Neuroscience

eLife Assessment

This study presents a well-executed investigation into how the olfactory system disconnects from the environment during sleep and anesthesia, identifying a potential gating mechanism at the earliest synaptic stages of the olfactory bulb. The findings are important, as they challenge current theories by demonstrating that sensory gating occurs in non-thalamic pathways even under controlled airflow conditions. The strength of evidence is solid, supported by rigorous multimodal recordings, although the reliance on anesthetic models to draw conclusions about natural sleep is a limitation that requires further contextualization.

eLife Assessment

This important study links blood-derived dietary content to sustained increases in sleep in the mosquito Aedes aegypti. Using multiple independent approaches, the authors provide convincing evidence for blood-induced changes in sleep. These findings have broad implications for understanding how specialized diets regulate sleep across species and for mosquito vector biology.

eLife Assessment

The present work provides new insights into detailed brain morphology. Using state-of-the-art methods, it provides compelling evidence for the relevance of sucal morphology for the precise localization of brain function. The fundamental findings have great relevance for the fields of imaging neuroscience and individualized medicine as ever-improving techniques improve precision to the point where individual brain anatomy is taking centre stage.

eLife Assessment

The authors establish solid theoretical principles for designing brain perturbations under the assumption that brain activity evolves under a linear model. By prioritizing low-variance components, resonant frequencies, and hub nodes, this framework provides an important foundation for optimizing information gain, neural state classification, and the control of neural dynamics. However, the lack of investigation of model mismatch makes the study incomplete.

eLife Assessment

This valuable computational study presents a conceptually simple and biologically plausible reinforcement-learning framework for motor learning based on policy-gradient methods. The evidence supporting the conclusions is convincing, including rigorous mathematical derivations of learning rules for the mean and variance of motor commands and simulation results for three sets of experimental data, based on three different motor learning tasks from the literature. However, there is a lack of a clear description of the specific conditions under which this framework yields unique mechanistic insights or predictive values, hence falling short of qualifying as a "general theory of motor learning". The work will be of interest to researchers in computational motor learning and motor neuroscience.