Salience

…it is facilitated by (top-down) salience, which enhances attention to behaviorally relevant learned cues predicting the occurrence of valenced outcomes. Using human neuroimaging, we recently separated value (ventral striatum, posterior ventromedial prefrontal cortex) from salience (anterior ventromedial cortex, occipital cortex

Understanding the activity of single neurons in relation to features in the environment is the first step in many neuroscience studies. We propose a method using algorithm unrolling, an emerging technique in interpretable deep learning, to deconvolve single-trial neuronal activity into interpretable components. Specifically, we model the firing rates of single neurons using a set of kernels characterizing neurons' responses to time-sensitive sparse events/stimuli. The kernels can be either unique or shared across the population and are weighted by codes whose amplitude and timing are trial-spe

…cortex is a critical component of a salience/midcingulo-insular network that is thought to mediate interactions between brain networks involved in externally oriented (central executive/lateral frontoparietal network) and internally oriented (default mode/medial frontoparietal network) processes. How these brain systems reconfigure with development is a critical question for cognitive neuroscience, with implications for neurodev

Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the f