This course covers the concepts of recurrent and convolutional nets (theory and practice), natural signals properties and the convolution, and recurrent neural networks (vanilla and gated, LSTM).
Course designed for advanced learners interested in understanding the foundations of Machine Learning in Python.
General: The course consists of 15 lectures (ca. 1-2 hours each) and 15 exercise sheets (for ca. 6 hours of programming each).
Institution: High-Performance Computing and Analytics Lab, University of Bonn
This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.
This course begins with the conceptual basics of machine learning and then moves on to some Python-based applications of popular supervised learning algorithms to neuroscience data. This is followed by a series of lectures that explore the history and applications of deep learning, ending with a presentation on the potential of deep learning for neuroscience applications/mis-applications.
This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.
Sessions from the INCF Neuroinformatics Assembly 2022 day 1.
Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.
As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and remaining barriers.
The course provides an introduction to the growing field of electrophysiology standards, infrastructure, and initiatives. From data curation on open research infrastructures like EBRAINS, to overviews of national data analytics platforms like Australia's AEDAPT, the lessons in this course highlight already available resources for the global neuroinformatics commuity while also reinforcing the need for and importance of FAIR science principles in future research projects.
Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools.
The lecture series focuses on current trends in modern techniques in neuroscience. Inspiring scientists from the NeurotechEU Alliance will give an overview of the latest advances and developments.
This course contains sessions from the second day of INCF's Neuroinformatics Assembly 2022.
These courses give introductions and overviews of some of the major statistics software packages currently used in neuroscience research.
This lecture series is presented by NeuroTechEU, an alliance between eight European universities with the goal to build a trans-European network of excellence in brain research and technologies. By following along with this series, participants will learn about the history of cognitive science and the development of the field in a sociocultural context, as well as its trajectory into the future with the advent of artificial intelligence and neural network development.
This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.
These lessons give an overview of the principles underpinning the objectives, policies, and practice of Open Science, including several representative policy documents that will be increasingly relevant to neuroscience research.
The Neurodata Without Borders: Neurophysiology project (NWB, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy-barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.
A virtual workshop with lectures and hands-on tutorials that will teach participants how to use open-source Miniscopes for in vivo calcium imaging. This workshop is designed to introduce all aspects of using Miniscopes, including basic principles of Miniscope design and imaging, how to build and attach a Miniscope, how to implant a GRIN lens for imaging deep structures, and how to analyze imaging data.
This workshop provides an opportunity to explore the advanced tools and techniques for data sharing, analysis, visualization, and simulation.
Sessions from the INCF Neuroinformatics Assembly 2022 day 1.