Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Ching, ShiNung"'
Autor:
Vedovati, Giacomo, Ching, ShiNung
Understanding how brain networks learn and manage multiple tasks simultaneously is of interest in both neuroscience and artificial intelligence. In this regard, a recent research thread in theoretical neuroscience has focused on how recurrent neural
Externí odkaz:
http://arxiv.org/abs/2408.01316
Dynamical system models such as Recurrent Neural Networks (RNNs) have become increasingly popular as hypothesis-generating tools in scientific research. Evaluating the dynamics in such networks is key to understanding their learned generative mechani
Externí odkaz:
http://arxiv.org/abs/2402.09735
In this paper, we study recurrent neural networks in the presence of pairwise learning rules. We are specifically interested in how the attractor landscapes of such networks become altered as a function of the strength and nature (Hebbian vs. anti-He
Externí odkaz:
http://arxiv.org/abs/2312.14896
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes may play a more direct and act
Externí odkaz:
http://arxiv.org/abs/2311.03508
The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability. This fostered conceptual advances centered on the notion of reci
Externí odkaz:
http://arxiv.org/abs/2211.09906
Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we study represe
Externí odkaz:
http://arxiv.org/abs/2205.05820
In this paper, we study representation learning for multi-task decision-making in non-stationary environments. We consider the framework of sequential linear bandits, where the agent performs a series of tasks drawn from distinct sets associated with
Externí odkaz:
http://arxiv.org/abs/2201.04805
System identification poses a significant bottleneck to characterizing and controlling complex systems. This challenge is greatest when both the system states and parameters are not directly accessible leading to a dual-estimation problem. Current ap
Externí odkaz:
http://arxiv.org/abs/2104.02827
Autor:
Ghazizadeh, Elham, Ching, ShiNung
Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-l
Externí odkaz:
http://arxiv.org/abs/2101.03163