Topic: Variational Autoencoders

Subcallosal cingulate stimulation for resistant depression needs objective biomarkers beyond stress-sensitive clinical ratings. Sankaraleengam Alagapan uses variational autoencoders to address high-dimensional longitudinal local-field potentials and unreliable labels. Supervised models track depressive states; a generative causal explainer identifies interpretable, low-dimensional features, advancing actionable biomarkers for stimulation management.

A venerable tradition in neuroscience seeks to understand sensory processing, and in particular vision, through unsupervised learning of natural statistics. Generative models have provided invaluable insights into the way neural response statistics of low level vision (including nonlinearities in response means, variability, oscillations) is shaped by probabilistic inference. However, progress has been hampered by the limited capabilities of generative models to learn both nonlinear and hierarchical representations of natural images. Here we harness the inspirations coming from neuroscience to

Divisive normalization (DN), the division of a neuron’s activity by a pool of connected neurons, has been recognized as a general computational motif in the cortex. Arguments support that in the visual cortex, DN acts as a canonical computation in generative models, which delivers contextual effects and contributes to shaping response variability. Deep generative models (DGMs) have recently been adopted by neuroscience from machine vision to embrace nonlinear computations in the visual cortex. Yet, it remains unclear whether DGMs effectively integrate DN as a core computational element. We inv

Understanding how cortical circuits encode sensory information remains a key challenge. Given a stimulus, the central question from the prespecitive of probablistic theories is how posterior probablities are neurally encoded. In recent years, standard approaches like sampling-based and variational approximations have been extended by deep neural networks (DNNs). For variational approaches, DNNs are frequently used to define encoders in variational autoencoders (VAE) settings. It remains unclear, however, what aspects of posterior approximations are important to explain neural encoding . Unlik

Normal brain function results from directed causal interactions between brain regions. However, the large-scale spatial and temporal structure of these interactions remains unclear. Lagged correlations between the activity of different brain regions may reflect their directed causal interactions. Thus, we combined magnetoencephalography (MEG) and machine learning approaches to characterize and disentangle the structure of lagged correlations in the human brain.We computed lagged correlations between all pairs of cortical regions and frequencies of neural activity. We employed variational autoe