the Turing Machine
https://t.co/YjyruR6FUj #Neuroscience
Nature
Deep posteromedial cortical rhythm in dissociation
Nature - Dissociative states in mouse and human brains are traced to low-frequency rhythmic neural activity—with distinct molecular, cellular and physiological properties—in the deep...
Dear Member,
We are very excited to announce that Dr Murty Dinavahi, MBBS, PhD will present on the topic- 'Slow and fast gamma oscillations in human EEG Beyond laboratory- Insights from Alzheimer's disease research' on 27th September 2020 at 6:30 pm IST/ 9 am EDT (Poster attached). You are cordially invited to attend the talk. Seats are limited, please join at least 10 minutes before the meeting. The Meet link to join the talk- https://meet.google.com/wwg-bmwj-jus
Regards,
Chief of Internal Affairs
Project Encephalon
We are very excited to announce that Dr Murty Dinavahi, MBBS, PhD will present on the topic- 'Slow and fast gamma oscillations in human EEG Beyond laboratory- Insights from Alzheimer's disease research' on 27th September 2020 at 6:30 pm IST/ 9 am EDT (Poster attached). You are cordially invited to attend the talk. Seats are limited, please join at least 10 minutes before the meeting. The Meet link to join the talk- https://meet.google.com/wwg-bmwj-jus
Regards,
Chief of Internal Affairs
Project Encephalon
Google
Real-time meetings by Google. Using your browser, share your video, desktop, and presentations with teammates and customers.
The log-dynamic brain: how
skewed distributions affect network
operations
György Buzsáki1,2 and Kenji Mizuseki1,3
Abstract | We often assume that the variables of functional and structural brain parameters — such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons — have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions — from synapses to cognition — are related to each other.
http://www.buzsakilab.com/content/PDFs/Mizuseki2014.pdf
#Neuroscience
skewed distributions affect network
operations
György Buzsáki1,2 and Kenji Mizuseki1,3
Abstract | We often assume that the variables of functional and structural brain parameters — such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons — have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions — from synapses to cognition — are related to each other.
http://www.buzsakilab.com/content/PDFs/Mizuseki2014.pdf
#Neuroscience
Forwarded from Complex Systems Studies
pypercolate
A scientific Python package for Monte Carlo simulation of percolation on graphs
https://pypercolate.readthedocs.io/en/stable/newman-ziff.html
A scientific Python package for Monte Carlo simulation of percolation on graphs
https://pypercolate.readthedocs.io/en/stable/newman-ziff.html