Recurrent Neural Networks in Brains and Machines
Explore neural networks as models for brain activity, examining their evolution and potential for bridging multiple cognitive systems. Gain insights into ongoing research and future directions in neuroscience.
Unlocking Insights in Complex and Dense Neuron Images Guided by AI
Explore AI-guided techniques for analyzing complex 3D neuron morphology, overcoming challenges in multi-modal imaging and diverse neuronal densities.
Стохастичний механізм підвищення селективності проекційних нейронів нюхової цибулини
Дослідіть стохастичні механізми та їх роль у підвищенні селективності нейронів нюхової цибулини, розкриваючи фундаментальні аспекти нейробіології.
The Bitter Lesson of Retrieval in Generative Language Model Workflows
Discover how leveraging data and compute in RAG architectures can enhance generative AI applications, with insights from YOKOT.AI's implementation for enterprise data solutions.
Backpropagation and Deep Learning in the Brain
Explore deep learning in the brain, backpropagation, and credit assignment algorithms. Discover neuroscience evidence and new models of neurons in this computational brain theory talk.
Self-Sustained Activity and Intermittent Synchronization in Balanced Networks
Explore self-sustained activity and intermittent synchronization in balanced networks with Fernando da Silva's insightful presentation.
Implementing Engrams from a Machine Learning Perspective: Matching for Prediction
Explore machine learning approaches to implementing engrams, focusing on matching techniques for predictive modeling in neuroscience and cognitive science.
Neural Network Models of Visual Learning and Development
Explore neural network models for understanding visual learning and development in natural and artificial intelligence with MIT postdoctoral fellow Chengxu Zhuang.
Understanding Higher-Level Intelligence: 10 AIs or 10 Billion AIs
Explore the future of AI: comparing small, specialized models with large, general-purpose systems. Insights from AI, psychology, and neuroscience on higher-level intelligence.
Computation in the Brain - Part 1
Explore neural computation through a historical lens, from ancient theories to modern discoveries in neuroscience, covering key concepts like synapses, neurotransmitters, and brain structures.
Learning from Machines
Explore cognitive models inspired by machine learning, offering insights into perception, expertise, and trauma. Gain a new perspective on human cognition and self-awareness.
Learning From Machines
Explore the intersection of neuroscience and machine learning, uncovering insights into human cognition, perception, and consciousness through computational models and visual metaphors.
Behavioral Signatures of a Developing Neural Code
Explore neural code development through behavioral signatures, focusing on computational neuroscience insights from Lilach Avitan's research at Hebrew University.
Autodiagnosis and the Dynamical Emergence Theory of Basic Consciousness
Explores the Dynamical Emergence Theory of consciousness, proposing that basic awareness arises from a system's ability to intrinsically discern its own states through dynamic autodiagnosis.
The Hodgkin-Huxley Model - Understanding Neural Electrical Signals
Dive into the Nobel Prize-winning Hodgkin-Huxley model to understand how neurons generate electrical signals, exploring membrane voltage, ion channels, and the biophysical principles of neural computation.
Neuroscience and AI - What Artificial Intelligence Teaches Us About the Brain and Vice Versa
Explore the fascinating intersection of neuroscience and AI with Stanford expert Surya Ganguli, examining how modern AI systems inform our understanding of biological intelligence and brain function.
Reverse Engineering the Visual System
Explorez les mécanismes du système visuel humain à travers une approche d'ingénierie inverse, révélant ses complexités et son fonctionnement fascinant.
A Neurogeometric Stereo Model for Individuation of 3D Perceptual Units
Explore a neurogeometric model for stereo vision and 3D perceptual unit individuation, focusing on sub-Riemannian structures and horizontal curves.
Introduction to Neuron and Perceptron: Working Principle - Lecture 1
Explore the working principle of perceptrons in neural networks, laying the foundation for understanding artificial neurons and their role in machine learning.
Comparing Human and AI-Constructed Models of a Neural Circuit
Explore groundbreaking research comparing neural circuit models developed by humans and AI, examining their implications for neuroscience and artificial intelligence advancement.
Towards Understanding Adaptive Intelligence
Explore groundbreaking research on adaptive intelligence and its implications for neuroscience and AI, examining the fascinating intersection between biological and artificial learning systems.