Neural coding

Neural oscillation

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity. Neural oscillations in humans were observed by researchers as early as 1924 (by Hans Berger). More than 50 years later, intrinsic oscillatory behavior was encountered in vertebrate neurons, but its functional role is still not fully understood. The possible roles of neural oscillations include feature binding, information transfer mechanisms and the generation of rhythmic motor output. Over the last decades more insight has been gained, especially with advances in brain imaging. A major area of research in neuroscience involves determining how oscillations are generated and what their roles are. Oscillatory activity in the brain is widely observed at different levels of organization and is thought to play a key role in processing neural information. Numerous experimental studies support a functional role of neural oscillations; a unified interpretation, however, is still lacking. (Wikipedia).

Neural oscillation
Video thumbnail

AWESOME Electromagnetic force oscillation!!!

In this video i show electromagnetic force oscillation on a ruler. Also i demonstrate the standing wave on a ruler!

From playlist ELECTROMAGNETISM

Video thumbnail

Βy which factors the oscillation period depends!

The oscillation period in simple harmonic motion!

From playlist MECHANICS

Video thumbnail

Accelerated motion and oscillation!

In this video i demonstrate accelerated motion with interface. I show the graphs of simple accelerating motion and simple harmonic motion with force and motion sensor!

From playlist MECHANICS

Video thumbnail

What Are Brain Waves?

This video was sponsored by "Robot-Proof", written by Northeastern University's President, Joseph E. Aoun. Learn more here: https://goo.gl/uF5Kx8 Thank you to our supporters on https://www.patreon.com/MinuteEarth Even the parts of our brains that don't control physical movement show a lo

From playlist Fourier

Video thumbnail

B03 Simple harmonic oscillation

Explaining simple (idealised) harmonic oscillation, through a second-order ordinary differential equation.

From playlist Physics ONE

Video thumbnail

Demonstrating the phenomenon of beats on the oscilloscope (slow motion)-Amazing science experiment

The main point of this demonstration is to hear the beats. It may be desirable, however, for the students to also have a visual display of what is happening to cause the beats. This is the purpose of the oscilloscop

From playlist Beats

Video thumbnail

Physics - Ch 66 Ch 4 Quantum Mechanics: Schrodinger Eqn (45 of 92) Quantum Nature of Oscillator 1

Visit http://ilectureonline.com for more math and science lectures! In this video I will explain the quantum mature of the oscillator. I will explain the step-function that represent the energy differences between different energy states. The change of the energy can only happen 1 step an

From playlist PHYSICS 66.1 QUANTUM MECHANICS - SCHRODINGER EQUATION

Video thumbnail

How to inspect time-frequency results

If you are unsure of how to look at time-frequency results, this video has the 5-step plan that you need! It also discusses whether time-frequency features can be interpreted as "oscillations." For more online courses about programming, data analysis, linear algebra, and statistics, see h

From playlist OLD ANTS #1) Introductions

Video thumbnail

Lecture 8.4 — Echo State Networks [Neural Networks for Machine Learning]

Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. Link to the course (login required): https://class.coursera.org/neuralnets-2012-001

From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton

Video thumbnail

Lecture 8D : Echo state networks

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 8D : Echo state networks

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

Video thumbnail

Tomislav Stankovski - Neural Cross-frequency Coupling: delta-alpha, resting state, anesthesia, sleep

Recorded 02 September 2022. Tomislav Stankovski of the Cyril and Methodius University of Skopje presents "Neural Cross-frequency Coupling Functions: delta-alpha coupling in resting state, anesthesia and sleep" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscienc

From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond

Video thumbnail

Birdsong as a model for learned, complex behavior by Bernardo Gabriel Mindlin

DISCUSSION MEETING NEUROSCIENCE, DATA SCIENCE AND DYNAMICS (ONLINE) ORGANIZERS: Amit Apte (IISER-Pune, India), Neelima Gupte (IIT-Madras, India) and Ramakrishna Ramaswamy (IIT-Delhi, India) DATE : 07 February 2022 to 10 February 2022 VENUE: Online This discussion meeting on Neuroscien

From playlist Neuroscience, Data Science and Dynamics (ONLINE)

Video thumbnail

Serhiy Yanchuk - Adaptive dynamical networks: from multiclusters to recurrent synchronization

Recorded 02 September 2022. Serhiy Yanchuk of Humboldt-Universität presents "Adaptive dynamical networks: from multiclusters to recurrent synchronization" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond. Abstract: Adaptive dynamical networks is

From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond

Video thumbnail

DDPS | 'No Equations, No Variables, No Parameters, No Space and No time' by Yannis Kevrekidis

Title: 'No Equations, No Variables, No Parameters, No Space and No time, Data and the Modeling of Complex Systems' Description: I will start by showing how several successful NN architectures (ResNets, recurrent nets, convolutional nets, autoencoders, neural ODEs, operator learning....) h

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

Video thumbnail

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Joint work with Nathan Kutz: https://www.youtube.com/channel/UCoUOaSVYkTV6W4uLvxvgiFA Discovering physical laws and governing dynamical systems is often enabled by first learning a new coordinate system where the dynamics become simple. This is true for the heliocentric Copernican syste

From playlist Data-Driven Dynamical Systems with Machine Learning

Video thumbnail

Demonstrating the phenomenon of beats on the oscilloscope (normal speed)

The main point of this demonstration is to hear the beats. It may be desirable, however, for the students to also have a visual display of what is happening to cause the beats. This is the purpose of the oscilloscope.

From playlist Beats

Video thumbnail

Demonstrating the phenomenon of beats on the oscilloscope (normal speed)

The main point of this demonstration is to hear the beats. It may be desirable, however, for the students to also have a visual display of what is happening to cause the beats. This is the purpose of the oscilloscope.

From playlist Beats

Related pages

EEG analysis | Feedback | Neurophysiological Biomarker Toolbox | Oscillatory neural network | Neural network | Dynamical system | Macroscopic scale | Hertz | Pink noise | Granger causality | Neural coding | Gaussian noise | Bifurcation theory | Neural binding | Resonance | Frequency domain | Action potential | Positive feedback | Frequency | Central pattern generator | Limit cycle | FitzHugh–Nagumo model | Dynamical systems theory | Harmonic oscillator | Abstract structure | Trigonometric functions | Non-equilibrium thermodynamics | Phase resetting in neurons | Hindmarsh–Rose model | Kuramoto model | Hodgkin–Huxley model