Disentangling Computation in Multi-Task Neural Networks with the Green's Operator
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.