A Factorized Low-Rank RNN Framework for Uncovering Independent Neural Latent Dynamics and Connectivity
arXiv:2511.13899v2 Announce Type: replace-cross Abstract: Low-rank recurrent neural networks (lrRNNs) are a class of models that uncover low-dimensional latent dynamics underlying neural population activity. Although their functional connectivity is low-rank, it lacks independence interpretations, making it difficult to assign distinct computational roles to different latent dimensions. To address this, we propose the Factored Recurrent Neural Network (FacRNN), a generative lrRNN framework that assumes group-wise independence among latent dynamics while allowing flexible within-group entanglem
Ongoing research in AI and neuroscience continually aims to develop more sophisticated models that can better understand and replicate complex brain functions, moving beyond current limitations of recurrent neural networks.
Improved models for understanding and factorizing neural dynamics could lead to significant advancements in AI, brain-computer interfaces, and the development of more biologically plausible artificial intelligence.
The proposed Factored Recurrent Neural Network (FacRNN) introduces a method to assign distinct computational roles to latent dimensions, addressing a key limitation in current low-rank RNN frameworks.
- · AI researchers
- · Computational neuroscience
- · Biomedical engineering
- · AI model developers
- · Developers of less interpretable black-box models
- · Current low-rank RNN frameworks (by obsolescence)
Enhanced interpretability in AI models through better understanding of latent dynamics.
Accelerated development of more efficient and specialized AI agents capable of higher-level cognitive functions.
Potential for breakthroughs in understanding consciousness and replicating it in artificial systems.
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Read at arXiv cs.LG