Lectures related to Neuronal Dynamics: Solution Types

Spatial Continuum: Field Equations MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores spatial continuum models, field equations, and coupling mechanisms in computational neuroscience.

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.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Decisions, actions, volition MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores decision models in computational neuroscience, focusing on competitive dynamics, perceptual decision making, and the problem of free will.

Modeling Electrophysiology: Different Scales MOOC: Neuroscience Reconstructed: Genetics and Brain Development

Covers modeling electrophysiology at different scales, discussing ion channels, single neurons, and microcircuits.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Neuronal Dynamics: Random Networks MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the dynamics of neuronal populations, emphasizing random networks and mean-field arguments for connectivity.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Understanding Neuronal Morphologies: NeuroM Basics MOOC: Simulation Neurocience

Covers NeuroM basics, including checking neuron quality, extracting morphometrics, and visualizing neurons in different formats.

Understanding the Hippocampus: Structure and Function

Explores the importance of the hippocampus in memory and spatial navigation, discussing its unique structure and implications for broader brain research.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Neural Networks: Two-layer Networks and Backpropagation

Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Ring Attractor Dynamics in Drosophila Brain

Explores ring attractor networks in the Drosophila brain for maintaining heading direction during navigation.

Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Modeling Neuronal Activity

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

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