Update course book

NeuromatchAcademy · Jul 1, 2024 · 7a68a92 · 7a68a92
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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 6e3d29a8d5141c3cb97024845583fd8a
+tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/CNAME b/CNAME
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+chatify.compneuro.neuromatch.io
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diff --git a/_sources/prereqs/ComputationalNeuroscience.md b/_sources/prereqs/ComputationalNeuroscience.md
@@ -0,0 +1,37 @@
+# Prerequisites and preparatory materials for NMA Computational Neuroscience
+
+Welcome to the [Neuromatch Academy](https://neuromatch.io/computational-neuroscience-course/)! We're really excited to bring computational neuroscience to such a wide and varied audience. We're preparing an amazing set of lectures and tutorials for you!
+
+## Preparing yourself for the course
+
+People are coming to this course from a wide range of disciplines and with varying levels of background, and we want to make sure everybody is able to follow and enjoy the school from day 1. This means you need to know the basics of programming in Python, some core math concepts, and some exposure to neuroscience. Below we provide more details.
+
+### Programming
+
+This course will be run using Python. If you've never programmed in Python, now is a good time to start practicing! We expect students to be familiar with variables, lists, dicts, the numpy and scipy libraries as well as plotting in matplotlib. Practice a little bit every day and you'll be in great shape by the time the class starts. 
+
+We have NMA Python workshop materials (W0D1 and W0D2 [here](https://compneuro.neuromatch.io/)). You should go through this NMA-made content at your own pace before the course.  
+
+Besides these NMA materials, we recommend the [Software carpentry 1-day Python tutorial](https://swcarpentry.github.io/python-novice-inflammation/) or the free Edx course [Using Python for Research](https://www.edx.org/course/using-python-for-research). For a more in-depth intro, see the [scipy lecture notes](https://scipy-lectures.org/). Finally, you can follow the [Python data science handbook](https://jakevdp.github.io/PythonDataScienceHandbook/), which also has a print edition. 
+
+If you're coming from a Matlab background, you can quickly get up to speed with [this cheatsheet](https://cheatsheets.quantecon.org/). You may also enjoy [this paperback](https://www.worldcat.org/title/neural-data-science-a-primer-with-matlab-and-python/oclc/973932708) on Neural Data Science with both Matlab and Python versions.
+
+### Math skills
+
+Computational neuroscience and neural data analysis relies on linear algebra, probability, basic statistics, and calculus (derivates and ODEs).
+
+We highly recommend going through our refreshers on linear algebra, calculus, and statistics (W0D3, W0D4, W0D5 [here](https://compneuro.neuromatch.io/)). You will be able to ask questions on discord before the course starts. 
+
+**Linear algebra:** You will need a good grasp of linear algebra to follow along, as linear algebra is crucial for almost anything quantitative involving more than one number at a time. You need to know vector and matrix addition and multiplication, rank, bases, determinants, inverses, and eigenvalue decomposition. In addition to our W0D3, we highly recommend this beautiful [lecture series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab). Another great resource is [Khan academy](https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/vectors/v/vector-introduction-linear-algebra).  Here is a series of exercises on [linear algebra in Python](https://www.w3resource.com/python-exercises/numpy/linear-algebra/index.php).
+
+**Statistics:** Understanding statistics is also important; you should be comfortable with means and variances, and the normal distribution. In addition to our W0D4, for a refresher, we recommend selective readings (i.e. chapters 6-7 from [Russ Poldrack's book "Statistical thinking of the 21st century"](https://statsthinking21.github.io/statsthinking21-core-site/).
+
+**Calculus:** Finally, basic calculus is crucial; you should know what integrals and derivatives are, and understand what a differential equation means. If you need to refresh your memory on differential and integral calculus, [Gilbert Strang's book](https://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf) is a good refreshment book. For differential equations, we recommend studying chapter 0-1 (including exercises!) of Jiri Lebl's book ["Differential equations for engineers"](https://www.jirka.org/diffyqs/).
+
+### Neuroscience
+
+If you're coming from outside neuroscience, it'll be great to familiarize yourself with fundamental concepts. We highly recommend watching our NMA neuro video series before the course (W0D0 [here](https://compneuro.neuromatch.io/)). Here is a [short read on the subject](https://www.bna.org.uk/static/uploads/resources/BNA_English.pdf). Here is another resource from [the Brain Facts book by Society For Neuroscience](https://www.brainfacts.org/the-brain-facts-book).
