Network structure influences the strength of learned neural representations.

From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown.

Dissociable hindbrain GLP1R circuits for satiety and aversion.

The most successful obesity therapeutics, glucagon-like peptide-1 receptor (GLP1R) agonists, cause aversive responses such as nausea and vomiting, effects that may contribute to their efficacy. Here, we investigated the brain circuits that link satiety to aversion, and unexpectedly discovered that the neural circuits mediating these effects are functionally separable. Systematic investigation across drug-accessible GLP1R populations revealed that only hindbrain neurons are required for the efficacy of GLP1-based obesity drugs.

AgRP neuron activity promotes associations between sensory and nutritive signals to guide flavor preference.

Objective: The learned associations between sensory cues (e.g., taste, smell) and nutritive value (e.

AgRP neuron activity promotes associations between sensory and nutritive signals to guide flavor preference.

Objective: The learned associations between sensory cues (e.g., taste, smell) and nutritive value (e.

Network structure influences the strength of learned neural representations.

Human experience is built upon sequences of discrete events. From those sequences, humans build impressively accurate models of their world. This process has been referred to as graph learning, a form of structure learning in which the mental model encodes the graph of event-to-event transition probabilities [1], [2], typically in medial temporal cortex [3]-[6].

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Unlabelled: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.

Methods: We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results.

Abstract representations of events arise from mental errors in learning and memory.

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory.