This module covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at NYU's Center for Data Science, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Pr

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

Future computing systems will capitalize on our increased understanding of the brain through the use of similar architectures and computational principles. During this workshop, we bring together recent developments in this rapidly developing field of neuromorphic computing systems, and also discuss challenges ahead.

This course consists of introductory lectures on different aspects of biophysical models. By following this course you will learn about various neuronal models, neuron anatomy and signaling, as well the numerous and complex cellular mechanisms underlying healthy brain function. 

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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This module introduces computational neuroscience by simulating neurons according to the AdEx model. You will learn about generative modeling, dynamical systems, and F-I curves. The MATLAB code introduces live scripts and functions.

This course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.

This course explores ethical and social issues that have arisen, and continue to arise, from the rapid research development in neuroscience, medicine, and ICT. Lectures focus on key ethical issues contained in the HBP – such as the ethics of robotics, dual use, ICT ethical issues, big data and individual privacy, and the use of animals in research.

This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.

This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.

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 couse is the opening module for the University of Toronto's Krembil Centre for Neuroinformatics' virtual learning series Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience. Lessons in this course introduce participants to the study of brain disorders, starting from elemental units like genes and neurons, eventually building up to whole-brain modelling and global activity patterns.

Neuroanatomy provides one of the unifying frameworks for neuroscience and thus it is not surprising that it provides the basis for many neuroinformatics tools and approaches.  Regardless of whether one is working at the subcellular, cellular or gross anatomical level or whether one is modeling circuitry, molecular pathways or function, at some point, this work will include an anatomical reference.

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).

This course consists of several lightning talks from the second day of INCF's Neuroinformatics Assembly 2023. Covering a wide range of topics, these brief talks provide snapshots of various neuroinformatic efforts such as brain-computer interface standards, dealing with multimodal animal MRI datasets, distributed data management, and several more. 

The workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

This course consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

This workshop provides an opportunity to explore the advanced tools and techniques for data sharing, analysis, visualization, and simulation.

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.