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Module 4: fMRI

Mike X. Cohen

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

 
INCF TrainingSpace

Biochemical Models

INCF

This course consists of introductory lectures on different aspects of biochemical models. By following this course, you will learn about the various forms plasticity can take at different levels in the brain, how to model chemical computation in the brain, as well as computationally demanding studies of synaptic plasticity on the molecular level. 

 
INCF TrainingSpace

Introduction to EEGLAB

Swartz Center for Computational Neuroscience

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data. In this course, you will learn about features incorporated into EEGLAB, including independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS X.

 

Bayesian Models of Learning and Integration of Neuroimaging Data

Krembil Centre for Neuroinformatics

Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to the fields of neuroscience and neuroinformatics. In this course, participants are taught how Bayesian statistics may be used to build cognitive models of processes like learning or perception. This course also offers theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data.

 

Module 1: Spikes

Mike X. Cohen

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.

 

INCF Assembly 2022 - Day 3 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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Module 3: Computational Models

Mike X. Cohen

This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

 
INCF TrainingSpace

Basic Mathematics for Computational Neuroscience

Alex Williams

A series of short explanations of the basic equations underlying computational neuroscience.

 

Population-Based Data Resources & Integrative Research Methods

Krembil Centre for Neuroinformatics

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

 

INCF Assembly 2022 - Day 2 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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Programming

A number of programming languages are ubiquitous in modern neuroscience and are key to the competence, freedom, and creativity necessary in neuroscience research. This course offers lectures on the fundamentals of data science and specific neuroinformatic tools used in the investigation of brain data. Attendees of this course will be learn about the programming languages Python, R, and MATLAB, as well as their associated packages and software environments. 

 
INCF TrainingSpace

Standards and Best Practices

INCF

Standards and best practices make neuroscience a data-centric discipline and are key for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. This study track provides an introduction to standards and best practices that support the FAIR Principles.

 

INCF Assembly 2022 - Day 2 Sessions

INCF

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

VIEW THE PROGRAM

 

The Virtual Brain: Bernstein Center Lectures 2019

The Virtual Brain

This course provides a general overview about brain simulation, including its fundamentals as well as clinical applications in populations with stroke, neurodegeneration, epilepsy, and brain tumors. This course also introduces the mathematical framework of multi-scale brain modeling and its analysis.

 

Cellular Mechanisms of Brain Function

EPFL

This course, arranged by EPFL and also available as a MOOC on edX, aims for a mechanistic description of mammalian brain function at the level of individual nerve cells and their synaptic interactions.

 
INCF TrainingSpace

Foundations of Neurotechnology

The BCI Guys

This course provides a broad, non-technical overview of the field of neurotechnology. It is intended to provide users with a fundamental understanding of how neurotechnology works.

 

INCF Short Course: Introduction to Neuroinformatics

INCF

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

 

Hypothes.is: A Web-Based Annotation Tool

Hypothes.is

This short course covers Hypothes.is, an annotation tool that enables users to collaboratively annotate course readings and other internet resources.

Features of Hypothes.is:

 

INCF Assembly 2022 - Training Day 2

INCF

This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.

 
INCF TrainingSpace

Deep Learning: Parameters Sharing

NYU Center for Data Science

This course covers the concepts of recurrent and convolutional nets (theory and practice), natural signals properties and the convolution, and recurrent neural networks (vanilla and gated, LSTM).