arXiv:2606. 10530v1 Announce Type: cross Abstract: Recent developments in brain recording are driving a demand for machine learning tools capable of decoding the latent structure of large populations of neurons.
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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.
The paper introduces a variational framework called VAMO that incorporates latent Markov dynamics for neural PDE solvers, aiming to improve long‑horizon predictions by mitigating error accumulation. By representing physical states as latent distributions and evolving them through probabilistic transitions, the method aligns learned dynamics with a spectral geometry induced by structured Gaussian perturbations. Experiments on fluid‑dynamics benchmarks show that VAMO reduces error growth and enhances rollout stability compared to deterministic and noise‑injection baselines.
The paper introduces the Recurrent Divisive Normalization Network (RDNN), a minimal model that incorporates divisive normalization—a common neural computation—to stabilize continuous working memory representations. Dynamical systems analysis shows that this biophysical constraint enables the network to converge to robust, high‑fidelity slow manifolds, while gradient dynamics during Backpropagation Through Time reveal an activity‑dependent local scaling that compresses the network’s effective rank into a low‑dimensional subspace. Ablation studies confirm that divisive normalization, rather than subtractive inhibition, is essential for preventing manifold shattering under time‑varying inputs.
arXiv:2610.01786v1 Announce Type: cross Abstract: Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions. Identifying these timescales from neural...
arXiv:2607. 10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity.
Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can induce distribution s...