Encoding information into autonomously bursting neural network with pairs of time-delayed pulses

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Title
Encoding information into autonomously bursting neural network with pairs of time-delayed pulses
Author(s)
Kim, June Hoan; Lee, Ho Jun; Wonshik Choi; Lee, Kyoung J.
Publication Date
2019-02
Journal
SCIENTIFIC REPORTS, v.9, no.1, pp.1394 -
Publisher
NATURE PUBLISHING GROUP
Abstract
Biological neural networks with many plastic synaptic connections can store external input information in the map of synaptic weights as a form of unsupervised learning. However, the same neural network often produces dramatic reverberating events in which many neurons fire almost simultaneously – a phenomenon coined as ‘population burst.’ The autonomous bursting activity is a consequence of the delicate balance between recurrent excitation and self-inhibition; as such, any periodic sequences of burst-generating stimuli delivered even at a low frequency (~1 Hz) can easily suppress the entire network connectivity. Here we demonstrate that ‘Δt paired-pulse stimulation’, can be a novel way for encoding spatially-distributed high-frequency (~10 Hz) information into such a system without causing a complete suppression. The encoded memory can be probed simply by delivering multiple probing pulses and then estimating the precision of the arrival times of the subsequent evoked recurrent bursts. © 2019, The Author(s)
URI
https://pr.ibs.re.kr/handle/8788114/5714
ISSN
2045-2322
Appears in Collections:
Center for Molecular Spectroscopy and Dynamics(분자 분광학 및 동력학 연구단) > Journal Papers (저널논문)
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