7 papers
CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots
Keisuke Kawano, Takuro Kutsuna, Naoki Hayashi +2
In many real-world settings--e.g., single-cell RNA sequencing, mobility sensing, and environmental monitoring--data are observed only as temporally aggregated snapshots collected o…
A ghost mechanism: An analytical model of abrupt learning in recurrent networks
Fatih Dinc, Ege Cirakman, Bariscan Kurtkaya +4
Abrupt learning is a common phenomenon in recurrent neural networks (RNNs) trained on working memory tasks. In such cases, the networks develop transient slow regions in state spac…
Provable Low-Frequency Bias of In-Context Learning of Representations
Yongyi Yang, Hidenori Tanaka, Wei Hu
In-context learning (ICL) enables large language models (LLMs) to acquire new behaviors from the input sequence alone without any parameter updates. Recent studies have shown that…
Dynamical phases of short-term memory mechanisms in RNNs
Bariscan Kurtkaya, Fatih Dinc, Mert Yuksekgonul +7
Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. Neuroscience has long focused on how sequential activity pa…
Latent computing by biological neural networks: A dynamical systems framework
Fatih Dinc, Marta Blanco-Pozo, David Klindt +8
Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable acr…
Understanding and controlling the geometry of memory organization in RNNs
Udith Haputhanthri, Liam Storan, Yiqi Jiang +7
Training recurrent neural networks (RNNs) is a high-dimensional process that requires updating numerous parameters. Therefore, it is often difficult to pinpoint the underlying lear…