Encoding information into autonomously bursting neural network with pairs of time-delayed pulses
- Authors
- Kim, June Hoan; Lee, Ho Jun; Choi, Wonshik; Lee, Kyoung J.
- Issue Date
- 4-Feb-2019
- Publisher
- NATURE PUBLISHING GROUP
- Citation
- SCIENTIFIC REPORTS, v.9
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENTIFIC REPORTS
- Volume
- 9
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/67670
- DOI
- 10.1038/s41598-018-37915-7
- ISSN
- 2045-2322
- 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 (similar to 1 Hz) can easily suppress the entire network connectivity. Here we demonstrate that 'Delta t paired-pulse stimulation', can be a novel way for encoding spatially-distributed high-frequency (similar to 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.
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Collections - College of Science > Department of Physics > 1. Journal Articles
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