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. 

In this course, you will learn how computational neuroscientists use mathematical models and computer simulations to study different plasticity phenomena in the brain. During the course, you will program your own neuron model, a so-called leaky-integrate-and-fire (LIF) neuron model, and simulate it with a computer. You will also learn how to add various neuronal properties and plasticity mechanisms to the model and study how they operate.

In this short course, you will learn about Jupyter Notebooks, an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

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

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 course consists of a series of webinars organized by the International Neuroethics Society on various neuroethics topics. 

This course contains videos, lectures, and hands-on tutorials as part of INCF's Neuroinformatics Assembly 2023 workshop on developing robust and reproducible research workflows to foster greater collaborative efforts in neuroscience.

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

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 module is intended to provide a foundation in energy-based models, and 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. Prerequisites for this mo

The goal of this module is to work with action potential data taken from a publicly available database. You will learn about spike counts, orientation tuning, and spatial maps. The MATLAB code introduces data types, for-loops and vectorizations, indexing, and data visualization.

This introductory-level course provide learners with an introduction to the field of neuroethics and spans the ethics of neuroscience to the neuroscience of ethics. The ethics of neuroscience lectures cover the ethical issues that arise in device/drug enhancement, imaging/monitoring, and social uses of neuroscience in the legal/justice system. The neuroscience of ethics lectures cover the origin of ethics (neural mechanisms and evolutionary origin).

This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR scienctific principles. The tutorials also provide an introduction to some of the most commonly used open-source scientific tools, including Git, GitHub, Google Colab, Binder, Docker, and the programming languages Python and R. 

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.

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 human mind is a complex system that produces, processes, and transmits information in an incomparable manner. Human thoughts and actions depend profoundly on the proper function of neurons. If this function is disrupted, degeneration and disease can be the consequence. This course provides insights into state-of-the-art views on neurodegenerative, neuropsychiatric, and neuroimmunological disorders as well as clinical neuroanatomy and clinical aspects of brain imaging.

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.

This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation.