This lecture provides an introductory overview of some of the most important concepts in software engineering.
This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles.
This lecture covers structured data, databases, federating neuroscience-relevant databases, and ontologies.
This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.
This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.
This primer on optogenetics primer discusses how to manipulate neuronal populations with light at millisecond resolution and offers possible applications such as curing the blind and "playing the piano" with cortical neurons.
This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
This lecture covers the history of behaviorism and the ultimate challenge to behaviorism.
This lecture covers various learning theories.
How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.
This lesson goes into the mechanisms behind changes in synaptic function created by learning.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture covers an Introduction to neuron anatomy and signaling, as well as different types of models, including the Hodgkin-Huxley model.
This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lecture provides an introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.