Stimulation-mediated reverse engineering of silent neural networks
Abstract
We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2023
- Source ID
- 10.1152/jn.00100.2023
Entities
People
- Adam Vareberg
- Aviad Hai
- Ilhan Bok
- Xiaoxuan Ren
Organizations
- National Institute of Neurological Disorders and Stroke
- Office of Naval Research
- Office of Naval Research Global
- University of Wisconsin–Madison