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In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Bard Ermentrout

This lesson provides a continued discussion and characterization of coupled oscillators. 

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : Bard Ermentrout

This lesson gives an overview of modeling neurons based on firing rate. 

Difficulty level: Intermediate
Duration: 1:26:42
Speaker: : Bard Ermentrout

This lesson characterizes the pattern generation observed in visual system hallucinations.

Difficulty level: Intermediate
Duration: 1:20:42
Speaker: : Bard Ermentrout

This lesson gives an introduction to stability analysis of neural models.

Difficulty level: Intermediate
Duration: 1:26:06
Speaker: : Bard Ermentrout

This lesson continues from the previous lectures, providing introduction to stability analysis of neural models.

Difficulty level: Intermediate
Duration: 1:25:38
Speaker: : Bard Ermentrout

In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.

Difficulty level: Intermediate
Duration: 1:24:30
Speaker: : Bard Ermentrout

This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting. 

Difficulty level: Intermediate
Duration: 1:31:57
Speaker: : Bard Ermentrout

This lesson provides more context around weakly coupled oscillators.

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Bard Ermentrout

This lesson builds upon previous lectures in this series, providing an overview of coupled oscillators.

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : Bard Ermentrout

In this lesson, you will learn about neuronal models based on their spike rate. 

Difficulty level: Intermediate
Duration: 1:26:42
Speaker: : Bard Ermentrout

In this lesson, you will learn about neural activity pattern generation in visual system hallucinations.

Difficulty level: Intermediate
Duration: 1:20:42
Speaker: : Bard Ermentrout

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate
Duration: 1:11:04
Speaker: : Etay Hay

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. 

Difficulty level: Intermediate
Duration: 1:32:53

This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter. 

Difficulty level: Intermediate
Duration: 39:05
Speaker: : Jil Meier

This lecture provides an general introduction to epilepsy, as well as why and how TVB can prove useful in building and testing epileptic models. 

Difficulty level: Intermediate
Duration: 37:12
Speaker: : Julie Courtiol

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

Difficulty level: Intermediate
Duration: 17:08
Speaker: : Ari Ercole

This lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate
Duration: 21:54

This lecture explains the need for data federation in medicine and how it can be achieved.

Difficulty level: Intermediate
Duration: 27:09
Speaker: : Philippe Ryvlin

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.

Difficulty level: Intermediate
Duration: 15:05