Lectures related to MathDetour 1: Separation of time scales

Scientific Computing in Neuroscience

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

Generalized Integrate-and-Fire Models

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

Computational Neuroscience: Biophysics & Modeling

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

Introduction to Systems Neuroscience: Memory Systems Overview

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

AdEx model: Firing patterns and phase plane analysis

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

Numerical Analysis: Stability in ODEs

Covers the stability analysis of ODEs using numerical methods and discusses stability conditions.

Parameter estimation

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

Attractor Networks and Spiking Neurons

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

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.

Neuronal Dynamics: Random Networks

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

Neural Networks: Hierarchical Models and Odor Taxis

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

Three definitions of rate code

Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.

Modeling Electrophysiology: Different Scales

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

Modeling in vitro data

Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.

Ordinary Differential Equations: Non-linear Analysis

Covers non-linear ordinary differential equations, including separation, Cauchy problems, and stability conditions.

Scientific Computing in Neuroscience

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Brain-Computer Interfaces: Advancements in Systems Neuroscience

Covers brain-computer interfaces and their impact on systems neuroscience and neuroprosthetics.

Dynamical modeling, decoding, and control of multiscale brain network activity

Explores dynamical modeling, decoding, and control of brain network activity for personalized therapy.

Models and data

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

Biophysical Understanding of Neuronal Behavior

Explores the biophysical understanding of neuronal behavior, focusing on action potentials, neuron modeling challenges, and dendritic inhibition.