# Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

arXiv: https://arxiv.org/abs/2305.15385 · HTML: https://mlsanity.com/paper/2305.15385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 401 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 16 of 16)

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- [AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks](https://mlsanity.com/paper/1902.09689/) (2019)
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- [Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness](https://mlsanity.com/paper/2104.05942/) (2021)
- [The Dimpled Manifold Model of Adversarial Examples in Machine Learning](https://mlsanity.com/paper/2106.10151/) (2021)
- [Py-Feat: Python Facial Expression Analysis Toolbox](https://mlsanity.com/paper/2104.03509/) (2021)
- [Winning the lottery with neural connectivity constraints: faster learning across cognitive tasks with spatially constrained sparse RNNs](https://mlsanity.com/paper/2207.03523/) (2022)
- [Generalization in Supervised Learning Through Riemannian Contraction](https://mlsanity.com/paper/2201.06656/) (2022)
- [Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning](https://mlsanity.com/paper/2204.05382/) (2022)
- [Contraction and Robustness of Continuous Time Primal-Dual Dynamics](https://mlsanity.com/paper/1803.05975/) (2018)
- [Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems](https://mlsanity.com/paper/2103.11766/) (2021)
- [Distributed online estimation of biophysical neural networks](https://mlsanity.com/paper/2204.01472/) (2022)

## Built on by (most cited first, 1 of 1)

- [Contraction Properties of the Global Workspace Primitive](https://mlsanity.com/paper/2310.01571/) (2023)

## Abstract (arXiv)

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.