Neural accommodation or neuronal accommodation occurs when a neuron or muscle cell is depolarised by slowly rising current (ramp depolarisation) in vitro. The Hodgkin–Huxley model also shows accommodation. Sudden depolarisation of a nerve evokes propagated action potential by activating voltage-gated fast sodium channels incorporated in the cell membrane if the depolarisation is strong enough to reach threshold. The open sodium channels allow more sodium ions to flow into the cell and resulting in further depolarisation, which will subsequently open even more sodium channels. At a certain moment this process becomes regenerative (vicious cycle) and results in the rapid ascending phase of action potential. In parallel with the depolarisation and sodium channel activation, the inactivation process of the sodium channels is also driven by depolarisation. Since the inactivation is much slower than the activation process, during the regenerative phase of action potential, inactivation is unable to prevent the "chain reaction"-like rapid increase in the membrane voltage. During neuronal accommodation, the slowly rising depolarisation drives the activation and inactivation, as well as the potassium gates simultaneously and never evokes action potential. Failure to evoke action potential by ramp depolarisation of any strength had been a great puzzle until Hodgkin and Huxley created their physical model of action potential. Later in their life they received a Nobel Prize for their influential discoveries. Neuronal accommodation can be explained in two ways. "First, during the passage of a constant cathodal current through the membrane, the potassium conductance and the degree of inactivation will rise, both factors raising the threshold. Secondly, the steady state ionic current at all strengths of depolarization is outward, so that an applied cathodal current which rises sufficiently slowly will never evoke a regenerative response from the membrane, and excitation will not occur." (quote from Hodgkin and Huxley) In vivo physiologic condition accommodation breaks down, that is long-duration slowly rising current excites nerve fibers at a nearly constant intensity no matter how slowly this intensity is approached. (Wikipedia).
Mapping The Brain | Digging Deeper
Should the United States spend billions to completely map the human brain? Will it ever be possible to build an artificial brain - and, if we do, what are the implications for the future? Join Ben and Matt as they talk about some interesting stuff that didn't make it into the Deceptive Bra
From playlist Stuff They Don't Want You To Know, New Episodes!
Artificial Neural Networks (2 of 3)
From playlist Decision Support Systems
Lecture 2A : An overview of the main types of neural network architecture
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] Lecture 2A : An overview of the main types of neural network architecture
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]
For cool updates on AI research, follow me at https://twitter.com/iamvriad. 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-
From playlist [Coursera] Neural Networks for Machine Learning — Geoffrey Hinton
Neuralink: Merging Man and Machine
Neuralink: Merging Man and Machine - Neuralink Explained Signup for a FREE trial to The Great Courses Plus here: http://ow.ly/2Jc830q7vB0 Follow me!: https://www.instagram.com/mcewen/ Elon Musk and Neuralink are carving a path into digitizing humanity, but many people are still in the dar
From playlist Science & Technology 🚀
This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
Interactive Learning of Task-Oriented Dialog Systems, Rasa Developer Summit 2019
Bing Liu, of Facebook shares how task-oriented spoken dialog systems are a prominent component in today’s virtual personal assistants (e.g. Alexa, Siri), which enables people to perform everyday tasks by interacting with devices via voice interfaces. Recent advances in deep learning enab
From playlist Rasa Developer Summit 2019
SHOP TIPS #215 Knurling on the Clausing Lathe tubalcain knurler
Tubalcain demonstrates knurling using a turret lathe type knurler adapted to a toolpost. I have over 1300 shop videos---watch them all. Search "tubalcain"
From playlist #3 MACHINE SHOP TIPS tubalcain playlist #201 thru #300
Can AI Learn to Cooperate? Multi Agent Deep Deterministic Policy Gradients (MADDPG) in PyTorch
Multi agent deep deterministic policy gradients is one of the first successful algorithms for multi agent artificial intelligence. Cooperation and competition among AI agents is going to be critical as applications of deep learning expand in our daily lives. In this tutorial, we are going
From playlist Advanced Actor Critic and Policy Gradient Methods
Advanced IR - Course Introduction 2022
Welcome 👋This is the introduction to the 2022 edition of Advanced Information Retrieval @ TU Wien. Slides & transcripts are available at: https://github.com/sebastian-hofstaetter/teaching 📖 Check out Youtube's CC - we added our high quality (human corrected) transcripts here as well. Sl
From playlist Advanced Information Retrieval 2022
How to Deploy Machine Learning Model from Scratch | Part - 1
This is the second video of the series. In this video, I have shown how to get the dataset for this project and how to build a simple CNN model using Keras. Stay tuned for the upcoming videos where I'll be showing how to set up a flask server and how to accommodate machine learning things
From playlist How to Deploy Machine Learning Apps?
Graph Neural Networks, Session 2: Graph Definition
Types of Graphs Common data structures for storing graphs
From playlist Graph Neural Networks (Hands-on)
Sentence Tokenization in Transformer Code from scratch!
ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [ 1 🔎 ] Samanantar: The paper https://paperswith
From playlist Transformers from scratch
Scaling the Mountain with Continuous Actor Critic Methods | PyTorch Tutorial
In this tutorial you're going to code a continuous actor critic agent to play the mountain car environment. We'll see that it comes up with a pretty smart solution that is precisely what we don't want. This really shows how the design of the agent's reward can backfire and lead to undesir
From playlist Get Started with Actor Critic and Policy Gradient Methods
The Neuroscience of Creativity
SUBSCRIBE to BrainCraft for more brain hacks, health tips and stories and psychology (and ring that bell!) 👉 http://ow.ly/rt5IE MY PATREON! https://www.patreon.com/BrainCraft My Twitter https://twitter.com/nessyhill | Instagram https://instagram.com/nessyhill Your brain uses lots of diff
From playlist Tips for Working From Home
Learn AI | Neural Style Transfer Using Keras & Tensorflow | Session 03 | #AI
Don’t forget to subscribe! This project series will guide you about neural style transfer using Keras & Tensorflow. This series will cover all the important steps that you need to learn for neural style transfer. We will use Keras & Tensorflow as a tool to perform neural style transfer.
From playlist Neural Style Transfer Using Keras & Tensorflow
Elias Khalil - Neur2SP: Neural Two-Stage Stochastic Programming - IPAM at UCLA
Recorded 02 March 2023. Elias Khalil of the University of Toronto presents "Neur2SP: Neural Two-Stage Stochastic Programming" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Stochastic Programming is a powerful modeling framework for decision-making under un
From playlist 2023 Artificial Intelligence and Discrete Optimization
One Neural network learns EVERYTHING ?!
We explore a neural network architecture that can solve multiple tasks: multimodal Neural Network. We discuss important components and concepts along the way. If you like this video, hit that like button. If you really like this video, hit that SUBSCRIBE button. And if you just love me hi
From playlist Deep Learning Research Papers
A brief tale on the possibilities of brain-to-brain communication, with a few cats. SUBSCRIBE to BrainCraft! Click here: http://ow.ly/rt5IE Talking psychology, neuroscience & why we act the way we do. Follow BrainCraft on Twitter https://twitter.com/nessyhill or https://twitter.com/Brain
From playlist Technology & Your Brain