Trialect

Active

Computational and Clinical Neuroscience

New York University

Main Areas of Interest

Lecturing and Teaching:

This Traineeship is designed for undergraduate or graduate students or medical students (with different background: mathematics, physics, computer science, engineering, neuroscience and biology) who look for research training in computational and clinical neuroscience. Lectures or teaching will be given by Zoom/Skype one-to-one meetings.

Techniques You Will Learn

Learning and Teaching Resources (web links, YouTube links, etc.):

http://www.scholarpedia.org/article/State_space_model
https://www.hindawi.com/journals/cin/2013/251905/
http://www.cn3lab.org/publications.html

Modules

Module 1: Background review and reading (30 min)

This course aims to prepare students with basic knowledge, such as probability, statistics, linear algebra,spectrum analysis, basic neuroscience, machine learning etc. The exact topics of focus depend on the need of the trainee. Additional homework reading and tutorial information will be provided. MATLAB or Python programming experiences are required.

Module 2: Statistical analysis for neural data (Part I, 30-40 min)

In this course, we will learn some basic tools for analyzing various types of neural data. We will cover basic concepts and provide software, tutorial information, and representative examples in practice.

Module 3: Statistical analysis for neural data (Part II, 30-40 min)

In this course, we will cover a few advanced topics in neural data analysis. We will provide software,tutorial information and walk-through examples.

Module 4: Introduction on neuroengineering and neural signal processing (30-40 min)

In this course, we will introduce the concept of brain-machine interface, and important topics of neural signal processing, including signal detection and neural decoding. Walk-through examples will be given. Additional homework reading materials will be provided.

Module 5: Introduction on computational modeling for behaviors (30-40 min)

In this course, we will introduce the concept of computational modeling based on abstraction. We will focus on behavioral modeling such as decision making and temporal difference reinforcement learning.How to use these models for characterizing abnormal behaviors (e.g., addiction) from diseased brains will be discussed.

Module 6: Application of machine learning for clinical neuroscience research (30-40 min)

In this course, we will learn from examples of how to apply machine learning to translational or clinical neuroscience research. Examples include human EEG and fMRI data analysis in pain and epilepsy research. Open source software will be provided.

Program Fee


Total Number of Modules
Time duration of Each module	minutes
Total Program Cost	$0

Who Can Apply

Host Details

Host Name: Zhe Sage Chen

Affiliation: New York University

Address: 27 West; Fourth Street; New York; NY 10003;United States

Website URL: https://www.nyu.edu/

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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