Work

On the control of recurrent neural networks using constant inputs.

Year: 2025

Type: preprint

Abstract: nodes, this implies that the reachable set (with constant inputs) of a given initial state is an affine subspace whose dimension equals the input rank and whose basis can be computed efficiently using a thin QR factorization. Numerical simulations illustrate the theoretical results and demonstrate the effectiveness of the proposed synthesis in guiding the design of brain stimulation protocols for therapeutic and cognitive applications.

Source: PubMed

DOI: 10.1109/tac.2025.3615934

Authors: Cyprien Tamekue, Ruiqi Chen, ShiNung Ching

Institution: Washington University in St. Louis

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Institute of Mental Health

Award: Cognitive enhancement through model-based and individualized neurostimulation

OpenAlex ID: W4404122347

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