NeuromatchAcademy · iamzoltan · Jun 26, 2025 · Jun 25, 2025 · Jun 25, 2025 · Jun 25, 2025
diff --git a/projects/neurons/README.md b/projects/neurons/README.md
@@ -2,22 +2,38 @@
 
 Scroll down for project templates associated to these datasets. 
 
-## Stringer
+## International Brain Laboratory
+
+The International Brain Laboratory (IBL) brain-wide map dataset ([youtube](https://www.youtube.com/watch?v=N69nvrnmq9g)) includes data from 699 Neuropixels probe insertions across 281 brain regions, recorded during a standardized visual decision-making task. To help users get started, a dedicated project and step-by-step tutorial are available. For more advanced users, the IBL ONE tutorial demonstrates how to access the full range of IBL data using the Open Neurophysiology Environment (ONE) API, enabling deeper exploration and custom analyses across the entire dataset.
+
+Credit for data curation: Mayo Faulkner
+
+|                       | Run                                                                                                                                                                                                                    | View                                                                                                                                                                                                               |
+|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Analyze prepared 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/IBL_BWM_Neuromatch_tutorial.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/IBL_BWM_Neuromatch_tutorial.ipynb?flush_cache=true) |
+| IBL ONE tutorial      | [![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/IBL_ONE_tutorial.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/IBL_ONE_tutorial.ipynb?flush_cache=true)            | 
+
+### References
 
-The Stringer datasets ([youtube](https://www.youtube.com/watch?v=78GSgf6Dkkk)) contain simultaneous recordings of 10,000 or 20,000 neurons from mouse visual cortex either during the presentation of gratings or during spontaneous behaviors like running, whisking and sniffing. These datasets are a little more advanced because you have to work with many neurons simultaneously. They are exciting, because they give a taste of what's to come in neuroscience.
+- International Brain Laboratory et al. (2023) A Brain-Wide Map of Neural Activity during Complex Behaviour doi: [10.1101/2023.07.04.547681]([https://doi.org/10.1101/2023.07.04.547681])
+- Findling et al. (2023) Brain-wide representations of prior information in mouse decision-making doi: [10.1101/2023.07.04.547684](https://doi.org/10.1101/2023.07.04.547684)
 
-Credit for data curation: Marius Pachitariu
+
+## Supervised and unsupervised learning 
+
+The [Zhong et al,2025](https://doi.org/10.1038/s41586-025-09180-y) dataset ([youtube](https://www.youtube.com/watch?v=78GSgf6Dkkk)) contains simultaneous recordings of up to 80,000 neurons from mouse visual cortex at different stages of visual learning in a virtual reality task with naturalistic images. It also contains recordings made during unsupervised exploration of the same virtual reality environments for comparisons, and recordings made after the introduction of novel stimuli that require behavioral classification.
+
+Credit for data curation: Lin Zhong and Marius Pachitariu
 
 |   | Run | View |
 | - | --- | ---- |
-| Orientation stimuli + running | [![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_stringer_orientations.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_stringer_orientations.ipynb?flush_cache=true) |
-| High-dimensional spontaneous behaviors | [![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_stringer_spontaneous.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_stringer_spontaneous.ipynb?flush_cache=true) |
+| Visual learning | [![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/visual_learning_80k_neurons.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/visual_learning_80k_neurons.ipynb?flush_cache=true) |
 
 ### References:
 
-- Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., and Harris, K. D. (2019). Spontaneous behaviors drive multidimensional, brainwide activity. Science, 364(6437): eaav7893. doi: [10.1126/science.aav7893](https://doi.org/10.1126/science.aav7893)
+- Zhong L, Baptista S, Gattoni R, Arnold J, Flickinger D, Stringer C and Pachitariu M. (2025) Unsupervised pretraining in biological neural networks. doi: [10.1038/s41586-025-09180-y](https://doi.org/10.1038/s41586-025-09180-y)
 
-- Stringer, C., Michaelos, M., Tsyboulski, D., Lindo, S. E., and Pachitariu, M. (2021). High-precision coding in visual cortex. Cell, 184(10): 2767-2778. doi: [10.1016/j.cell.2021.03.042](https://doi.org/10.1016/j.cell.2021.03.042)
+- Zhong L et al (2025). Figshare data repository. doi: [10.25378/janelia.28811129.v2](https://doi.org/10.25378/janelia.28811129.v2)
 
 ## Allen Institute
 
@@ -35,35 +51,20 @@ Credit for data curation: Marina Garret, Iryna Yavorska, Doug Ollerenshaw
 - 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]
 
 
-## International Brain Laboratory
-
-The International Brain Laboratory (IBL) brain-wide map dataset ([youtube](https://www.youtube.com/watch?v=N69nvrnmq9g)) includes data from 699 Neuropixels probe insertions across 281 brain regions, recorded during a standardized visual decision-making task. To help users get started, a dedicated project and step-by-step tutorial are available. For more advanced users, the IBL ONE tutorial demonstrates how to access the full range of IBL data using the Open Neurophysiology Environment (ONE) API, enabling deeper exploration and custom analyses across the entire dataset.
 