+
+We're so excited to have you here! Looking forward to meeting you soon,
+
+The Neuromatch Academy team.
diff --git a/_sources/projects/ECoG/ECoG_videos.ipynb b/_sources/projects/ECoG/ECoG_videos.ipynb
@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "execution": {},
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/main/projects/ECoG/ECoG_videos.ipynb\" target=\"_blank\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a>   <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/projects/ECoG/ECoG_videos.ipynb\" target=\"_blank\"><img alt=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {},
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Overview videos\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "##  TED talk:   Kai Miller (watch until 15:45)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "ECoG_videos",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernel": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/_sources/projects/ECoG/README.md b/_sources/projects/ECoG/README.md
@@ -0,0 +1,59 @@
+# Guide to choosing an EEG/ECoG/LFP dataset
+
+*July 5-23, 2021*
+
+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.
+
+* 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.
+
+* Students can choose one dataset based on their particular interest (sensory / motor / memory / BCI).
+
+* For slightly more advanced groups, you should definitely consider the LFPs from the Steinmetz dataset, which are much better suited for exploratory analyses and a wide diversity topics. They are also better for computational projects, because they provide high-dimensional data (lots of neurons) with lots of trials, and they are well supported at NMA, because the Steinmetz dataset has been well curated and annotated in general.  
+
+Credit for data curation: Marius Pachitariu and the project TAs
+
+|   | Run | View |
+| - | --- | ---- |
+| FacesHouses | [![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/load_ECoG_faceshouses.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/load_ECoG_faceshouses.ipynb?flush_cache=true) |
+| FingerFlex | [![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/load_ECoG_fingerflex.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/load_ECoG_fingerflex.ipynb?flush_cache=true) |
+| JoystickTrack | [![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/load_ECoG_joystick_track.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/load_ECoG_joystick_track.ipynb?flush_cache=true) |
+| MemoryNback | [![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/load_ECoG_memory_nback.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/load_ECoG_memory_nback.ipynb?flush_cache=true) |
+| MotorImagery | [![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/load_ECoG_motor_imagery.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/load_ECoG_motor_imagery.ipynb?flush_cache=true) |
+| ExploreAJILE12 | [![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) |
+
+## References:
+
+### Faces/Houses:
+
+- Miller, K. J., Hermes, D., Pestilli, F., Wig, G. S., and Ojemann, J. G. (2017). Face percept formation in human ventral temporal cortex. Journal of neurophysiology 118(5): 2614-2627. doi: [10.1152/jn.00113.2017](https://doi.org/10.1152/jn.00113.2017)
+
+- Miller, K. J., Hermes, D., Witthoft, N., Rao, R. P., and Ojemann, J. G. (2015). The physiology of perception in human temporal lobe is specialized for contextual novelty. Journal of neurophysiology 114(1): 256-263. doi: [10.1152%2Fjn.00131.2015](https://doi.org/10.1152%2Fjn.00131.2015)
+
+- Miller, K. J., Schalk, G., Hermes, D., Ojemann, J. G., and Rao, R. P. (2016). Spontaneous decoding of the timing and content of human object perception from cortical surface recordings reveals complementary information in the event-related potential and broadband spectral change. PLoS computational biology 12(1): e1004660. doi: [10.1371/journal.pcbi.1004660](https://doi.org/10.1371/journal.pcbi.1004660)
+
+### Fingerflex:
+
+- Miller, K. J., Zanos, S., Fetz, E. E., Den Nijs, M., & Ojemann, J. G. (2009). Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans. Journal of Neuroscience 29(10): 3132-3137. doi: [10.1523%2FJNEUROSCI.5506-08.2009](https://doi.org/10.1523%2FJNEUROSCI.5506-08.2009)
+
+- Miller, K. J., Hermes, D., Honey, C. J., Hebb, A. O., Ramsey, N. F., Knight, R. T., ... and Fetz, E. E. (2012). Human motor cortical activity is selectively phase-entrained on underlying rhythms. PLoS computational biology: e1002655. doi: [10.1371/journal.pcbi.1002655](https://doi.org/10.1371/journal.pcbi.1002655)
