Update course book

NeuromatchAcademy · Jul 16, 2024 · 8d41190 · 8d41190
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diff --git a/_sources/projects/docs/project_guidance.md b/_sources/projects/docs/project_guidance.md
@@ -2,7 +2,7 @@
 
 ## Summary
 
-This project plan explicitly encourages the iterative nature of research as a series of questions and answers that gradually refine your hypotheses. We have assigned you to pods based on your broad interests (neurons, fMRI, ECoG, theory and behavior). Each pod will split into two groups alphabetically, with the goal of making well-balanced groups. If the split is not well-balanced (for example, all the coding experts in one group), then move one or two people around.
+This project plan explicitly encourages the iterative nature of research as a series of questions and answers that gradually refine your hypotheses. We have assigned you to pods based on your broad interests (neurons, fMRI, ECoG, theory and behavior). Each pod will split into two groups, with the goal of making well-balanced groups. There is more guidance below.
 
 Once you're in groups, you will start brainstorming and searching the literature for interesting papers, with the goal of forming a project question. For the rest of week 1 you will try preliminary analyses of the datasets with the goal of refining and informing your question. Week 2 day 1 (W2D1) is dedicated to projects and will teach you a general strategy for approaching projects (the "10 steps"). You will apply this strategy to your own question. 
 

diff --git a/_sources/projects/neurons/README.md b/_sources/projects/neurons/README.md
@@ -38,7 +38,7 @@ Credit for data curation: Marius Pachitariu
 
 ## Allen Institute
 
-The Allen Institute dataset ([youtube](https://www.youtube.com/watch?v=3YP-GYvYnuA)) is new this year, and it was designed to be very friendly for beginners. The mice do a visual adaptation task using either familiar or novel images. The recordings are from specific neuron populations (VIP, SST etc) in multiple visual cortical brain areas. This dataset is well supported with code and a dedicated project template. This would provide a more focused experience for beginner groups than the Steinmetz dataset, with the caveat that the data is unpublished so it is harder to find supporting information for it. For more advanced groups, a separate dataloader is available using the Allen Institute SDK, which gives access to the entire dataset for more exploratory analyses.
+The Allen Institute dataset ([youtube](https://www.youtube.com/watch?v=3YP-GYvYnuA)) is new this year, and it was designed to be very friendly for beginners. The mice do a visual change detection task using either familiar or novel images. The recordings are from specific neuron populations (Excitatory, VIP, and SST) in multiple visual cortical brain areas (V1 and LM). This dataset is well supported with code and a dedicated project template. This would provide a more focused experience for beginner groups than the Steinmetz dataset. For more advanced groups, a separate dataloader is available using the Allen Institute SDK, which gives access to the entire dataset for more exploratory analyses. You can learn more about the dataset and find additional description and helpful tools on [Allen Brain Atlas](https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html) and [SWDB databook](https://allenswdb.github.io/physiology/ophys/visual-behavior/VB-Ophys.html).
 
 Credit for data curation: Marina Garret, Iryna Yavorska, Doug Ollerenshaw
 
@@ -47,8 +47,6 @@ Credit for data curation: Marina Garret, Iryna Yavorska, Doug Ollerenshaw
 | Analyze one dataset | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/projects/neurons/load_Allen_Visual_Behavior_from_pre_processed_file.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/NeuromatchAcademy/course-content/blob/main/projects/neurons/load_Allen_Visual_Behavior_from_pre_processed_file.ipynb?flush_cache=true) |
 | Access to all data | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/projects/neurons/load_Allen_Visual_Behavior_from_SDK.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/NeuromatchAcademy/course-content/blob/main/projects/neurons/load_Allen_Visual_Behavior_from_SDK.ipynb?flush_cache=true) |
 
-### References: none yet for this dataset, but see:
+### You can read more about scientific discoveries related to this dataset in our preprint:
 
-- de Vries, S. E., Lecoq, J. A., Buice, M. A., Groblewski, P. A., Ocker, G. K., Oliver, M., ... and Koch, C. (2020). A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nature neuroscience, 23(1): 138-151. doi: [10.1038/s41593-019-0550-9](https://doi.org/10.1038/s41593-019-0550-9)
-
-- Siegle, J. H., Jia, X., Durand, S., Gale, S., Bennett, C., Graddis, N., ... and Koch, C. (2021). Survey of spiking in the mouse visual system reveals functional hierarchy. Nature, 592(7852): 86-92. doi: [10.1038/s41586-020-03171-x](https://doi.org/10.1038/s41586-020-03171-x)
+- 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]
diff --git a/_sources/projects/theory/README.md b/_sources/projects/theory/README.md
@@ -28,7 +28,7 @@ Here is a list of cool databases you might want to use to look for existing mode
 | Name                                                  | Model? | Analyses? | Data? | Description                                                  |
 | ----------------------------------------------------- | ------ | --------- | ----- | ------------------------------------------------------------ |
 | [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. |
-| [ModelDB](https://senselab.med.yale.edu/modeldb/)     | :+1:   |           |       | **ModelDB** provides an accessible location for storing and efficiently retrieving computational neuroscience models. A ModelDB entry contains a model's source code, concise description, and a citation of the article that published it. |
+| [ModelDB](https://modeldb.science/)     | :+1:   |           |       | **ModelDB** provides an accessible location for storing and efficiently retrieving computational neuroscience models. A ModelDB entry contains a model's source code, concise description, and a citation of the article that published it. |
 | [Open Source Brain](https://www.opensourcebrain.org/) | :+1:   |           |       | **Open Source Brain** is a resource for sharing and collaboratively developing computational models of neural systems. |
 | [CRCNS](http://crcns.org/)                            | :+1:   | :+1:      | :+1:  | **CRCNS Data sharing website** is a marketplace and discussion forum for sharing tools and data in neuroscience |
 | [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. |

