Matthew Elliott Jupyter as a Brain Computer Interface | JupyterCon 2023

JupyterCon · Beginner ·📐 ML Fundamentals ·2y ago
We developed a Jupyter interface to conduct machine learning experiments on living human brain tissue. Our group, the Braingeneers, is a collaboration between neuroscientists, computational scientists, and engineers from University of California Santa Cruz, San Francisco, Santa Barbara, and Washington University St. Louis working to incorporate AI algorithms in the design of neuroscience experiments. We found Jupyter to be an indispensable tool that allows us to accomplish two key goals: 1. Rapidly design automated experiments on neurobiology 2. Share results with collaborators and students Over two years, our group has created a customized open-source Jupyter environment which we call WetAI, because its intention is to run AI algorithms with wet biology brain organoids. Brain organoids are sesame seed-sized brain-like tissues that are grown from stem cells in the lab, such that they can be induced to form neural circuits. We have used WetAI to control automated experiments that are conducted on human brain organoids. Code written in Jupyter implements an experimental protocol or learning algorithm that defines how a computer communicates with neurons. The WetAI portal provides experimenters with access to the brain-computer-interface and the other biotechnological devices that maintain tissue in optimal condition. Researchers can view a livestream of the tissue underneath a microscope and send drugs to the culture medium using Jupyter widgets. Our neuroscience experiments take place over multiple days, and this entire process is automated through Jupyter without a biologist needing to be present. We also used WetAI to teach programming and to provide data analytic tools to the general scientific community. WetAI was instrumental in teaching underrepresented high school students scientific coding in physically distant locations. Youtube tutorials were embedded into notebooks, and the Jupyter interface was customized to be tablet and smartphone friendly. We designe
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from JupyterCon · JupyterCon · 0 of 60

← Previous Next →
1 Interview   Joshua Patterson NVIDIA
Interview Joshua Patterson NVIDIA
JupyterCon
2 Dave Stuart - Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform | JupyterCon 2020
Dave Stuart - Jupyter as an Enterprise “Do It Yourself” (DIY) Analytic Platform | JupyterCon 2020
JupyterCon
3 Jeffrey Mew - Supercharge your Data Science workflow | JupyterCon 2020
Jeffrey Mew - Supercharge your Data Science workflow | JupyterCon 2020
JupyterCon
4 Michelle Ufford- Supercharging SQL Users with Jupyter Notebooks | JupyterCon 2020
Michelle Ufford- Supercharging SQL Users with Jupyter Notebooks | JupyterCon 2020
JupyterCon
5 Alan Yu - What we learned from introducing Jupyter Notebooks to the SQL community  | JupyterCon 2020
Alan Yu - What we learned from introducing Jupyter Notebooks to the SQL community | JupyterCon 2020
JupyterCon
6 Chris Holdgraf- 2i2c: sustaining open source through hosted Jupyter infrastructure | JupyterCon 2020
Chris Holdgraf- 2i2c: sustaining open source through hosted Jupyter infrastructure | JupyterCon 2020
JupyterCon
7 Yiwen Li - Intro to Elyra - an AI centric extension for JupyterLab | JupyterCon 2020
Yiwen Li - Intro to Elyra - an AI centric extension for JupyterLab | JupyterCon 2020
JupyterCon
8 Luciano Resende - What's new on Elyra - A set of AI centric JupyterLab extensions | JupyterCon 2020
Luciano Resende - What's new on Elyra - A set of AI centric JupyterLab extensions | JupyterCon 2020
JupyterCon
9 Alan Chin - Explore and Extend AI Pipeline Runtimes with Elyra and JupyterLab | JupyterCon 2020
Alan Chin - Explore and Extend AI Pipeline Runtimes with Elyra and JupyterLab | JupyterCon 2020
JupyterCon
10 Eduardo Blancas- Streamline your Data Science projects with Ploomber | JupyterCon 2020
Eduardo Blancas- Streamline your Data Science projects with Ploomber | JupyterCon 2020
JupyterCon
11 Thorin Tabor - Democratizing the accessibility of computational workflows | JupyterCon 2020
