Brain Criticality - Optimizing Neural Computations
Explore how the brain optimizes information processing by operating near critical points, examining phase transitions, neuronal avalanches, and scale-free properties in neural networks.
Information Theory of Deep Learning - Naftali Tishby
Explores the connection between deep learning and information theory, offering new insights into neural network design, generalization, and optimization through an information bottleneck framework.
Computational Principles Underlying the Learning of Sensorimotor Repertoires
Explore computational principles of motor learning, focusing on context's role in memory activation and how statistical learning shapes object representations.
Neurobiology - Vijay Balasubramanian
Explore computational neurobiology with Vijay Balasubramanian, covering neurons, retina, efficiency, and the brain's computational language in this comprehensive tutorial.
Embodiment Without Situatedness - An Embodied Perspective on Neural Architecture Search
Explore embodied perspectives on neural architecture search with Nick Cheney, examining the intersection of neurorobotics and complex systems in AI development and intelligence research.
Evolving Brains - Synthetic Brains and Minds: What is Possible? - Lecture 2
Exploring synthetic biology, artificial life, and AI to understand cognitive complexity and the potential for creating synthetic minds different from human cognition.
Contextual and Bottom-Up Attention-Guided Large-Scale Visual Inferences
Explore visual inference techniques, including bottom-up attention and unsupervised object discovery, with applications in neuroscience, gesture recognition, and machine learning.
How Experiment and Theory Have Informed Each Other in Neuroscience - Past 25 Years
Explore a captivating lecture by 2024 Nobel Prize in Physics winner John Hopfield, delivered at the Kavli Institute for Theoretical Physics in 2010. Delve into the intricate relationship between experiment and theory in neuroscience over the past quarter…
The Functional Role of Oscillatory Dynamics in Neocortical Circuits: A Computational Perspective
Explore computational perspectives on oscillatory dynamics in neocortical circuits, examining their functional roles and implications for neural processing.
Large Language Models and Transformers: Perspectives from Physics, Neuroscience, and Theory
Explore perspectives on large language models and transformers from physics, neuroscience, and theory with Stanford's Surya Ganguli in this insightful Simons Institute lecture.
In-Context Principle Learning from Mistakes
Explore Uri Alon's insights on in-context principle learning from mistakes, focusing on emerging generalization settings in AI and machine learning.
Towards a Computational Model of the Brain - Tools for Mapping and Perturbing Neural Circuits - Ed Boyden
Explore cutting-edge tools and techniques for mapping and manipulating neural circuits, advancing our understanding of brain structure and function at unprecedented levels of detail.
Theoretical Study of the Emergence of Periodic Solutions for the Inhibitory NNLIF Neuron Model with Synaptic Delay
Explore periodic solutions in inhibitory NNLIF neuron models with synaptic delay through theoretical analysis and mathematical insights.
A New Synaptic Rule to Unify Experimental Heterogeneity
Découvrez une nouvelle règle synaptique unifiant l'hétérogénéité expérimentale en neurosciences, présentée par Yuri Rodrigues de l'Université Côte d'Azur.
Characterizing Differences in Structural Network Changes from Traumatic Brain Injury
Exploring structural network changes in traumatic brain injury, focusing on motif coherence patterns and their potential link to clinical outcomes like post-traumatic epilepsy through computational modeling.
The Neuroscience of Consciousness
Explore the fascinating neuroscience of consciousness with Prof. Anil Seth, delving into how our biology shapes our unique experiences and sense of self.
Artificial Neural Networks Through the Lens of Dynamical Systems Theory
Explore artificial neural networks from a dynamical systems perspective, gaining insights into their behavior and properties.
Combining Innate Concepts with Learning to Perceive the World
Explore how innate concepts and learning processes interact to shape our perception of the world, drawing insights from AI, psychology, and neuroscience perspectives.
Stable Adaptation and Learning in Nonlinear Systems and Neural Networks
Explore nonlinear systems tools for insights on collective computation and learning in large dynamical networks, focusing on stable adaptation and implicit sparse regularization.
Topological Data Analysis on Dynamic Brain Networks
Explore topological data analysis for dynamic brain networks, learning to cluster networks into states and analyze temporal changes using Wasserstein distance. Gain insights into state space estimation and heritability of network changes.
Self-Study Computational Neuroscience - Coding, Textbooks, Math
Comprehensive guide to starting computational neuroscience: programming languages, coding practice, textbooks, math resources, project ideas, and datasets for self-study and skill development.