How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, dynamical motifs, or local perturbation growth. W...
The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.
arXiv:2609.38598v1 Announce Type: new Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from o...
The paper introduces Recursive Quadrature Filters (RQFs), complex‑valued temporal filters that act as band‑pass filters within diagonal state‑space models. By making each layer’s bottom‑up input prospective through a parameter‑free two‑tap update, the authors mitigate depth‑dependent gradient attenuation in deep continuous‑time recurrent networks. Experiments on RQFs, S5, and ORGaNICs show that prospective variants match or surpass non‑prospective controls, achieving high accuracy on raw‑audio Speech Commands and the Path‑X task with few parameters.
arXiv:2601. 19019v3 Announce Type: replace-cross Abstract: Neural population activity in sensory cortex is organized on low-dimensional manifolds, but why such manifolds arise and what determines their geometry remain unclear.
arXiv:2602. 18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware.
arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.
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.
Dynamic Compression in Recurrent Networks proposes a method that lets recurrent models revisit and revise their fixed-size state through additional updates, rather than compressing all information in a single causal pass. This approach allows the model to retain lower-fidelity history and refine only the relevant parts when needed, reducing the required state size for accurate task reuse. Experiments show that dynamic compression lowers the recurrent state needed and scales better as the number of stored functions increases.
arXiv:2609.39892v1 Announce Type: new Abstract: Looped Transformers repeatedly apply the same set of Transformer layers, giving them a recurrent architecture for latent computation. Their strong perf...
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
Backpropagation is the computational engine of deep learning, yet its mathematical structure is typically treated as a procedural traversal of computational graphs. We present a global operator theory of the \emph{F-adjoint} framework, which reformulates the layerwise backward recursion of an $L$-depth feedforward network into a single linear system $(I-\cB)\Xs=\bG$, where $\bG$ is a source vector.