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projects/ECoG/README.md

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# Guide to choosing an EEG/ECoG/LFP dataset
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*July 5-23, 2021*
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We have two types of ECoG datasets, each with their project template: one set from the AJILE12 dataset, and one set from Kai Miller's lab. Scroll down to see the project templates.
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New in 2021, we have ECoG datasets ([youtube](https://youtube.com/watch?v=rAqtrBhwS80)) from Kai Miller! This is a rare dataset from intracranial electrocorticographic recordings in clinical settings. Please watch Kai Miller's TED talk to familiarize yourself with this type of recording.
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## Exploring AJILE12 dataset:
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See these papers for detailed information about the dataset:
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- Peterson, S. M., Singh, S. H., Wang, N. X., Rao, R. P., & Brunton, B. W. (2021). Behavioral and neural variability of naturalistic arm movements. Eneuro, 8(3). doi: [10.1523/ENEURO.0007-21.2021](https://doi.org/10.1523/ENEURO.0007-21.2021)
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- Singh, S. H., Peterson, S. M., Rao, R. P., & Brunton, B. W. (2021). Mining naturalistic human behaviors in long-term video and neural recordings. Journal of Neuroscience Methods, 358, 109199. doi: [10.1016/j.jneumeth.2021.109199](https://doi.org/10.1016/j.jneumeth.2021.109199)
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Credit for data curation: Nima Dehghani
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| | Run | View |
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| - | --- | ---- |
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| AJILE12 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/projects/ECoG/exploreAJILE12.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/NeuromatchAcademy/course-content/blob/main/projects/ECoG/exploreAJILE12.ipynb?flush_cache=true) |
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## Kai Miller datasets:
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This a series of smaller datasets ([youtube](https://youtube.com/watch?v=rAqtrBhwS80)) from intracranial electrocorticographic recordings in clinical settings. Please watch Kai Miller's TED talk to familiarize yourself with this type of recording.
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* The datasets are more or less at the same difficulty level. All datasets are from the same research group, using the same recording methods and standardized protocols.
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- Miller, K. J., Schalk, G., Fetz, E. E., Den Nijs, M., Ojemann, J. G., and Rao, R. P. (2010). Cortical activity during motor execution, motor imagery, and imagery-based online feedback. Proceedings of the National Academy of Sciences 107(9):4430-4435. doi: [10.1073/pnas.0913697107](https://doi.org/10.1073/pnas.0913697107)
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### Exploring AJILE12 dataset:
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- Peterson, S. M., Singh, S. H., Wang, N. X., Rao, R. P., & Brunton, B. W. (2021). Behavioral and neural variability of naturalistic arm movements. Eneuro, 8(3). doi: [10.1523/ENEURO.0007-21.2021](https://doi.org/10.1523/ENEURO.0007-21.2021)
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- Singh, S. H., Peterson, S. M., Rao, R. P., & Brunton, B. W. (2021). Mining naturalistic human behaviors in long-term video and neural recordings. Journal of Neuroscience Methods, 358, 109199. doi: [10.1016/j.jneumeth.2021.109199](https://doi.org/10.1016/j.jneumeth.2021.109199)
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# Project Templates
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Click on each image below to see a full browser version!
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# Exploring AJILE12 dataset
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<img src="template_images/NeuromatchProject_AJILE12.svg" width="100%">
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# Does neural activity reflect face perception?
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<img src="template_images/NeuralBasisOfFacePerception.svg" width="100%">