-Credit for data curation: Mayo Faulkner
-
-|                       | Run                                                                                                                                                                                                                    | View                                                                                                                                                                                                               |
-|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Analyze prepared 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/IBL_BWM_Neuromatch_tutorial.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/IBL_BWM_Neuromatch_tutorial.ipynb?flush_cache=true) |
-| IBL ONE tutorial      | [![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/IBL_ONE_tutorial.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/IBL_ONE_tutorial.ipynb?flush_cache=true)            | 
+# Project Templates
 
-### References
+Click on each image below to see a full browser version!
 
-- International Brain Laboratory et al. (2023) A Brain-Wide Map of Neural Activity during Complex Behaviour doi: [10.1101/2023.07.04.547681]([https://doi.org/10.1101/2023.07.04.547681])
-- Findling et al. (2023) Brain-wide representations of prior information in mouse decision-making doi: [10.1101/2023.07.04.547684](https://doi.org/10.1101/2023.07.04.547684)
+## Brain-wide map of neural activity during behaviour
 
+<img src="template_images/BrainwideMapIBL.svg" width="100%">
 
-# Project Templates
+## Supervised and unsupervised learning in recordings of 80,000 neurons
 
-Click on each image below to see a full browser version!
+<img src="template_images/SupervisedUnsupervisedLearning.svg" width="100%">
 
 ## Effect of stimulus context and behavior state on visual representations
 
 <img src="template_images/StimulusContextBehaviorState.svg" width="100%">
 
-## Behavior representations in mouse visual cortex
-
-<img src="template_images/MouseOrofacialBehaviors.svg" width="100%">
-
-## Brain-wide map of neural activity during complex behaviour
-
-<img src="template_images/BrainwideMapIBL.svg" width="100%">
diff --git a/projects/neurons/template_images/MouseOrofacialBehaviors.svg b/projects/neurons/template_images/MouseOrofacialBehaviors.svg
diff --git a/projects/neurons/template_images/SupervisedUnsupervisedLearning.svg b/projects/neurons/template_images/SupervisedUnsupervisedLearning.svg
diff --git a/projects/neurons/visual_learning_80k_neurons.ipynb b/projects/neurons/visual_learning_80k_neurons.ipynb
diff --git a/projects/project_materials.yml b/projects/project_materials.yml
@@ -1,15 +1,12 @@
-- title: Steinmetz Decisions
+- title: IBL NMA tutorial
   category: neurons
-  link: projects/neurons/load_steinmetz_decisions.ipynb
-- title: Steinmetz Extra
+  link: projects/neurons/IBL_BWM_Neuromatch_tutorial.ipynb
+- title: IBL extra
   category: neurons
-  link: projects/neurons/load_steinmetz_extra.ipynb
-- title: Stringer orientations
+  link: projects/neurons/IBL_ONE_tutorial.ipynb
+- title: Visual learning
   category: neurons
-  link: projects/neurons/load_stringer_orientations.ipynb
-- title: Stringer behaviors
-  category: neurons
-  link: projects/neurons/load_stringer_spontaneous.ipynb
+  link: projects/neurons/visual_learning_80k_neurons.ipynb
 - title: Allen Preprocessed
   category: neurons
   link: projects/neurons/load_Allen_Visual_Behavior_from_pre_processed_file.ipynb

diff --git a/tutorials/W2D1_ModelingPractice/W2D1_Tutorial1.ipynb b/tutorials/W2D1_ModelingPractice/W2D1_Tutorial1.ipynb
@@ -1103,7 +1103,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1fh411h7aX')]\n",
+    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1QHjWzVEy9')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",

diff --git a/tutorials/W2D1_ModelingPractice/instructor/W2D1_Tutorial1.ipynb b/tutorials/W2D1_ModelingPractice/instructor/W2D1_Tutorial1.ipynb
@@ -1103,7 +1103,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1fh411h7aX')]\n",
+    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1QHjWzVEy9')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",

diff --git a/tutorials/W2D1_ModelingPractice/student/W2D1_Tutorial1.ipynb b/tutorials/W2D1_ModelingPractice/student/W2D1_Tutorial1.ipynb
@@ -1103,7 +1103,7 @@
     "  return tab_contents\n",
     "\n",
     "\n",
-    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1fh411h7aX')]\n",
+    "video_ids = [('Youtube', '_umEOcDjqMA'), ('Bilibili', 'BV1QHjWzVEy9')]\n",
     "tab_contents = display_videos(video_ids, W=854, H=480)\n",
     "tabs = widgets.Tab()\n",
     "tabs.children = tab_contents\n",