2024 Summer schools taught by Mike X Cohen
Rhythmic activity such as oscillations and synchronization are widespread in neural time series data, and are thought to have important roles in brain function, including providing temporal structure to shape information-processing, dynamically routing information processing, and synchronizing dynamics over multiple spatial and temporal scales.
Detailed theories are important for understanding the role of rhythmic activity in the brain, but appropriate data analyses are absolutely essential. Unfortunately, there is often a gap between scientists' ideas about how to analyze their data, and their knowledge of the mathematical and practical steps to analyze the data in order to test those ideas.
The purpose of this summer school course is to provide a firm grounding for understanding advanced neural time series (LFP/EEG/MEG) analyses, with a strong focus on time-frequency and synchronization analyses.
By the end of the Analyzing Neural Time Series course you will be able to
- Understand the mechanics of the Fourier transform and how to implement it in MATLAB.
- Use complex wavelet convolution to extract time-frequency information from time series data.
- Simulate data to test the accuracy of data analysis methods and effects of parameters. Implement non-parametric statistics to evaluate statistical significance while correcting for multiple comparisons.
- Use AI tools like ChatGPT to learn, apply, and communicate science.
To learn more, check out last year's syllabus.
This course is designed for PhD students, postdocs, and senior researchers who have experience with data analysis and want a deeper understanding of advanced data analysis methods. Some experience with MATLAB is necessary. The course focuses on analog electrophysiology signals (LFP/EEG/MEG).
This material has been taught by Mike X Cohen for over a decade in several different countries, and is the basis of the book Analyzing Neural Time Series Data (MIT Press, 2014).
Due to popular demand, the ANTS course is offered twice: 29 July - 2 August 2024, and 12-16 August 2024. The content is the same in both weeks, but the second session is recommended if you will also join the LAN course.
Practical and application information appear towards the bottom of this site.
Neuroscience is moving towards "big data," with new and improved brain measurement technologies that acquire an ever-increasing amount of data. Increases in the number of simultaneously recorded data allows new discoveries about spatiotemporal structure in the brain, but also presents new challenges for data analyses. Because data are stored in matrices, algorithms developed in matrix analysis will be extremely useful. On the other hand, linear algebra and matrix analysis are unfortunately rarely taught in neuroscience/biology/psychology courses.
The purpose of this course is to introduce you to matrix-based data analysis methods in neural time series data, with a focus on least-squares model fitting, multivariate dimensionality-reduction, and source-separation methods. The focus is on understanding methods and their implementation in MATLAB, rather than on using analysis toolboxes.
By the end of the Linear Algebra for Neuroscientists course you will be able to
- Understand the key concepts in linear algebra including matrix multiplication, inverse, and projections.
- Understand geometric and algebraic ways of representing data and analyses.
- Implement the least-squares algorithm to compute general linear models for quantifying brain-behavior relationships and functional connectivity.
- Understand eigendecomposition and its use in dimension reduction and source separation.
- Simulate multivariate data to evaluate analysis methods and model overfitting.
- Use AI tools like ChatGPT to learn, apply, and communicate neural data science.
To learn more, check out last year's syllabus.
This course is offered on 19-23 August 2024.
Many people come for ANTS2 and LAN to get a full educational (and social!) experience in neural time series analysis.
This course is designed for PhD students, postdocs, and senior researchers who have experience with data analysis and want a deeper understanding of the underpinnings and implementations of multivariate data analysis methods. Some experience with MATLAB is necessary.
This material has been taught by Mike X Cohen for several years as workshops and week-long courses, and is the basis of the book Linear Algebra: Theory, Intuition, Code (Sincxpress, 2020), and several online courses.
COURSE STRUCTURE AND ENVIRONMENT
I have been teaching and refining my courses since 2008. My courses involve a mix of in-person lectures, pre-recorded video lectures, group work, projects, and one-on-one interactions. The assignments are given at multiple levels of difficulty to accomodate learners with different backgrounds and coding expertise.
