Answering the next generation of open questions in neuroscience will require very large data sets and complex analytical methods. The purpose of Brainhack is to bridge the data science and neuroscience research communities to advance the progress of brain science research. Brainhack is a unique conference that convenes researchers from across the globe and myriad disciplines to work together on innovative projects related to neuroscience. Year after year, global Brainhack events have brought together researchers to participate in open collaboration and regional Brainhack events keep the momentum going throughout the year.
These collaborative workshops combine elements of Hackathons and Unconferences, with a variety of educational activities, to accelerate the adaptation of data science and computational methods in Neuroscience. Much of the conference is allocated to open working time during which attendees are encouraged to work together in interdisciplinary teams on projects that utilize computational techniques to solve problems in neuroscience. Periodic unconference sessions provide an opportunity for attendees to share their expertise on topics that become relevant through the course of the event. In parallel to these activities, an educational track provide hands-on tutorials on relevant tools such as python, github, cloud computing, and innovative statistical methods.
Local events:¶
Neurohackademy¶
Neurohackademy is a summer school in neuroimaging and data science, held at the University of Washington eScience Institute.
Participants will learn about technologies used to analyze human neuroscience data and to make analysis and results shareable and reproducible.
The first week is devoted to hands-on lectures and open Q&A discussions. The second week of the course is devoted to participant-directed activities: breakout sessions and collaboration sessions on topics of interest.
Neuromatch Academy¶
We provide research education and training in computational neuroscience that is crucial for success in both academia and industry. We do this through live courses, interactive projects, professional development, networking, and research experiences in an inclusive, supportive, all-online, environment.
In the academy, we help participants to collaborate with a global community of experts and peers, hone their problem-solving skills, and build a supportive network that celebrates inclusivity and accessibility in science education.
Neuromatch Academy is a volunteer-led organization, run by computational neuroscientist enthusiasts from all over the world. From students to faculty to industry professionals, our volunteers are invested in creating globally-accessible science education and building inclusive communities for scientists to learn, grow, network, and discover
Coursed include:
Computational Neuroscience:¶
Learn computational neuroscience techniques and methods in a live instruction hands-on classroom.
We cover cutting-edge advances in machine learning and causality research with state-of-the-art modeling approaches in neuroscience with a focus on interpretability and the process of modeling.
NeuroAI¶
In this intensive, graduate-level course for students with both AI and computational neuroscience experience, you’ll learn about how intelligent systems generalize. By applying principles from computational neuroscience, AI research, and cognitive neuroscience, this course will explore the fundamental nature of natural and artificial intelligence.
MILA¶
Mila is one of the world’s largest academic research institutes dedicated to deep learning. For years, its professors and students have been pushing the boundaries of artificial intelligence (AI) research across Canada and around the world.
As a leading AI research institute, Mila collaborates with Canadian universities, industry partners, international organizations and governments, putting AI at the service of society while also contributing to Canada’s vibrant AI ecosystem.
Drawing upon the skills and knowledge of more than 1,200 researchers, Mila is responsibly bridging the gap between state-of-the-art Al and human intelligence, particularly in relation to reasoning, robustness and other cognitive abilities still lacking in Al.
At Mila, students and world-renowned professors are pursuing groundbreaking work on both the foundational aspects of machine learning and many innovative algorithms. Each year, hundreds of Mila’s peer-reviewed scientific papers are presented at major AI conferences, including ICLR, ICML and NeurIPS.
Mila published many of the early papers on deep learning, including the introduction of word embeddings in neural language models; denoising auto-encoders; deep nets with ReLUs instead of sigmoids or tanh; self-attention and the resulting machine translation and NLP revolution; GANs, etc. Mila is also responsible for a popular textbook on deep learning (MIT Press, 2016), and co-created the International Conference on Learning Representations (ICLR).
The institute has been a hothouse for the development of deep reinforcement learning, and the theoretical foundations of deep learning, including why it works, optimization methods and analysis, generalization principles, in- and out-of-distribution data processing, causality, and generative and probabilistic methods.
