Phase-Amplitude Coupling in Neuronal Oscillator Networks

Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one. We propose a model that explains the emergence of PAC in two commonly-accepted architectures in the brain, namely, a high-frequency neural oscillation driven by an external low-frequency input and two interacting local oscillations with distinct, locally-generated frequencies. We further propose an interconnection structure for brain regions and demonstrate that low-frequency phase synchrony can integrate high-frequency activities regulated by local PAC and control the direction of information flow across distant regions.

[ source ]

#paper

Follow: @theTuringMachine

Brain & Mind Computational Seminar

23.2.2021 11:30 – 12:45 (Tehran Time)

A monthly seminar and venue for informal conversation about topics such as artificial intelligence, neuroscience, human behaviour, and digital humanities. Welcome!

Speakers:

Luigi Acerbi
Probabilistic machine and biological learning under resource constraints

Yasser Roudi

https://www.aalto.fi/en/events/brain-mind-computational-seminar

💰 Hey, I am hiring at UCNZ UCNZMaths
• 1 fully paid, 3 years, #PhD scholarship
• a bunch of Research Assistant positions

Us: Data Science analysis of Social Networks with a strong Social Justice focus (yes, we're SJW).
You: curious, willing to learn transdisciplinary.

Our research methodology is maths, data and code heavy: hashtag #complexnetworks #DeepLearning #julialang. We also dialogue a lot with other disciplines and take ethics seriously.

The PhD is your adventure: you don't need to know everything in advance; can mix and match.

https://www.gvdallariva.net/

"Information theory of networks"

https://youtu.be/WeZNyQUYnz0

https://pedramardakani.wordpress.com/2021/02/19/keyboard-ninja-series-3

#blog #tutorial #gnu #linux #shell

Can you speak Python?

Are you familiar with the Python programming language, along with the basic packages for scientific computing (NumPy/SciPy/Matplotlib/Pandas)?

To test your knowledge of these basics (and point you to relevant documentation to fill in any gaps), Aalto Scientific Computing team has designed the Gizmo challenge.

https://github.com/wmvanvliet/gizmo

Tune in Wednesday, Feb. 24 at 11 am (BRT time) at https://t.co/xgP5EtRry4 for the third colloquium series of Physics Discussions presented by Prof. Steven Strogatz (Cornell University).

https://pedramardakani.wordpress.com/2021/02/26/dark-mode-everywhere/

#blog #tutorial #browser

Considering sex as a biological variable will require a global shift in science culture
http://feeds.nature.com/~r/neuro/rss/current/~3/zVCGJgqZlKk/s41593-021-00806-8

Nature Neuroscience, Published online: 01 March 2021; doi:10.1038/s41593-021-00806-8 (https://www.nature.com/articles/s41593-021-00806-8)Mandates to include both sexes are a critical step toward improving the translational value of preclinical research, but they will not succeed without intentional, large-scale shifts in scientific incentive structures and publishing standards.

Synaptic plasticity as Bayesian inference
http://feeds.nature.com/~r/neuro/rss/current/~3/T3ULWJgeCiE/s41593-021-00809-5

Nature Neuroscience, Published online: 11 March 2021; doi:10.1038/s41593-021-00809-5 (https://www.nature.com/articles/s41593-021-00809-5)We propose that synapses compute probability distributions over weights, not just point estimates. Using probabilistic inference, we derive a new set of synaptic learning rules and show that they speed up learning in neural networks.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems:

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neurosci- ence, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only as- sumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

[ source ] - [ Github ] - [ pySINDy-paper ] - [ documentation ]

Follow: @theTuringMachine