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The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.

Difficulty level: Beginner
Duration: 55:36
Speaker: : Elissa Chesler

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate
Duration: 0:40

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

Difficulty level: Intermediate
Duration: 1:23

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

Difficulty level: Intermediate
Duration: 1:20

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

Difficulty level: Intermediate
Duration: 1:08

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

Difficulty level: Intermediate
Duration: 1:18

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

Difficulty level: Intermediate
Duration: 1:26

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

Difficulty level: Intermediate
Duration: 0:42

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

Difficulty level: Intermediate
Duration: 2:40

This lesson reviews theoretical and mathematical descriptions of correlated spike trains.

Difficulty level: Intermediate
Duration: 2:54

This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).

Difficulty level: Intermediate
Duration: 1:43

This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.

Difficulty level: Intermediate
Duration: 2:58

In this lesson, you will learn about the intrinsic plasticity of single neurons.

Difficulty level: Intermediate
Duration: 2:08

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

Difficulty level: Intermediate
Duration: 1:58

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

Difficulty level: Intermediate
Duration: 1:40

This lesson briefly wraps up the course on Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate
Duration: 0:37

This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles. 

Difficulty level: Beginner
Duration: 25:44
Speaker: : Fahim Imam

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

Difficulty level: Intermediate
Duration: 33:41

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

Difficulty level: Intermediate
Duration: 50:18
Speaker: : Jeff Grethe