Satellite 6: Neural Signal and Image Processing: Quantitative Analysis of Neural Activity

Dates: May 20 & 21, 2019.

Location: SickKids building (686 Bay St), Toronto

Organizers:

Majid Mohajerani | University of Lethbridge

Artur Luczak | University of Lethbridge

Steve Prescott | University of Toronto

Description: Given the exponentially growing size and complexity of experimental data, advanced data analyses methods are proving to be indispensable for neuroscience research. In this workshop we will overview different analysis methods used in variety of neuroscience fields to help to understand complex brain signals.

The target audience of the workshop will be graduate students, postdoctoral researchers and principal investigators in neuroscience and psychology with interest in data analysis.  The course will combine lectures and hands-on tutorials using MATLAB. Participants will perform the computer exercises using data sets and analysis software on their own laptop computers.

Scope: This two-day course will provide a survey of diverse topics, including methods for analyzing single and multiple spike trains, local field potential, optical imaging data (single-cell level vs whole brain imaging), EEG/MEG recordings, and fMRI data.

Based on feedback from students attending our CAN satellite workshops in the last 2 years, we have expanded it to 2 days to cover more data analysis topics and to have more time for in-class Matlab exercises.

Estimated attendance: 30-50 participants. (Maximum of 60)

Ticket price:  95 CAD

Preliminary Schedule

Day 1 (Mon. May 20)

Opening remarks

  1. Analyses of EEG Signals (Kyle E. Mathewson, University of Alberta)
  2. Analyses of Neurons Population Data (Artur Luczak, University of
    Lethbridge)
  3. Place Fields and Head Direction Cells Analyses (TBD)
  4. Quantitative Analysis Toolbox for Characterization of Spatiotemporal
    Dynamics in Mesoscale Optical Imaging of Brain Activity (Majid Mohajerani, University of Lethbridge)
  5. Analysis of Functional Magnetic Resonance Imaging Data: Principles and Techniques, Part 1 (TBD)
  6. Analysis of Functional Magnetic Resonance Imaging Data: Principles and Techniques, Part 2 (TBD)
  7. Deep Learning for Neuronal and Behavioral Data Analyses Part 1 (Artur Luczak, University of Lethbridge)
  8. Deep Learning for Neuronal and Behavioral Data Analyses Part 2 (Blake Richards, University of Toronto )
  9. DeepEEG demo ( Kyle E. Mathewson, University of Alberta)

Day 2 (Tue. May 21)

  1. Quantitative Tools to Analyze Cellular Based Calcium Imaging Data (Andrea Giovannucci, University of North Carolina)
  2. High-Throughput and Open Source Approaches to Histology and Analysis (Jon Epp, University of Calgary)
  3. Graph theory and measures of brain connectivity (Bratislav Misic, McGill University)
  4. Estimating model parameters from experimental data (Milad Lankarany, Krembil Research Institute/UHN, Toronto)
  5. Analyzing intracellular signals (Steve Prescott, University of Toronto)
  6. Use Of ‘Virtual Brain’ for Modeling and Simulation (Kelly Shen, Rotman Research Institute, University of Toronto).
  7. Multivariate Analyses Part 1 (Mark Reimers, Michigan State University)
  8. Multivariate Analyses Part 2 (Mark Reimers, Michigan State University)
  9. Open Discussion About Data Analysis Methods (All Instructors and Students)

To register:

CAN meeting and satellite meeting registration

Satellite meeting registration only