This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

In this lesson, you will learn how to utilize various features and tools included in the EBRAINS platform, particularly focusing on rodent brain atlases and how to incorporate them into your analyses. 

This talk describes how to use DataLad for your data management and curation techniques  when dealing with animal datasets, which often contain several disparate types of data, including MRI, microscopy, histology, electrocorticography, and behavioral measurements. 

In this short talk you will learn about The Neural System Laboratory, which aims to develop and implement new technologies for analysis of brain architecture, connectivity, and brain-wide gene and molecular level organization.

In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species. 

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system. 

In this lecture, attendees will learn how Mutant Mouse Resource and Research Center (MMRRC) archives, cryopreserves, and distributes scientifically valuable genetically engineered mouse strains and mouse ES cell lines for the genetics and biomedical research community.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.

This lesson corresponds to slides 158-187 of the PDF below. 

This lecture discusses the the importance and need for data sharing in clinical neuroscience.

This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.

This lecture gives an overview on the European Health Dataspace. 

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience. 

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.

This demonstration walks through how to import your data into MATLAB.

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.