Lectures related to Neuroscience and ML

Neuroscience Inspired Artificial Intelligence

Explores the historical development of deep learning, reinforcement learning, attention mechanisms, and memory systems in AI inspired by neuroscience.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Neuroprosthetics: Hallucinations and Self Distortions

Explores hallucinations, self distortions, cognitive decline, and altered sensorimotor processing in neuroprosthetics.

The Three Models of Memory

Delves into the Multi-Store Memory Model, Working Memory Model, and memory principles.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Attention and Cognitive Load

Explores attention, arousal, cognitive load, and learning strategies.

Working Memory Model: Features and Applications

Explores the working memory model and its applications in learning, emphasizing simplicity and channel utilization.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Learning & Memory

Explores learning, memory, cognitive loads, metacognition, and problem-solving tasks.

Memory & Learning

Explores memory, learning, cognitive load, and problem-solving strategies to enhance learning and cognitive performance.

Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Modeling the Role of Hippocampus in Memory

Delves into the critical role of the hippocampus in memory formation, focusing on declarative memory and spatial representation.

Memory Encoding and Neuroplasticity

Explores memory encoding, neuronal plasticity, types of memory, brain regions, and clinical cases.

Neuroscience and Machine Learning

Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.