This lecture covers modeling the neuron in silicon, modeling vision and audition, and sensory fusion using a deep network.
This lesson gives an overview of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.
Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.
In this lesson, you will learn about data management within the Open Data Commons (ODC) framework, and in particular, how Spinal Cord Injury (SCI) data is stored, shared, and published. You will also hear about Frictionless Data, an open-source toolkit aimed at simplifying the data experience.
This lesson introduces several open science tools like Docker and Apptainer which can be used to develop portable and reproducible software environments.
This talk covers the differences between applying HED annotation to fMRI datasets versus other neuroimaging practices, and also introduces an analysis pipeline using HED tags.
This lesson provides a brief visual walkthrough on the necessary steps when copying data from one brainlife project to another.
This lesson visually documents the process of uploading data to brainlife via the command line interface (CLI).
This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).
This video will document the process of running an app on brainlife, from data staging to archiving of the final data outputs.
This video demonstrates each required step for preprocessing T1w anatomical data in brainlife.io.
This short video shows how data in a brainlife.io publication can be opened from a DOI inside a published article. The video provides an example of how the DOI deposited on the journal can be opened with a web browser to redirect to the associated data publication on brainlife.io.
This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.
This video explains what metadata is, why it is important, and how you can organize your metadata to increase the FAIRness of your data on EBRAINS.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lesson is part 1 of 2 of a tutorial on statistical models for neural data.
What is the difference between attention and consciousness? This lecture describes the scientific meaning of consciousness, journeys on the search for neural correlates of visual consciousness, and explores the possibility of consciousness in other beings and even non-biological structures.
The "connectome" is a term, coined in the past decade, that has been used to describe more than one phenomenon in neuroscience. This lecture explains the basics of structural connections at the micro-, meso- and macroscopic scales.
EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information.