Lessons

Clear search and reset the filters
Extracellular spiking
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 5:17
Speaker: : Mike X. Cohen
Import data and convert spikes to data matrix
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 11:37
Speaker: : Mike X. Cohen
Creating spike count histograms
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 5:31
Speaker: : Mike X. Cohen
Tuning curves
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 13:48
Speaker: : Mike X. Cohen
Spatial map of spike count
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 12:16
Speaker: : Mike X. Cohen
Spatial map of tuning
Course:
Module 1: Spikes

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

Difficulty level: Intermediate
Duration: 13:11
Speaker: : Mike X. Cohen
Introduction to computational models
Course:
Module 3: Computational Models

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

Difficulty level: Intermediate
Duration: 8:21
Speaker: : Mike X. Cohen
Build the AdEx model
Course:
Module 3: Computational Models

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

Difficulty level: Intermediate
Duration: 22:01
Speaker: : Mike X. Cohen
Embed the simulation in a function
Course:
Module 3: Computational Models

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

Difficulty level: Intermediate
Duration: 11:20
Speaker: : Mike X. Cohen
Generate an F/I curve
Course:
Module 3: Computational Models

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and FI curves. The MATLAB code introduces Live Scripts and functions.

Difficulty level: Intermediate
Duration: 20:39
Speaker: : Mike X. Cohen
Single neuron models - I
Course:
Dynamical neural systems

Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:26:06
Speaker: : Bard Ermentrout
Single Neuron Models - II
Course:
Dynamical neural systems

Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:25:38
Speaker: : Bard Ermentrout
Neural oscillations, weak coupling and networks
Course:
Dynamical neural systems

Oscillations and bursting

Difficulty level: Intermediate
Duration: 1:24:30
Speaker: : Bard Ermentrout
Neural oscillations and networks II
Course:
Dynamical neural systems

Oscillations and bursting

Difficulty level: Intermediate
Duration: 1:31:57
Speaker: : Bard Ermentrout
Oscillatory networks and the Ott Antonsen theory
Course:
Dynamical neural systems

Weakly coupled oscillators

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Bard Ermentrout
Oscillatory networks II
Course:
Dynamical neural systems

Continuation of coupled oscillators

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : Bard Ermentrout
Firing rate models - networks
Course:
Dynamical neural systems

Firing rate models.

Difficulty level: Intermediate
Duration: 1:26:42
Speaker: : Bard Ermentrout
Spatiotemporal dynamics, waves, pattern formation
Course:
Dynamical neural systems

Pattern generation in visual system hallucinations.

Difficulty level: Intermediate
Duration: 1:20:42
Speaker: : Bard Ermentrout
Single neuron models - I
Course:
Introduction to computational neuroscience

Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:26:06
Speaker: : Bard Ermentrout
Single Neuron Models - II
Course:
Introduction to computational neuroscience

Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:25:38
Speaker: : Bard Ermentrout

Topics