Lectures related to Attractor Networks and Spiking Neurons

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.

Scientific Computing in Neuroscience

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

Neural Networks: Hierarchical Models and Odor Taxis

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

Generalized Integrate-and-Fire Models

Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

AdEx model: Firing patterns and phase plane analysis

Explores the AdEx neuron model, analyzing firing patterns and phase planes.

Modeling Neuronal Activity

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

Building Physical Neural Networks

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

Network Simulation and Activity Dynamics

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Introduction to Systems Neuroscience: Memory Systems Overview

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

Modeling Electrophysiology: Different Scales

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

Attractor Networks and Generalizations

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

Neural Networks: Multilayer Perceptrons

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

In Silico Neuroscience: Network Simulation

Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.

Neuronal Dynamics: Random Networks

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

Computational Neuroscience: Biophysics & Modeling

Covers the fundamentals of computational neuroscience, focusing on biophysics and modeling.

Parameter estimation

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Networks of Spiking Neurons

Explores networks of spiking neurons, mean-field models, transfer functions, and predictions of state space and neuron behavior.

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.

Stationary Mean-Field: Neuronal Dynamics

Explores the stationary mean-field concept in computational neuroscience to predict neuronal activity based on population and single neuron firing rates.