ArticleScientific reports2019
Encoding information into autonomously bursting neural network with pairs of time-delayed pulses.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review.Microsystems & nanoengineering · 2023Review
- Disruption of the autism-associated geneFrontiers in cellular neuroscience · 2023Article
- Coherence resonance for neuronal bursting with spike undershoot.Cognitive neurodynamics · 2021Article
- ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling.Frontiers in computational neuroscience · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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