Thorin Tabor - Democratizing the accessibility of computational workflows | JupyterCon 2020
JupyterCon
12 Simon Willison- Using Datasette with Jupyter to publish your data | JupyterCon 2020
Simon Willison- Using Datasette with Jupyter to publish your data | JupyterCon 2020
JupyterCon
13 Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020
Brendan O'Brien - Using Qri (“query”) to fetch, query, combine and publish datasets.|JupyterCon 2020
JupyterCon
14 Georgiana Dolocan - Putting the JupyterHub puzzle pieces together | JupyterCon 2020
Georgiana Dolocan - Putting the JupyterHub puzzle pieces together | JupyterCon 2020
JupyterCon
15 Yuvi Panda- Running nonjupyter applications on JupyterHub with jupyter-server-proxy| JupyterCon 2020
Yuvi Panda- Running nonjupyter applications on JupyterHub with jupyter-server-proxy| JupyterCon 2020
JupyterCon
16 Richard Wagner- The Streetwise Guide to JupyterHub Security | JupyterCon 2020
Richard Wagner- The Streetwise Guide to JupyterHub Security | JupyterCon 2020
JupyterCon
17 TamNguyen- Handling Custom Jupyter Data Sources | JupyterCon 2020
TamNguyen- Handling Custom Jupyter Data Sources | JupyterCon 2020
JupyterCon
18 Immanuel Bayer- ipyannotator - the infinitely hackable annotation framework  | JupyterCon 2020
Immanuel Bayer- ipyannotator - the infinitely hackable annotation framework | JupyterCon 2020
JupyterCon
19 Rebecca Kelly- A shared Python, R and Q  Jupyter Notebook - A Quant Sandbox Dream |JupyterCon 2020
Rebecca Kelly- A shared Python, R and Q Jupyter Notebook - A Quant Sandbox Dream |JupyterCon 2020
JupyterCon
20 Itay Dafna - Leap of faith: Transitioning from Excel to Jupyter-based applications | JupyterCon 2020
Itay Dafna - Leap of faith: Transitioning from Excel to Jupyter-based applications | JupyterCon 2020
JupyterCon
21 Damián Avila - Using the Jupyterverse to power MADS | JupyterCon 2020
Damián Avila - Using the Jupyterverse to power MADS | JupyterCon 2020
JupyterCon
22 Chiin Rui Tan- From Zero to Hero | JupyterCon 2020
Chiin Rui Tan- From Zero to Hero | JupyterCon 2020
JupyterCon
23 Firas Moosvi- Teaching an Active Learning class with Jupyter Book| JupyterCon 2020
Firas Moosvi- Teaching an Active Learning class with Jupyter Book| JupyterCon 2020
JupyterCon
24 Daniel Mietchen- Jupyter in the Wikimedia ecosystem | JupyterCon 2020
Daniel Mietchen- Jupyter in the Wikimedia ecosystem | JupyterCon 2020
JupyterCon
25 Qiusheng Wu- How Jupyter and geemap enable interactive mapping and analysis | JupyterCon 2020
Qiusheng Wu- How Jupyter and geemap enable interactive mapping and analysis | JupyterCon 2020
JupyterCon
26 Stephanie Juneau- Jupyterenabled astrophysical analysis for researchers and students|JupyterCon 2020
Stephanie Juneau- Jupyterenabled astrophysical analysis for researchers and students|JupyterCon 2020
JupyterCon
27 Denton Gentry- The Care and Feeding of JupyterHub for Climate Solution Models| JupyterCon 2020
Denton Gentry- The Care and Feeding of JupyterHub for Climate Solution Models| JupyterCon 2020
JupyterCon
28 Tingkai Liu- FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era  | JupyterCon 2020
Tingkai Liu- FlyBrainLab: Interactive Computing in the Connectomic/Synaptomic Era | JupyterCon 2020
JupyterCon
29 Kunal Bhalla- A Notebook Style Guide| JupyterCon 2020
Kunal Bhalla- A Notebook Style Guide| JupyterCon 2020
JupyterCon
30 Julia Wagemann - How to avoid 'Death by Jupyter Notebooks' | JupyterCon 2020
Julia Wagemann - How to avoid 'Death by Jupyter Notebooks' | JupyterCon 2020
JupyterCon
31 David Pugh - Best practices for managing Jupyter-based data science  | JupyterCon 2020
David Pugh - Best practices for managing Jupyter-based data science | JupyterCon 2020
JupyterCon
32 Karla Spuldaro - Debugging notebooks and python scripts in JupyterLab | JupyterCon 2020
Karla Spuldaro - Debugging notebooks and python scripts in JupyterLab | JupyterCon 2020
JupyterCon
33 Shreyas Dalia - assert browserTest == True # Frontend Testing JupyterLab  | JupyterCon 2020
Shreyas Dalia - assert browserTest == True # Frontend Testing JupyterLab | JupyterCon 2020
JupyterCon
34 Chris Holdgraf - The new Jupyter Book stack | JupyterCon 2020
Chris Holdgraf - The new Jupyter Book stack | JupyterCon 2020
