023-09-27" @default.
- W3086624272 title "Sequence memory in recurrent neuronal network can develop without structured input" @default.
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- W3086624272 doi "https://doi.org/10.1101/2020.09.15.297580" @default.
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