Alireza Saeedi

Alireza Saeedi
Max Planck Institute for Biological Cybernetics | KYB · Department of Physiology of Cognitive Processes

Master of Science

About

4
Publications
717
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
16
Citations

Publications

Publications (4)
Article
Full-text available
During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single-electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particula...
Article
Full-text available
The authors have retracted this article Jannesari et al. (2019) because an incorrect version of the article was published in error. The manuscript has been republished as Jannesari et al. (2020). All authors agree to this retraction.
Article
Full-text available
During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particula...
Article
Self-organized criticality (SOC) and stochastic oscillations (SOs) are two theoretically contradictory phenomena that are suggested to coexist in the brain. Recently it has been shown that an accumulation-release process like sandpile dynamics can generate SOC and SOs simultaneously. We considered the effect of the network structure on this coexist...

Network

Cited By

Projects

Project (1)
Project
To investigate bilateral visual integration, we attempt to connect an artificial neural the network that functions as a visual cortical hemisphere to a mouse's cortical hemisphere, using an invasive brain-machine interface. Given the recent progress in Deep Learning (e.g. GAN), we will test various types of neural networks computed on GPUs and ultimately develop our own which would stablish mutual interactions between the biological hemisphere. The brain-machine system will be validated by a "biological visual field vs. machine visual field" comparison task.