This lecture presents the Medical Informatics Platform's data federation in epilepsy.

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.

This talk introduces data sharing initiatives in Epilepsy, particularly across Europe.

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

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. 

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

Overview of the content for Day 1 of this course.

Best practices: the tips and tricks on how to get your Miniscope to work and how to get your experiments off the ground.

This talk delves into challenges and opportunities of Miniscope design, seeking the optimal balance between scale and function.

Attendees of this talk will learn aobut computational imaging systems and associated pipelines, as well as open-source software solutions supporting miniscope use.

This talk covers the present state and future directions of calcium imaging data analysis, particularly in the context of one-photon vs two-photon approaches. 

In this talk, results from rodent experimentation using in vivo imaging are presented, demonstrating how the monitoring of neural ensembles may reveal patterns of learning during spatial tasks.

How to start processing the raw imaging data generated with a Miniscope, including developing a usable pipeline and demoing the Minion pipeline.

The direction of miniature microscopes, including both MetaCell and other groups.

Overview of the content for Day 2 of this course.

Summary and closing remarks for this three-day course.