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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
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Difficulty level: Beginner
Duration: 43:38
Speaker: : Kent Lloyd
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This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network. 

Difficulty level: Beginner
Duration: 1:32:17
Speaker: : Shih-Chii Liu
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Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.

Difficulty level: Beginner
Duration: 21:15
Speaker: : Tadashi Yamazaki
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Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.

Difficulty level: Beginner
Duration: 41:57
Speaker: : Giacomo Indiveri
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Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.

Difficulty level: Beginner
Duration: 20:39
Speaker: : Giacomo Indiveri
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The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.

Difficulty level: Intermediate
Duration: 19:57
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This lecture covers describing and characterizing an input-output relationship.

Difficulty level: Beginner
Duration: 1:35:33
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Part 1 of 2 of a tutorial on statistical models for neural data

Difficulty level: Beginner
Duration: 1:45:48
Speaker: : Jonathan Pillow
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Part 2 of 2 of a tutorial on statistical models for neural data.

Difficulty level: Beginner
Duration: 1:50:31
Speaker: : Jonathan Pillow
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Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:26:06
Speaker: : Bard Ermentrout
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Introduction to stability analysis of neural models

Difficulty level: Intermediate
Duration: 1:25:38
Speaker: : Bard Ermentrout
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Oscillations and bursting

Difficulty level: Intermediate
Duration: 1:24:30
Speaker: : Bard Ermentrout
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Oscillations and bursting

Difficulty level: Intermediate
Duration: 1:31:57
Speaker: : Bard Ermentrout
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Weakly coupled oscillators

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Bard Ermentrout
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Continuation of coupled oscillators

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : Bard Ermentrout
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Firing rate models.

Difficulty level: Intermediate
Duration: 1:26:42
Speaker: : Bard Ermentrout
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Pattern generation in visual system hallucinations.

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

Introduction to stability analysis of neural models

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

Introduction to stability analysis of neural models

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