projects/behavior_and_theory/README.md

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# Guide to choosing a Behavior dataset
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# Guide to choosing a Behavior And Theory dataset
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*July 5-23, 2021*
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The behavior and theory datasets and project templates are combined into the same topic. They range from projects focused on data analysis (i.e. the Caltech dataset) to pure theory projects where you implement and study a model like a recurrent neural network. Scroll down to see the project templates.
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Everyone should consider the behavior-only datasets that we have, which are very rich with many subjects and many trials.
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Pure theory projects are usually pursued by more advanced students. If your group is relatively new to neuroscience, you should consider doing a dataset or behavior project *and* include a theory component to it.
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Every group can bring up and discuss theory projects. We have several example project templates for this that include code. However, a theory project can go in any direction.
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## Caltech
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| Additional analyses | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/projects/behavior/laquitaine_motion_prior_learning.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/NeuromatchAcademy/course-content/blob/main/projects/behavior/laquitaine_motion_prior_learning.ipynb) |
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# Guide to choosing a Theory project
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*July 5-23, 2021*
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Pure theory projects are usually pursued by more advanced students. If your group is relatively new to neuroscience, you should consider doing a dataset project *and* include a theory component to it.
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Every group can bring up and discuss theory projects. We have two example project templates for this that include code. However, a theory project can go in any direction.
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## Working memory
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| | Run | View |
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## Databases of models
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Here is a list of cool databases you might want to use to look for existing models, computational analyses / data processing tools and data.
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| Name | Model? | Analyses? | Data? | Description |
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| [BioModels](https://www.ebi.ac.uk/biomodels/) | :+1: | | | **BioModels** is a repository of mathematical models of biological and biomedical systems. It hosts a vast selection of existing literature-based physiologically and pharmaceutically relevant mechanistic models in standard formats. |
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| [EEGbase]([http://eegdatabase.kiv.zcu.cz/home-page](http://neuroinformatics.kiv.zcu.cz/articles/read/eegerp-portal-eegbase-_2014-12-19)) | | :+1: | :+1: | **EEGbase** is a system for storage, management, sharing and retrieval of EEG/ERP data, metadata, tools and documents related to electrophysiology. |
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| [INCF resources](https://www.incf.org/resources/sbps) | :+1: | :+1: | :+1: | A list of resources endorsed by **INCF**. |
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| [NITRC](https://www.nitrc.org/) | :+1: | :+1: | :+1: | **NeuroImaging Tools & Resources Collaboratory** is an award-winning free web-based resource that offers comprehensive information on an ever expanding scope of neuroinformatics software and data. |
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## Additional resources
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| Name | Description |
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| [figshare](https://figshare.com/) | A database of everything research related that's openly shared |
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| [Google Dataset search](https://datasetsearch.research.google.com/) | Lets you do a Google search specifically to find datasets |
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| [NeuroVault](https://neurovault.org/) | A public repository of unthresholded statistical maps, parcellations, and atlases of the brain. (MRI and PET) |
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| [KnowledgeSpace](https://knowledge-space.org/) | A globally-used, community-based, data-driven encyclopedia for neuroscience that links brain research concepts to data, models, and the literature that support them. |
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| [KnowledgeSpace](https://knowledge-space.org/) | A globally-used, community-based, data-driven encyclopedia for neuroscience that links brain research concepts to data, models, and the literature that support them. |
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# Project Templates
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Click on each image below to see a full browser version!
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# The structure of mouse social behavior
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<img src="template_images/MouseSocialBehavior.svg" width="100%">
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# State-dependent decision-making in mice performing a 2AFC task
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<img src="template_images/MouseBehavior2AFC.svg" width="100%">
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# Probing the dynamics of human estimation errors in a Bayesian framework
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<img src="template_images/HumanEstimationErrorDynamicsInBayesianFramework.svg" width="100%">
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# The working memory capacity of recurrent neural network models
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<img src="template_images/WorkingMemoryRNNs.svg" width="100%">
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# Attractor models of working memory
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<img src="template_images/WorkingMemoryAttractorModels.svg" width="100%">
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# Linking structure, dynamics and function in recurrent neural networks (RNNs) during motor learning
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<img src="template_images/StructureDynamicFunctionInMotorRNN.svg" width="100%">

projects/docs/project_templates.md

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# Project Templates
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Click on each image below to see a full browser version!
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# Flow of information through the brain during a sensorimotor task
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<img src="template_images/InformationFlowSensoryMotorTask.svg" width="100%">
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# Effect of stimulus context and behavior state on visual representations
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<img src="template_images/StimulusContextBehaviorState.svg" width="100%">
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# Behavior representations in mouse visual cortex
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<img src="template_images/MouseOrofacialBehaviors.svg" width="100%">
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# Mapping brain activation and representation with fMRI
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<img src="template_images/MappingBrainRepresentationwithfMRI.svg" width="100%">
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# Retinotopic mapping with fMRI
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<img src="template_images/RetinotopicMappingFMRI.svg" width="100%">
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# Modelling of navigational affordances and scene-selective responses
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<img src="template_images/NavigationalAffordancesFMRI.svg" width="100%">
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# Visual information in different brain regions
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<img src="template_images/VisualInformationAcrossRegions.svg" width="100%">
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# State-dependent decision-making in mice performing a 2AFC task
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<img src="template_images/MouseBehavior2AFC.svg" width="100%">
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# The working memory capacity of recurrent neural network models
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<img src="template_images/WorkingMemoryRNNs.svg" width="100%">
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# Attractor models of working memory
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<img src="template_images/WorkingMemoryAttractorModels.svg" width="100%">
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# Does neural activity reflect face perception?
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<img src="template_images/NeuralBasisOfFacePerception.svg" width="100%">
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# The structure of mouse social behavior
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<img src="template_images/MouseSocialBehavior.svg" width="100%">
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# Probing the dynamics of human estimation errors in a Bayesian framework
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<img src="template_images/HumanEstimationErrorDynamicsInBayesianFramework.svg" width="100%">
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# Linking structure, dynamics and function in recurrent neural networks (RNNs) during motor learning
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<img src="template_images/StructureDynamicFunctionInMotorRNN.svg" width="100%">
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# Exploring AJILE12 dataset
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<img src="template_images/NeuromatchProject_AJILE12.svg" width="100%">

projects/neurons/README.md

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# Guide to choosing a Neurons dataset
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*July 5-23, 2021*
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Scroll down for project templates associated to these datasets.
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## Steinmetz
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### You can read more about scientific discoveries related to this dataset in our preprint:
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- Garrett, M. et. al. (2023) Stimulus novelty uncovers coding diversity in visual cortical circuits. bioRxiv doi: [https://www.biorxiv.org/content/10.1101/2023.02.14.528085v2]
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# Project Templates
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Click on each image below to see a full browser version!
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# Flow of information through the brain during a sensorimotor task
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<img src="template_images/InformationFlowSensoryMotorTask.svg" width="100%">
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# Effect of stimulus context and behavior state on visual representations
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<img src="template_images/StimulusContextBehaviorState.svg" width="100%">
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# Behavior representations in mouse visual cortex
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<img src="template_images/MouseOrofacialBehaviors.svg" width="100%">
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