Unsupervised Discovery of Dynamic Neural Circuits
Abstract
What can we learn about the functional organization of cortical microcircuits from large-scale recordings of neural activity? To obtain an explicit and interpretable model of time-dependent functional connections between neurons and to establish the dynamics of the cortical information flow, we develop 'dynamic neural relational inference' (dNRI). We study both synthetic and real-world neural spiking data and demonstrate that the developed method is able to uncover the dynamic relations between neurons more reliably than existing baselines.
Cite
Text
Graber et al. "Unsupervised Discovery of Dynamic Neural Circuits." NeurIPS 2019 Workshops: Neuro_AI, 2019.Markdown
[Graber et al. "Unsupervised Discovery of Dynamic Neural Circuits." NeurIPS 2019 Workshops: Neuro_AI, 2019.](https://mlanthology.org/neuripsw/2019/graber2019neuripsw-unsupervised/)BibTeX
@inproceedings{graber2019neuripsw-unsupervised,
title = {{Unsupervised Discovery of Dynamic Neural Circuits}},
author = {Graber, Colin and Loh, Ryan and Vlasov, Yurii and Schwing, Alexander},
booktitle = {NeurIPS 2019 Workshops: Neuro_AI},
year = {2019},
url = {https://mlanthology.org/neuripsw/2019/graber2019neuripsw-unsupervised/}
}