Lectures related to Neuronal Dynamics: Solution Types

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.

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.

Neural Networks: Hierarchical Models and Odor Taxis

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

Neuroscience and AI: Bridging the Gap

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

Decisions, actions, volition

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

Neuroscience and Machine Learning: Understanding Visual Intelligence

Explores the relationship between neuroscience and machine learning in visual intelligence.

Modeling Electrophysiology: Different Scales

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

Embodied Behavioral Control: Animals and Robots

Covers controlling behavior in animals and robots through oral presentations, computational exercises, and a mini-project for hands-on neuroscientific discovery.

Introduction to Systems Neuroscience: Memory Systems Overview

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

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

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

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Neural Networks: Training and Activation

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

Understanding Neuronal Morphologies: NeuroM Basics

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

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.

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: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.