JupyterCon
35 Hamel Husain - Fastpages - A new, open source Jupyter notebook blogging system | JupyterCon 2020
Hamel Husain - Fastpages - A new, open source Jupyter notebook blogging system | JupyterCon 2020
JupyterCon
36 Marc Wouts - Jupytext: Jupyter Notebooks as Markdown Documents | JupyterCon 2020
Marc Wouts - Jupytext: Jupyter Notebooks as Markdown Documents | JupyterCon 2020
JupyterCon
37 Sheeba Samuel- ProvBook |JupyterCon 2020
Sheeba Samuel- ProvBook |JupyterCon 2020
JupyterCon
38 Philipp Rudiger - To Jupyter and back again | JupyterCon 2020
Philipp Rudiger - To Jupyter and back again | JupyterCon 2020
JupyterCon
39 Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
Jacob Tomlinson - What is my GPU doing? | JupyterCon 2020
JupyterCon
40 Afshin Darian - A visual debugger in Jupyter | JupyterCon 2020
Afshin Darian - A visual debugger in Jupyter | JupyterCon 2020
JupyterCon
41 Eric Charles - Jupyter Real Time Collaboration| JupyterCon 2020
Eric Charles - Jupyter Real Time Collaboration| JupyterCon 2020
JupyterCon
42 Devin Robison - Optimizing model performance | JupyterCon 2020
Devin Robison - Optimizing model performance | JupyterCon 2020
JupyterCon
43 Junhua zhao - PayPal Notebooks: ML & Data Science experience | JupyterCon 2020
Junhua zhao - PayPal Notebooks: ML & Data Science experience | JupyterCon 2020
JupyterCon
44 April Wang - Redesigning Notebooks for Better Collaboration | JupyterCon 2020
April Wang - Redesigning Notebooks for Better Collaboration | JupyterCon 2020
JupyterCon
45 Bryan Weber - Distributing and Collecting Jupyter Notebooks for Manual Grading| JupyterCon 2020
Bryan Weber - Distributing and Collecting Jupyter Notebooks for Manual Grading| JupyterCon 2020
JupyterCon
46 Georgiana Dolocan - The Littlest JupyterHub distribution | JupyterCon 2020
Georgiana Dolocan - The Littlest JupyterHub distribution | JupyterCon 2020
JupyterCon
47 Tim Metzler - Electronic Examination using Jupyter Notebook | JupyterCon 2020
Tim Metzler - Electronic Examination using Jupyter Notebook | JupyterCon 2020
JupyterCon
48 Blaine Mooers - Why develop a snippet library for Jupyter in your subject domain? | JupyterCon 2020
Blaine Mooers - Why develop a snippet library for Jupyter in your subject domain? | JupyterCon 2020
JupyterCon
49 Ryan Abernathey - Cloud Native Repositories for Big Scientific Data | JupyterCon 2020
Ryan Abernathey - Cloud Native Repositories for Big Scientific Data | JupyterCon 2020
JupyterCon
50 Tanya Rai - Introducing Bento: Jupyter Notebooks @ Facebook | JupyterCon 2020
Tanya Rai - Introducing Bento: Jupyter Notebooks @ Facebook | JupyterCon 2020
JupyterCon
51 Kenton McHenry - From Papers to Notebooks | JupyterCon 2020
Kenton McHenry - From Papers to Notebooks | JupyterCon 2020
JupyterCon
52 Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
JupyterCon
53 Ana Ruvalcaba - Community building is a sustainability strategy | JupyterCon 2020
Ana Ruvalcaba - Community building is a sustainability strategy | JupyterCon 2020
JupyterCon
54 Martin Renou - Xeus: an ecosystem of Jupyter kernels | JupyterCon 2020
Martin Renou - Xeus: an ecosystem of Jupyter kernels | JupyterCon 2020
JupyterCon
55 Michael Wilson - Teaching teenagers to understand Dark Energy | JupyterCon 2020
Michael Wilson - Teaching teenagers to understand Dark Energy | JupyterCon 2020
JupyterCon
56 Davide De Marchi - Voilà dashboards for policy support | JupyterCon 2020
Davide De Marchi - Voilà dashboards for policy support | JupyterCon 2020
JupyterCon
57 Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020
Marcos Lopez Caniego - ESASky's JupyterLab widget| JupyterCon 2020
JupyterCon
58 Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020
Praveen Kanamarlapud - Kernel Life Cycle Management | JupyterCon 2020
JupyterCon
59 Aaron Bray - Pulse Physiology Engine | JupyterCon 2020
Aaron Bray - Pulse Physiology Engine | JupyterCon 2020
JupyterCon
60 Aaron Watters - Using WebGL2 transform/feedback in Jupyter widgets | JupyterCon 2020
Aaron Watters - Using WebGL2 transform/feedback in Jupyter widgets | JupyterCon 2020
JupyterCon

Related AI Lessons

Up next
Debug Slow HTTP Requests in Python With httptap
Real Python
Watch →