diff --git a/projects/behavior/behavior_videos.html b/projects/behavior/behavior_videos.html
diff --git a/projects/docs/project_guidance.html b/projects/docs/project_guidance.html
@@ -1823,7 +1823,7 @@ <h2> Contents </h2>
 <h1>Daily guide for projects<a class="headerlink" href="#daily-guide-for-projects" title="Permalink to this heading">#</a></h1>
 <section id="summary">
 <h2>Summary<a class="headerlink" href="#summary" title="Permalink to this heading">#</a></h2>
-<p>This project plan explicitly encourages the iterative nature of research as a series of questions and answers that gradually refine your hypotheses. We have assigned you to pods based on your broad interests (neurons, fMRI, ECoG, theory and behavior). Each pod will split into two groups alphabetically, with the goal of making well-balanced groups. If the split is not well-balanced (for example, all the coding experts in one group), then move one or two people around.</p>
+<p>This project plan explicitly encourages the iterative nature of research as a series of questions and answers that gradually refine your hypotheses. We have assigned you to pods based on your broad interests (neurons, fMRI, ECoG, theory and behavior). Each pod will split into two groups, with the goal of making well-balanced groups. There is more guidance below.</p>
 <p>Once you’re in groups, you will start brainstorming and searching the literature for interesting papers, with the goal of forming a project question. For the rest of week 1 you will try preliminary analyses of the datasets with the goal of refining and informing your question. Week 2 day 1 (W2D1) is dedicated to projects and will teach you a general strategy for approaching projects (the “10 steps”). You will apply this strategy to your own question.</p>
 <p>For the rest of the second week you will continue to analyze data / refine your question, with the goal of writing an abstract on W2D5, which is another project day. Your abstract may or may not include results, but it should at least include a testable hypothesis. You’ll swap abstracts with another group on W2D5. For the rest of the course, you will focus on getting evidence for/against your hypothesis.</p>
 <p>Finally, in W3D5 you will meet with other groups in your pod and megapod (organized by the lead TAs), and tell them the story of your project. This is a low-key presentation that may include some of the plots you made along the way, but it is not meant as a real research presentation with high “production values”. See some of the <a class="reference external" href="https://compneuro.neuromatch.io/projects/docs/project_2020_highlights.html">examples</a>  to get a sense of what the presentation will look like.</p>

diff --git a/projects/fMRI/fMRI_videos.html b/projects/fMRI/fMRI_videos.html
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@@ -2065,8 +2065,8 @@ <h1>Question<a class="headerlink" href="#question" title="Permalink to this head
 <p><em>Part of Step 1</em></p>
 <p>We assume that we have build the train illusion model (see the other example project colab). That model predicts that accumulated sensory evidence from vestibular signals determines the decision of whether self-motion is experienced or not. We now have vestibular neuron data (simulated in our case, but let’s pretend) and would like to see if that prediction holds true.</p>
 <p>The data contains <span class="math notranslate nohighlight">\(N\)</span> neurons and <span class="math notranslate nohighlight">\(M\)</span> trials for each of 3 motion conditions: no self-motion, slowly accelerating self-motion and faster accelerating self-motion.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-39c791e7-39d8-469f-8460-f614e572e36a">
-<span class="eqno">(470)<a class="headerlink" href="#equation-39c791e7-39d8-469f-8460-f614e572e36a" title="Permalink to this equation">#</a></span>\[\begin{align}
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 N &amp;= 40\\
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 \end{align}\]</div>
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 <p>Blue is the no-motion condition, and produces flat average spike counts across the 3 s time interval. The orange and green line do show a bell-shaped curve that corresponds to the acceleration profile. But there also seems to be considerable noise: exactly what we need. Let’s see what the spike trains for a single trial look like:</p>
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 <p>You can change the trial number in the bit of code above to compare what the rasterplots look like in different trials. You’ll notice that they all look kind of the same: the 3 conditions are very hard (impossible?) to distinguish by eye-balling.</p>
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 <p>We asked for 8 cross validations, which show up as the blue dots in the graph (two have the same accuracy). Prediction accuracy ranges from 56% to 72%, with the average at 65%, and the orange line is the median. Given the noisy data, that is not too bad actually.</p>
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 <p>This is the exact same figure as before, so our function <code class="docutils literal notranslate"><span class="pre">classifyMotionFromSpikes()</span></code> also works as intended.</p>
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 <p>Well, that’s interesting! The logistic regression doesn’t do a perfect job, but there is information in these results.</p>