The courses take place in a conference room with comfortable seating for 60 people, although we will have maximum 30. Break-out group work takes place in one of several smaller rooms, each of which is equipped with wifi, power, large smart TVs with HDMI connections, and unlimited coffee.
Please note that these courses are intense and immersive; nonetheless, we try to make it as fun and engaging as possible.
LEVEL AND PREREQUISITES
These courses are mathematically rigorous but approachable to researchers with no formal mathematics background. If you want to analyse your neuroscience data completely on your own, this course will certainly help get you started. It will also provide a firm basis for using analysis toolboxes such as eeglab or fieldtrip, although the course does not provide instructions for how to use these toolboxes.
Hotspot Coworking; downtown Bucharest, Romania (close to the main train station, which itself is 45 minutes from OTP international airport).
Hotspot Coworking is a perfect space for this workshop, including rooms for lectures and break-out group work, unlimited coffee/tea/snacks, and many restaurants, hotels, and nightlife in walking distance (location on map).
Bucharest is a vibrant and dynamic city, with a rich history and myriad options for cafes, restuarants, nightlife, museums, parks, etc. It is the capital of Romania and is easily accessible via OTP international airport. Romania is a member state of the European Union, and it is your responsibility to determine whether you need an entry visa. Additional information regarding hotels, currency, and local transport, will be made available upon course acceptance.
ABOUT THE INSTRUCTOR
Dr. Mike X Cohen (that's me!) has been teaching time series analysis, applied mathematics, and scientific programming for over 20 years. He has published several textbooks on these topics and teaches a variety of "real-life" and online courses. He lives in Bucharest, has a questionable sense of humor, and enjoys long walks on the beach.
The course fee includes registration; course materials; wifi; lifetime access to the accompanying online course; lifetime access to the dedicated Discord server; unlimited coffee/tea/water and healthy snacks (and yes, chocolate is considered healthy!); welcome reception on Sunday (the evening before the course starts); closing ceremony on Friday; several meals and evening social activities; and VAT.
Each week-long course costs 800 Euros. A discount is offered for those attending both ANTS2 and LAN: 1500 Euros for both weeks instead of 1600.
The course fee does not include flights, airport transportation, hotel, all meals (though daily snacks and several dinners are provided), access to computers, or MATLAB licenses. You will need to bring your own laptop with MATLAB installed.
During the course, you are free to use all facilities available at the coworking space. If you wish to use the coworking facilities before or after the course, please contact Hotspot Coworking directly.
Registration fees are paid by SEPA/wire transfer in EUR. Cash, checks, or credit cards are not accepted. Payment must be received within two months after acceptance, or one month before the start of the course. More details will be provided upon acceptance to the course. Reduced fees (except the discount for attending both courses) and travel grants are not available.
Upon completion of the course, you will receive a signed certificate of completion. This certificate also serves as proof of attendence.
The application is an online form. Please submit the form only once.
You will receive an email confirmation of receipt of the application within a week. Acceptance or rejection is made within approximately two months of submission. Applications will remain open until the courses are full (30 students per week), or one month prior to the start of the course. If your application is accepted but the course is full, you will be placed on a waitlist and given priority for the next available spot.
Previous experience (beginner to moderate level) with MATLAB programming is required. A strong background in mathematics is not required. The most important requirement is a positive and optimistic attitude!
Please prepare the following responses for the application:
- Describe your previous experience with analyzing M/EEG/LFP/spikes or any other kind of data (approximately 1000 characters).
- Describe your previous experience with MATLAB, and with analysis packages (eeglab, fieldtrip, BVA, SPM, FSL, etc.) (approximately 1000 characters).
- Describe your motivation for taking this course. You should include a statement "By the end of this course I want to..." (approximately 1500 characters)
Please note: Applications that are too short to be deemed serious are rejected.
LINK TO APPLICATION
You can find the application here. Please submit the form only once. By submitting an application, you agree to pay the registration fee and attend the workshop in person if your application is accepted.