Mila has developed expertise at the intersection of theoretical neuroscience and deep learning (NeuroAI), as well as in many areas of applied machine learning that focus on AI for social good. Computer vision, NLP and robotics, and applications in health care such as medical imaging and drug discovery have all been front and centre. So, too, has the environment, through Mila’s co-founding of the Climate Change AI organization. In addition, Mila has chosen to focus on AI4Science to support modelling and discovery in physics, chemistry and biology, and works actively within the social sciences — fighting sexual exploitation and modern slavery, while also contributing to the legal and philosophical aspects of responsible and safe AI.
Available Tools¶
https://
Center for Artificial Intelligence and Machine Learning - CAMIL¶
https://
CAMIL is a center of excellence in Artificial Intelligence and Machine Learning, gathering researchers and practitioners with comprehensive experience, ranging from knowledge discovery to semantic web to computer vision. CAMIL members have made internationally recognized contributions in fundamental research in AI and ML, and applied their expertise in numerous practical contexts, including medicine, biology, finance, and industry. CAMIL’s mission is to draw together the competences of its members and serve as a hub for channeling their combined activities. CAMIL is a member of Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) and the TAILOR network – Foundations of Trustworthy AI – Integrating Learning, Optimization and Reasoning.
Organization for Human Brain Mapping¶
The Organization for Human Brain Mapping (OHBM) is an international society dedicated to advancing the understanding of the anatomical and functional organization of the human brain using neuroimaging. A primary function of the society is to provide educational forums for the exchange of up-to-the-minute and groundbreaking research across neuroimaging methods and applications. OHBM achieves this through its member-led committees and an Annual Meeting that is held in different locations throughout the world.
OHBM Mission¶
The purpose of the Society shall be to advance the understanding of the anatomical and functional organization of the human brain, and promote its medical and societal applications.
OHBM will:
Bring together researchers who are engaged in investigations relevant to human brain organization (MRI, fMRI, PET, EEG/MEG, and other cutting-edge approaches such as electrophysiology, preclinical imaging, neuroepidemiology, genetics); and -Engage in other activities to facilitate communication among these scientists and promote education in human brain organization.
Resources¶
Aperture Neuro¶
Aperture Neuro is a peer-reviewed, low-cost, open-access journal dedicated to advancing our understanding of the brain, catalyzing the dissemination of novel methods and tools, and increasing the impact of brain imaging and assessment on society.
Affiliated with the Organization for Human Brain Mapping (OHBM), our mission is to support an innovative publishing ecosystem that reflects the diverse ways modern neuroimaging research is conducted and shared.
To foster an inclusive and respectful environment, we expect all authors, editors, and reviewers to follow the organization’s Code of Conduct. Scope of Submissions
We welcome submissions from all researchers in brain imaging and neuroscience. We seek contributions that enhance our understanding of the brain’s structure, function, organizational principles, and computational mechanisms.
In addition to traditional submission types, Aperture Neuro accepts non-traditional research objects, recognizing their contribution to scientific progress and striving to give them the credit and visibility they deserve.
Our current publication scope covers a wide array of categories, including:
Original research
Review articles
Book reviews
Tutorials
Conference and workshop summaries
Registered reports
Editorials
Commentaries
Replication studies
Toolboxes and resusable software
Dataset descriptions
Committee on Best Practices in Data Analysis and Sharing (COBIDAS)¶
In June 2014, OHBM Council created a “Statement on Neuroimaging Research and Data Integrity”, and in a practical move created a Committee on Best Practices in Data Analysis and Sharing (COBIDAS).
The OHBM Committee on Best Practice in Data Analysis and Sharing
(COBIDAS) on MRI was completed in 2016 and is available here:
http://
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., Proal, E., Thirion, B., Van Essen, D. C., White, T., Yeo, B. T. T. (2016). Best Practices in Data Analysis and Sharing in Neuroimaging using MRI. bioRxiv doi: 10.1101/054262.