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Bonus Material: Dynamical similarity analysis (DSA)#

Week 1, Day 3: Comparing Artificial And Biological Networks

By Neuromatch Academy

Content creators: Mitchell Ostrow

Content reviewers: Xaq Pitkow, Hlib Solodzhuk

Production editors: Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Patrick Mineault

This short notebook expands the toolset of network comparison by taking a look at another important dimension for analysis - time. In particular, it would be beneficial to understand how the systems evolve over time and whether their dynamics are similar. The presented materials are the most similar to the ones introduced in Tutorial 2 for this day, and one of the projects on Comparing Networks is exactly about DSA.

Install and import feedback gadget#

Hide code cell source 
# @title Install and import feedback gadget

!pip install vibecheck --quiet

from vibecheck import DatatopsContentReviewContainer
def content_review(notebook_section: str):
    return DatatopsContentReviewContainer(
        "",  # No text prompt
        notebook_section,
        {
            "url": "https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab",
            "name": "neuromatch_neuroai",
            "user_key": "wb2cxze8",
        },
    ).render()


feedback_prefix = "W1D3_Bonus"

Bonus material slides#

Video 1: Dynamical Similarity Analysis#

Submit your feedback#

Hide code cell source 
# @title Submit your feedback
content_review(f"{feedback_prefix}_DSA_video")

⚠ Experimental LLM-enhanced tutorial ⚠

This notebook includes Neuromatch’s experimental Chatify 🤖 functionality. The Chatify notebook extension adds support for a large language model-based “coding tutor” to the course materials. The tutor provides automatically generated text to help explain any code cell in this notebook.

Note that using Chatify may cause breaking changes and/or provide incorrect or misleading information. If you wish to proceed by installing and enabling the Chatify extension, you should run the next two code blocks (hidden by default). If you do not want to use this experimental version of the Neuromatch materials, please use the stable materials instead.

To use the Chatify helper, insert the %%explain magic command at the start of any code cell and then run it (shift + enter) to access an interface for receiving LLM-based assitance. You can then select different options from the dropdown menus depending on what sort of assitance you want. Press the Submit button to generate a response. To disable Chatify and run the code block as usual, simply delete the %%explain command and re-run the cell.

Thanks for giving Chatify a try! Love it? Hate it? Either way, we’d love to hear from you about your Chatify experience! Please consider filling out our brief survey to provide feedback and help us make Chatify more awesome!

Run the next two cells to install and configure Chatify…

%pip install -q davos
import davos
davos.config.suppress_stdout = True

Note: you may need to restart the kernel to use updated packages.

smuggle chatify      # pip: git+https://github.com/ContextLab/chatify.git
%load_ext chatify