codeweek

Aspects od Neuroscience conference satellite event

18-19th November 2017 @ University of Warsaw

The call for proposals for the AoN Brainhack Warsaw 2017 is now open!

On the weekend of 18-19th November 2017, the first edition of AoN Brainhack Warsaw will take place. During this two-day event dedicated to students and PhD students, we will work in teams on neuroscience-related projects.

The aim of the event is to meet new, enthusiastic researchers, make new friendships in academia, learn, share the knowledge on data mining and brain research, but also promote open science in the spirit of the whole Brainhack community (Craddock et al., 2016). Attendees of various backgrounds are welcome to join!

By submitting your own, genuine research project, you can gain a priceless leadership experience as you will manage a group of researchers at our two-day event.

Please note AoN Brainhack Warsaw 2017 is a satellite event for the interdisciplinary Aspects of Neuroscience conference which will take place on 24-26th November 2017 in Warsaw. Participation in the conference is not mandatory to take part in Brainhack but we encourage to also consider this.

Deadline for project proposals: 01.09.2017

Announcement of projects: 15.09.2017

Deadline for participant registration: 01.11.2017

Please send the project proposals and all the related questions at the mailing address: brainhackwarsaw@gmail.com

Also, please team up with us! Join our channel (#brainhack-warsaw-2017) on Brainhack Slack for updated information on the developing Hackathon content and to contribute your own ideas.

Venue

Brainhack will take place at the University of Warsaw, Faculty of Physics, Pasteura 5, 02-093 Warsaw, Poland. Additional information how to get to Warsaw and university campus can be found at the AoN website.

Projects

At the moment, the call for project proposals is open. The proposals can include various forms of a group activity, e.g., data analysis, app development, a discussion club on a particular topic, developing a software etc. The organizers will provide a few openly available datasets to the project authors, but developing projects using private datasets is also welcome.

A project proposal should contain the following points:

  1. a title,
  2. a list of authors / mentors / supervisors,
  3. an abstract,
  4. a list of 1-5 key papers / online materials summarising the subject (which the participants should become familiar with before starting the project),
  5. a list of requirements for taking part in the project (education level / English level / programming language required),
  6. a maximal number of participants on the project,
  7. what participants gain / learn from the project?,
  8. can the project be extended to a peer-reviewed paper if the results are promising?

Two exemplary projects which will take place at this event, are listed below:


Project 1: Functional connectivity research: can we find a common ground?

Author: Natalia Bielczyk, MSc

Senior supervisor: Michał Bola, PhD

Abstract: Functional connectivity (FC) research has become one of the leading concepts used for characterising network dynamics across multiple disciplines, from neuroimaging, through gene expression networks, to social networks. It is also a basis for graph theoretical biomarkers of psychiatric disorders and as such, it become an important subfield of cognitive neuroimaging.

FC is usually operationalised by means of Pearson’s and partial correlation, however the implementation of FC can vary between different fields, and different applications. Then, there is a question: does an optimal method to quantify functional connectivity exist? Or is the choice dependent on the data properties? How to choose the right method? In this project, we will use open-access data from functional Magnetic Resonance Imaging, EEG, gene expression data, stock exchange data and a few other open-access datasets, and we will compare the leading methods for computing FC when applied to these datasets.

We will attempt to answer the questions: what are the pros and cons of different methods for quantifying FC? What are the differences and the similarities between different datasets, and how to choose the right method for the given dataset?

image-title-here Fig: different types of networks. A: a social network (Facebook); B: correlations on the stock exchange (106 companies listed at NASDAQ-100); C: a gene co-expression network (image adapted from http://wikipedia.org on CC BY-SA 3.0 license); D: large scale resting state networks in the brain (image adapted from Smith et al, 2009)

A list of 1-5 key papers/materials summarising the subject:

  1. http://www.scholarpedia.org/article/Brain_connectivity
  2. http://journal.frontiersin.org/article/10.3389/fnsys.2015.00175/full
  3. A. K. Enge, C. Gerloff,C. C. Hilgetag and G. Nolte (2013). Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity. Neuron 80 (4): 867–86
  4. M. Bola and V. Borchardt (2016). Cognitive Processing Involves Dynamic Reorganization of the Whole-Brain Network’s Functional Community Structure. Journal of Neuroscience 36 (13): 3633–5

A list of requirements for taking part in the project (education level / English level / programming language required):

  1. BSc program, or higher
  2. English: good, not necessarily proficient
  3. programming languages / other competences: basics of Matlab, Python, LaTeX, basic statistics

A maximal number of participants: 8

Skills and competences you can learn during the project:

  1. looking for parallels in the datasets from different disciplines, representing the datasets with a model
  2. group project planning (we will discuss and divide tasks on the site)
  3. programming in a team, solving problems in parallel
  4. scientific writing (at least one paragraph per participant)

Is there a plan for extending this work to a paper in case the results are promising? Yes


Project 2: Detecting trypophobia triggers

Author: Piotr Migdał, PhD

Abstract:Trypophobia is a phobia of irregular patterns or clusters of small holes or bumps. It may arise from the sense of aversion towards skin infection with maggots or fungi. In general, this phenomenon is adaptive, because a strong sense of disgust may protect against touching infected humans, animals or corpses. Yet, it can also become maladaptive if some, otherwise benign, patterns cause a strong aversive response. During this workshop we will create an artificial convolutional neural network that predicts if an image is likely to cause a trypophobic response. Such networks are a state of the art technique for visual pattern detection.

The goal of the project is twofold:

  1. provide a tool to filter or censor triggering images while browsing the Internet
  2. empirically explore which patterns contribute to this phenomenon, and potentially relate the results to analogous regions in the human visual cortex

We will provide the data for this project. The initial results are promising, see this git repo.

image-title-here Fig: The holes in lotus seed heads cause some anxiety in some people (source: Wikipedia)

A list of 1-5 key papers/materials summarising the subject:

In this case, only the basic knowledge of trypophobia is required. An additional knowledge may help with giving a general context, but most likely won’t contribute to the solution during this event:

  1. https://en.wikipedia.org/wiki/Trypophobia
  2. https://www.reddit.com/r/trypophobia/ (warning: triggers)

Additionally, take a look at http://p.migdal.pl/2017/04/30/teaching-deep-learning.html. If you are new to Python, this book may be relevant.

A list of requirements for taking part in the project (education level / English level / programming language required):

  1. BSc program, or higher
  2. English: good, not necessarily proficient
  3. programming languages / other competences: at least basics of Python (we will create a neural network in either Keras or PyTorch, modern frameworks for deep learning)

A maximal number of participants: 6 (1-2 per computer)

Skills and competences you can learn during the project:

  1. practical experience with deep learning for image classification
  2. insights into how artificial neural networks abstract visual information processing

Is there a plan for extending this work to a paper in case the results are promising? Yes

Registration

Please send the project proposals before 1st September 2017 to the mailing address: brainhackwarsaw@gmail.com

Registration for project participants will start in September and it will last until 1st November 2017 .

During registration, there will be small registration fee for the project participants (to cover the catering during the event, not more than 20€)

Commitee

The AoN Brainhack Warsaw 2017 committee: