November 27, 2025
By Thomas Wiecki
Digital therapeutics are evidence-based, clinically evaluated software and devices that can be used to treat an array of diseases and disorders, according to the Digital Therapeutics Alliance, the industry's trade association. They can be used independently or with medications, devices, and other therapies to treat physical and behavioral health conditions, including pain, diabetes, anxiety, post-traumatic stress disorder, and asthma.
In this talk, PyMC Labs and Akili discuss using Bayesian methods and PyMC to test a range of computational models of cognition, specifically with an eye towards ADHD (Attention-deficit/hyperactivity disorder). They focus on some technical challenges and how ideas from likelihood-free inference and machine learning can help overcome them.
00:00 Thomas Wiecki introduction
01:27 Alex introduction
01:51 Titi introduction
02:55 Andy introduction
03:42 Akili background
05:11 EndeavorRx by Akili and PyMC's involvement
09:49 Likelihood approximation networks in PyMC
10:15 NeuroRacer
15:44 Two important aspects of the Model
20:39 Inference with model variants
21:05 Inference from access to simulators
21:55 Inference with models
22:56 Training
24:06 Previous toolbox: HDDM
24:59 Properties inherited from Neural Networks
26:31 Graphical representation of model in PyMC
29:42 Code in PyMC
30:07 Neural network (LAN, CPN)
31:17 Proof of concept (Parameter Recovery)
31:42 Proof of concept (Speed)
32:41 Thomas question on speed
36:11 Thomas on Before PyMC vs after PyMC
36:32 Titi on before PyMC vs after PyMC
38:11 Andy on production use case
39:00 Thomas question on application
39:16 Andy explains the use case and the application
40:28 Titi on impact in applications
41:15 Thomas on Knowledge transfer to Akili research team and collaboration
43:02 Andy on working with PyMC team
44:55 Thomas question to Alex on applying this method to other applications across industries
47:15 Why does Akili care about these kinds of models ?
49:31 PyMC's work and impact towards Akili's mission
51:04 Audience Q/A (What other conditions can this be applied other than ADHD?)
52:03 Audience Q/A (Is Data enough to conduct experiments ?)
56:32 Closed form solution vs Neural Networks
56:52 Optimizing LAN for faster forward pass, primary metric and designing networks