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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. 

Difficulty level: Intermediate
Duration: 1:28:14

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. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke

This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth-cone shape dynamics.

Difficulty level: Intermediate
Duration: 26:12
Speaker: : Geoff Goodhill

In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriente primary dendrites toward active axons in the barrel hollow.

Difficulty level: Intermediate
Duration: 27:32
Speaker: : Tomomi Shimogori

In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.

Difficulty level: Intermediate
Duration: 30:45
Speaker: : Nenad Sestan

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

Difficulty level: Intermediate
Duration: 3:54
Speaker: : Dan Goodman

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

Difficulty level: Intermediate
Duration: 9:40
Speaker: : Dan Goodman

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

Difficulty level: Intermediate
Duration: 1:39:04

In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI). 

Difficulty level: Intermediate
Duration: 1:35:14
Speaker: : Matt Glasser

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

Difficulty level: Intermediate
Duration: 8:41
Speaker: : Marcus Ghosh

This lecture highlights the importance of correct annotation and assignment of location, and updated atlas resources to avoid errors in navigation and data interpretation.

Difficulty level: Intermediate
Duration: 22:04
Speaker: : Trygve Leergard

We are at the exciting technological stage where it has become feasible to represent the anatomy of an entire human brain at the cellular level. This lecture discusses how neuroanatomy in the 21st Century has become an effort towards the virtualization and standardization of brain tissue.

Difficulty level: Intermediate
Duration: 25:27
Speaker: : Jacopo Annese

This lecture covers essential features of digital brain models for neuroinformatics, particularly NeuroMaps. 

Difficulty level: Intermediate
Duration: 22:26
Speaker: : Douglas Bowden

This presentation covers the neuroinformatics tools and techniques used and their relationship to neuroanatomy for the Allen Institute's atlases of the mouse, developing mouse, and mouse connectional atlas.

Difficulty level: Intermediate
Duration: 23:41
Speaker: : Mike Hawrylycz