Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (EEG) features contribute to their characte...
Yuwen Zeng, Dengzhe Hou, Zhang Zhang et al.· 0 citations
It is shown that for every approximation bound that is valid for a set of multi-spike neural networks, there is an equivalent set of single-spike neural networks with only linearly more (or less) neurons, in the maximum number of spikes, for which the bound holds.
Dominik Dold, Philipp C. Petersen· arXiv.org· 2 citations
Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of a shared recurrent network, but leaves open how such a system is learned. We translate this principle into a novel architecture for learnin...
Sreejan Kumar, M. Mattar, Lea Duncker· 0 citations
This paper proposes that the mind pursues goals with closed control loops and emotions are recognized patterns of cognitive operations in the control loops and advances the building of a more accurate theory of emotions that deepens the understanding of human minds and accelerates the construction of artificial minds.
Yue Jin· 0 citations
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Despite their widespread use, the principles governing the organisation of syntactic dependency trees remain poorly understood. I analyse dependency trees from 124 typologically, genetically, and geographically diverse languages. Their topology departs systematically from randomness. Relative to uniformly sampled rando...
The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora, yet prior language models forgo the biological constraints of the neural circuitry that acquires human language: spiking neurons separated into excitatory and inhibitory...
Training recurrent neuronal networks consisting of excitatory (E) and inhibitory (I) units with additive noise for working memory computation slows and diversifies inhibitory timescales, leading to improved task performance that is attributed to emergent marginally stable equilibria [PNAS 122 (2025) e2316745122]. Yet t...
This work proposes MAPA, an otherwise vanilla masked autoencoder with two spatial encodings, an anatomical region embedding and a relative positional encoding that together enable it to learn neural representations that transfer to unseen subjects and across various tasks.
Ben-Ting Tang, Z. Spalding, G. Cogan· arXiv.org· 0 citations
Perceptual Reality Transformer is introduced, an evidence-linked workflow and descriptive atlas that retain the source account alongside representations and generated illustrations that retain the source account alongside representations and generated illustrations.
Neural correlates of spatial cognitive map are well documented, yet exactly how neural circuits perform spatial navigation in complex environments - e.g., reaching a goal while avoiding obstacles - remains largely unclear. Here, we show that a hippocampal network with appropriate recurrent connections can naturally ach...
Yu-Hang He, Jun-Feng Zuo, Tian-Hao Chu et al.· 0 citations
It is argued that while the problem of finding a meaning-preserving compression is computationally hard in the worst case, there exist efficient algorithms which achieve near optimal performance in the typical case.
Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables; consequently, similar activity can emerge from coordinated parameter changes. We formalize these compatible parameter sets as \emph{viable parameter manifolds}: the inverse images of target dyna...
Ruilin Zhang, Louis Tao, Zhuo-Cheng Xiao· 0 citations
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