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180 papers
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.
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...
URCHIN (Unified Recurrent Connectome with Horizontal Integrate-and-fire Neurons), which applies the Parallelized Hierarchical Connectome Spiking State-space Model (PHCSSM) to language modeling and offers a biologically plausible, efficient, and directly deployable reference point.
The Jacobian matrices describing the dynamics near these equilibria are investigated and it is shown that they are sparse, non-Hermitian rectangular-block matrices modified by heterogeneous synaptic decay timescales and activation-function gains.
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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.
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.
It is shown that a hippocampal network with appropriate recurrent connections can naturally achieve optimal goal-directed navigation via its relaxation dynamics, and it is proved that this field is mathematically equivalent to the desirability field of a Linearly-solvable Markov Decision Process (LMDP).
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.
This work formalizes compatible parameter sets as \emph{viable parameter manifolds}: the inverse images of target dynamical features under a parameter-to-feature map, organized as inverse geometry, with diffusion models providing practical tools for sampling, visualizing, and interrogating that geometry.
It is suggested that the pulling illusion depends on perceptual processing beyond basic vibration detection, through which asymmetric vibration is experienced as directional pulling.
The Platonic brain bridge hypothesis is proposed: omni models, multimodal large language models that process video, audio and text jointly, converge on brain-like representations usable in both directions, and human brain organization is a usable architectural prior for multimodal large language models.
EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification, is presented and results support EEG-centric multimodal binding as a practical strategy for source-level IED classification.
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