Neural binding is the neuroscientific aspect of what is commonly known as the binding problem: the interdisciplinary difficulty of creating a comprehensive and verifiable model for the unity of consciousness. "Binding" refers to the integration of highly diverse neural information in the forming of one's cohesive experience. The neural binding hypothesis states that neural signals are paired through synchronized oscillations of neuronal activity that combine and recombine to allow for a wide variety of responses to context-dependent stimuli. These dynamic neural networks are thought to account for the flexibility and nuanced response of the brain to various situations. The coupling of these networks is transient, on the order of milliseconds, and allows for rapid activity. A viable mechanism for this phenomenon must address (1) the difficulties of reconciling the global nature of the participating (exogenous) signals and their relevant (endogenous) associations, (2) the interface between lower perceptual processes and higher cognitive processes, (3) the identification of signals (sometimes referred to as “tagging”) as they are processed and routed throughout the brain, and (4) the emergence of a unity of consciousness. Proposed adaptive functions of neural binding have included the avoidance of hallucinatory phenomena generated by endogenous patterns alone as well as the avoidance of behavior driven by involuntary action alone. There are several difficulties that must be addressed in this model. First, it must provide a mechanism for the integration of signals across different brain regions (both cortical and subcortical). It must also be able to explain the simultaneous processing of unrelated signals that are held separate from one another and integrated signals that must be viewed as a whole. (Wikipedia).
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
Build your own neural network, Exercise 6
Get the full course experience at https://e2eml.school/312 In this course we build a neural network framework from scratch. By the time you are done, you will have a simple but fully functional neural network framework. You will understand every important concept, including optimization,
From playlist E2EML 312. Build a neural network framework
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!
Multilayer Neural Networks - Part 1: Introduction
This video is about Multilayer Neural Networks - Part 1: Introduction Abstract: This is a series of video about multi-layer neural networks, which will walk through the introduction, the architecture of feedforward fully-connected neural network and its working principle, the working prin
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Graph Neural Networks, Session 2: Graph Definition
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From playlist Graph Neural Networks (Hands-on)
In this video, I present some applications of artificial neural networks and describe how such networks are typically structured. My hope is to create another video (soon) in which I describe how neural networks are actually trained from data.
From playlist Machine Learning
Build your own neural network, Exercise 4
Get the full course experience at https://e2eml.school/312 In this course we build a neural network framework from scratch. By the time you are done, you will have a simple but fully functional neural network framework. You will understand every important concept, including optimization,
From playlist E2EML 312. Build a neural network framework
Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA
Recorded 10 January 2023. Osbert Bastani of the University of Pennsylvania presents "Interpretable Machine Learning via Program Synthesis" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: Existing approaches to interpretability largely focus on fixed mo
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Geometric deep learning for functional protein design - Michael Bronstein
Seminar on Theoretical Machine Learning Topic: Geometric deep learning for functional protein design Speaker Michael Bronstein Affiliation: Imperial College London Date: February 20, 2020 For more video please visit http://video.ias.edu
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Learning the Regulatory Code of the Accessible Genome - D. Kelley - 1/14/16
Full Title: Learning the Regulatory Code of the Accessible Genome with Deep Convolutional Neural Networks Bioinformatics Research Symposium Beckman Institute Auditorium Thursday, January 14, 2016
From playlist Bioinformatics Research Symposium
AMMI Course "Geometric Deep Learning" - Lecture 12 (Applications & Conclusions) - Michael Bronstein
Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July-August 2021 by Michael Bronstein (Imperial College/Twitter), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind) Lecture 12: What's next? • Beyond Mess
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Neural Networks for Images & Audio Workshop
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Reading and Writing the Cell Fate Code - M. Thomson - 1/14/16
Bioinformatics Research Symposium Beckman Institute Auditorium Thursday, January 14, 2016
From playlist Bioinformatics Research Symposium
This lecture discusses some key limitations of neural networks and suggests avenues of ongoing development. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
End-to-End Differentiable Proving: Tim Rocktäschel, University of Oxford
We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specific
From playlist Logic and learning workshop
Lecture 10: What's Next? - Michael Bronstein
Video recording of the First Italian School on Geometric Deep Learning held in Pescara in July 2022. Slides: https://www.sci.unich.it/geodeep2022/slides/Pescara%202022%20-%20conclusions.pdf Blog post: https://towardsdatascience.com/graph-neural-networks-beyond-weisfeiler-lehman-and-vani
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How Does Caffeine Wake You Up?
When you get coffee jitters, caffeine has tricked your brain into anticipating danger. Lauren explains how it works. Learn more at HowStuffWorks.com: http://science.howstuffworks.com/caffeine.htm Share on Facebook: https://goo.gl/adWdyH Share on Twitter: https://goo.gl/IrlOYS Subscribe:
From playlist How Lauren Vogelbaum Works
Frank Noe - Advancing molecular simulation with deep learning - IPAM at UCLA
Recorded 23 January 2023. Frank Noe of Freie Universität Berlin presents "Advancing molecular simulation with deep learning" at IPAM's Learning and Emergence in Molecular Systems Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/learning-and-emergence-in-molecular
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