Category
134 papers
This work introduces a symbolic distillation pipeline that converts trained neural likelihoods into explicit, interpretable expressions optimized for efficient parameter estimation, and uses a recovery-directed objective to guide symbolic regression toward expressions that preserve parameter-recovery accuracy.
Transformers perform computations through many successive attention and feedforward blocks, allowing them to learn complex correlations between a large collection of coupled variables. When does stacking successive attention-feedforward computations over many layers provide a computational advantage over a single trans...
A new model family (CortexMAE) trained using the masked autoencoder framework is introduced, and in this setting the CortexMAE family outperforms prior models by a large margin, and the first open evaluation suite (Brainmarks) for fMRI foundation models is released.
A computational-theoretical framework is established to provide an account of psychopathology applicable to LLMs and experiments suggest that network-theoretic computations of psychopathology may have emerged in LLMs.
Reach audiences
Advertise in front of researchers, engineers, and readers.
An explainable deep learning framework based on sparse projected residual networks to predict fluid, crystallized, and total intelligence from resting-state functional magnetic resonance imaging in 5,285 participants from the Adolescent Brain Cognitive Development study suggests that intelligence emerges from the inter...
Reliable decisions depend on recognizing when an answer may be wrong. In biological cognition, metacognitive monitoring can dissociate from task performance, raising the question of how closely solving and judging are linked in language models. Here we study the confidence reports of four frontier models across 15 benc...
This work proposes a model that builds on the presence of a certain network scaffold at the onset of learning and the existence of dendritic compartments for enhancing neuronal information storage and computation and builds on these features to acquire and replay complex non-Markovian spatio-temporal patterns using onl...
Research on artificial consciousness increasingly shifts evaluation from behaviour to internal architecture. Theory-based indicators are used to update probability assignments. This improves on behavioural tests but raises two distinct problems. First, these assignments cannot currently be calibrated against independen...
In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or an overly salient memory. Motivated by control theory, we develop an AI-driven neural-surrogate framework that proposes candidate representational changes and tests their...
The results show that appropriate receptive fields can serve as a computational prior beyond sparsity itself, and that their value depends on the computational expressivity of individual neurons.
This work proposes a latent functional alignment (LFA) approach that maps imagery-evoked activity to the pretrained model's semantic content-enriched conditioning space, by adding a simple alignment module, while keeping the original remaining components frozen.
This work charts this field around four challenges: representing experiments, generating synthetic behavior, synthesizing models, and closing the loop to discover psychological theories.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
