Skip to content

IGMN NeuroComputation Workshop

  • Objective: to provide an overview of computational methodologies useful for various types of neuroscience data, plus opportunities to “practice” with example data sets. The “practice” sessions will include basic instruction on using R, python, and matlab; data management, data transfer, and data cleaning concepts; and practice using various data analysis/visualization programs.
  • Dates: presented each year during the summer
  • Schedule: Previous workshop was Aug 6-14, 2018
  • Requirement for Graduate Minor in Neuroscience: Students who attend and engage in all the workshop presentations will receive a “Certificate of Completion”, which will verify that they have met this requirement for the Graduate Minor in Neuroscience.

Workshop Overview

Omics – transcriptomics, proteomics, metabolomics Lead Instructor: Dan Jacobson

  1. Omics and you
  2. What and why is GWAS
  3. Machine Learning in Omics
  4. Application to Plants and Neuroscience
  5. TBD

Computing Facility and Operating System Instructors: Bobby Whitten and Lonnie Crosby

  1. Overview of the Advanced Computing Facility (ACF)
  2. Introduction to Unix
  3. File systems/data transfer

EEG Analysis Methods for Brain Computer Interface Instructor: Xiaopeng Zhao

  1. Overview on Brain Imaging Techniques
    1. Introduction to common brain imaging techniques
  1. EEG Monitoring and Analysis
    1. Basic Principles
    2. Recording Techniques
    3. Applications
  1. Brain Computer Interface
    1. Concepts and Applications
    2. Sensorimotor Rhythm
    3. Imagined Body Kinematics
    4. SSVEP
    5. ERP
  1. EEG Analysis Methods for BCI
    1. Signal Processing
    2. Spatial Filtering
    3. Time-frequency analysis

Analyzing Microscale Neural Activity for Clinical Applications Instructor: Sara Hanrahan

  1. Introduction
    1. Acquiring microscale neural signals
    2. Basic analysis methods for microscale neural signals
      1. Filtering neural signals
      2. Sorting spikes to identify individual neurons
    3. Behavioral event-related neural analysis methods
      1. Characterize neural encoding: raster plots and peri-event time histograms
      2. Decoding neural activity: local field potential spectrograms
  1. Applications of neurocomputation: I. Advancing functional neurosurgery
    1. Intraoperative mapping of neural signals to identify functioning and diseased tissue
    2. Awake Deep Brain Stimulation Surgery
      1. Mechanisms of Deep Brain Stimulation in treatment of Parkinson’s disease
      2. Locating deep brain nuclei in real-time using neural activity
      3. Developing adaptive Deep Brain Stimulation systems with neurocomputation
  1. Applications of neurocomputation: II. Elucidating anesthesia mechanisms
    1. Determining the effect of Propofol on microscale neural activity in intraoperative patients

Measuring Hemodynamics Instructor: Aaron Buss

  1. Introduction

1.1, Vascular vs. electrical systems in the brain

1.2, Measuring blood flow with lasers and magnets

1.3, Levels of models

  1. Cognitive Neuroscience

2.1, What kind of questions can we ask?

2.1.1, Functional activation

2.1.2, Network structure/connectivity

2.2, Analyses

2.2.1, Spiking

2.2.2, Rate based

2.3, Interoperability

  1. fMRI

3.1, Physics (briefly)

3.2, Pre-processing

3.2.1, Skull-stripping

3.2.2, Spatial registration

3.2.3, Slice timing correction

3.2.4, Spatial smoothing

3.2.5, Deconvolution

3.2.6, Registration to common spatial frame of reference

3.3, Group-level analyses

3.3.1, ANOVA for task-based analyses & contrast method, Family-wise error-correction

3.3.2, PCA et al. for network analyses

3.4, Structural MR

3.4.1, White matter volume

3.4.2, Cortical thickness

3.4.3, Diffusion tensor imaging

3.5, Software

3.5.1, Afni

3.5.2, SPM

3.5.3, FSL

  1. fNIRS

4.1, Physics (briefly)

4.2, Pre-processing

4.2.1, Temporal filtering

4.2.2, Conversion to optical density

4.2.3, Motion correction/rejection

4.2.4, Accounting for systemic blood-flow (PCA or short-channel)

4.2.5, Deconvolution or block average

4.3, Group-level analyses

4.3.1, ANOVA

4.3.2, Connectivity

  1. Model-based neuroimaging

5.1, Dynamic field theory

5.2, Neural basis of the blood-flow responses

5.3, Simulating hemodynamic resposnes

5.4, Extracting cognitively-functional networks

Neural Modeling Instructor: Bruce MacLennan

  1. Introduction

1.1, Goals of modeling

1.2, What is relevant and what is not?

1.3, Levels of models

  1. Levels of models

2.1, Single neuron

2.1.1, Microphysiological

2.1.2, Compartmental

2.2, Network

2.2.1, Spiking

2.2.2, Rate based

2.3, Interoperability

  1. Available software (tentative)

3.1, Microphysiological

3.2, Compartmental

3.2.1, Brain Dynamics Toolbox

3.2.2, Brain Modeling Toolkit (BMTK)

3.2.3, GENESIS & PGENESIS – GEneral NEural SImulation System (v2.4)

3.2.4, NEURON

3.2.5, Blue Brain

3.3, Network

3.3.1, Spiking, Brian, Nengo, NEST, CSIM (& PCSIM) – neural microCircuit SIMulator

3.3.2, Rate based, emergent

3.3.3, SPAUN

3.3.4, Topographica

3.4, Multiscale

3.4.1, MOOSE – Multiscale Object-Oriented Simulation Environment (v3.0.1 “Gulab Jamun”)

3.5, Interoperability

3.5.1, PyNN

3.5.2, NeuroML

3.6, Modeling support

3.7, Internet resources

3.7.1, Perlewitz Neuroscience software (

  1. Demonstrations (tentative)

4.1, Emergent

4.1.1, Single neuron

4.1.2, Networks

4.2, MyFirstNEURON

4.3, SPAUN videos

The flagship campus of the University of Tennessee System and partner in the Tennessee Transfer Pathway.