Browse the search results

A generative-model-based, unsupervised learning toolbox for characterizing oscillatory bursting and brain network dynamics in univariate or multivariate time series.

A cortical model inspired by cognitive theories and deep learning demonstrates how the combination of wakefulness, non-rapid eye movement (NREM), and REM dreams leads to the emergence of robust and semantically organized neuronal representations in higher cortical areas.

Identification of causal genes and their effects on other biological determinants untangles the complexities of aging and Alzheimer's and can facilitate drug discovery for sustaining healthy aging and treating Alzheimer's.

A proposed model of optimal inference under cognitive costs accounts for human sequential effects, including subtle patterns of attractive and repulsive influence of past observations, in a binary prediction task across a wide range of stimulus conditions.

Humans are able to generate a complete category of varied objects from just one exemplar shape, identifying and utilizing its most distinctive parts to create a coherent group, even for other observers.

Prediction of future input explains diverse neural tuning properties in sensory cortex.

The dependence of incentive value attribution on the anticipation of rewards within a given context is explained via a normative Bayesian account of how rewards map to incentive values.

Computational analysis reveals that additive summary statistics from genome-wide association studies can provide evidence of non-additive heritability in complex traits.

The P300, an electroencephalography (EEG) component known to be evoked by surprising events, predicts learning in a bidirectional manner that depends critically on the surrounding statistical context.

Behavioral and computational results show that the perception of our body as our own depends on Bayesian probabilistic reasoning that take into account the variations in sensory uncertainty when integrating visual and somatosensory cues.