Lectures related to Hopfield network

Attractor Networks and Spiking Neurons

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Hopfield Model: Memory and Dynamics

Explores the Hopfield Model for associative memory and its dynamics.

Attractor Networks and Generalizations

Explores attractor networks, Hopfield model generalizations, and memory dynamics in computational neuroscience.

Network Functions: Analysis and Design

Explores network functions in s-domain circuit analysis, covering stability, poles, responses, and design.

Energy landscape: Symmetric Interactions

Explores the energy landscape in symmetric interactions, emphasizing the importance of symmetric weights and random patterns.

Associative Memory: Magnetic Materials

Explores the dynamics of associative memory in networks of neurons and includes a detour into magnetic materials.

Dynamics of Linear Neural Networks

Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.

Modeling the role of hippocampus in spatial navigation

Discusses the role of the hippocampus in spatial navigation and the formation of place cell representation through recurrent network models.

Attractor Networks: Hopfield Model Generalizations

Explores attractor networks and Hopfield model generalizations in computational neuroscience, focusing on memory retrieval and dynamics convergence.

Neuronal Dynamics of Cognition: Associative Memory

Explores associative memory in neuronal networks, neuronal structure, and information processing.

Liquid Networks for Learning Control

Explores Liquid Networks for Learning Control in autonomous systems, emphasizing end-to-end learning and robust performance.

Convolutional Networks: Overview and Architecture

Covers the motivation and architecture of convolutional networks, from LeNet to AlexNet.

Regularization by Early Stopping

Explores regularization by early stopping in deep neural networks to control flexibility and avoid overfitting.

Neural Network Activity: Modeling and Analysis

Explores neural network modeling, sensitivity analysis, and replicating experimental conditions to understand brain activity.

Introduction to the simulation of the microcircuit

Introduces the simulation of a hippocampal microcircuit and its key aspects.

Stochastic Hopfield model

Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.

Neural Networks: Hierarchical Models and Odor Taxis

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Learning of Associations

Delves into associative memory, Hebbian learning, and hierarchical organization in neural networks.

Reconstructing Brain Regions: Challenges and Strategies

Explores challenges in brain region modeling using atlases and strategies to compensate for missing data and assumptions.

Relationship to Neural Learning Rules

Explores the relationship between neural learning rules and weight updates based on rewards and neuron activity.