+
+### Joystick track:
+
+- Schalk, G., Kubanek, J., Miller, K. J., Anderson, N. R., Leuthardt, E. C., Ojemann, J. G., ... and Wolpaw, J. R. (2007). Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. Journal of neural engineering 4(3): 264-275. doi: [0.1088/1741-2560/4/3/012](https://doi.org/10.1088/1741-2560/4/3/012)
+
+- Schalk, G., Miller, K. J., Anderson, N. R., Wilson, J. A., Smyth, M. D., Ojemann, J. G., ... and Leuthardt, E. C. (2008). Two-dimensional movement control using electrocorticographic signals in humans. Journal of neural engineering 5(1): 75-84. doi: [10.1088/1741-2560/5/1/008](https://doi.org/10.1088/1741-2560/5/1/008)
+
+### Memory nback (no direct references but see)
+
+- Brouwer, A. M., Hogervorst, M. A., Van Erp, J. B., Heffelaar, T., Zimmerman, P. H., and Oostenveld, R. (2012). Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of neural engineering 9(4): 045008. doi: [10.1088/1741-2560/9/4/045008](https://doi.org/10.1088/1741-2560/9/4/045008)
+
+- Grissmann, S., Faller, J., Scharinger, C., Spüler, M., and Gerjets, P. (2017). Electroencephalography based analysis of working memory load and affective valence in an n-back task with emotional stimuli. Frontiers in human neuroscience 11: 616. doi: [10.3389%2Ffnhum.2017.00616](https://doi.org/10.3389%2Ffnhum.2017.00616)
+
+### Motor imagery:
+
+- 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)
+
+### Exploring AJILE12 dataset:
+
+- 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)
+- 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)
diff --git a/_sources/projects/README.md b/_sources/projects/README.md
@@ -0,0 +1,5 @@
+# Projects
+
+----
+
+See the [Daily Guide to Projects](./docs/project_guidance.md).
diff --git a/_sources/projects/behavior/README.md b/_sources/projects/behavior/README.md
@@ -0,0 +1,38 @@
+# Guide to choosing a Behavior dataset
+
+*July 5-23, 2021*
+
+Everyone should consider the behavior-only datasets that we have, which are very rich with many subjects and many trials.
+
+## Caltech
+
+The Caltech dataset ([youtube](https://youtube.com/watch?v=tDmhmasjPeM)) has pose-tracking data from socially-interacting mice, and is well supported with code and a project template.
+
+Credit for data curation: Ann Kennedy
+
+|   | Run | View |
+| - | --- | ---- |
+| Loader notebook | [![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/Loading_CalMS21_data.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/Loading_CalMS21_data.ipynb?flush_cache=true) |
+
+## IBL
+
+The IBL dataset ([youtube](https://youtube.com/watch?v=NofrFH8FRZU)) is a visual decision-making task, that is very similar to the one used in the Steinmetz dataset.
+
+Credit for data curation: Eric DeWitt and the IBL team
+
+|   | Run | View |
+| - | --- | ---- |
+| Exploration notebook | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/int-brain-lab/nma-ibl/blob/master/01-Explore%20IBL%20behavior%20data%20pipeline.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/int-brain-lab/nma-ibl/blob/master/01-Explore%20IBL%20behavior%20data%20pipeline.ipynb?flush_cache=true) |
+| Psychometric notebook | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/int-brain-lab/nma-ibl/blob/master/02-Plot%20Psychometric%20curve.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/int-brain-lab/nma-ibl/blob/master/02-Plot%20Psychometric%20curve.ipynb?flush_cache=true) |
+| Additional analyses | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/int-brain-lab/nma-ibl/blob/master/03-Replication%20of%20paper%20figures.ipynb) | [![View the notebook](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/int-brain-lab/nma-ibl/blob/master/03-Replication%20of%20paper%20figures.ipynb?flush_cache=true) |
+
+## Laquitaine & Gardner, Neuron, 2017
+
+The Laquitaine & Gardner dataset contains behavioral data collected in a motion direction estimation task performed by 12 human subjects. The data include motion direction estimates, reaction times and various task variables recorded for 83,214 trials. The authors varied the strength of sensory evidence (motion coherence) and priors (set of directions) and compared the trial-estimate distribution with the predictions of Bayesian observer models.
+
+|   | Run | View |
+| - | --- | ---- |
+| Loader & Exploration notebook | [![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_human_errors.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_human_errors.ipynb) |
+| 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) |
+
+