Neuroscience

eLife Assessment

This valuable study presents the design of a new device for using high-density electrophysiological probes ('Neuropixels') in freely moving rodents. The evidence demonstrating the system's versatility and ability to record high-quality extracellular data in both mice and rats is compelling. This study will be of significant interest to neuroscientists performing chronic electrophysiological recordings.

eLife Assessment

This important paper explores the impact of early life stress (ELS) on adult brain and behavior. The significance of the convincing findings are that they implicate regulation of non-neuronal cells in the development of brain and behavioral dysfunction associated with ELS. With an elegant combination of behavioral models, morphological and functional assessments using immunostaining, electrophysiology, and viral-mediated loss-of-function approaches, the authors report that astrocyte dysfunction plays a role in ELS responses. The work is of interest to a broad behavioral and cellular neuroscience audience.

eLife Assessment

This important study combines the use of Fisher Kernels with Hidden Markov models aiming to improve brain-behaviour prediction. The evidence supporting the authors' conclusions is compelling, comparing brain-behaviour prediction accuracies across a range of different traits, including out of sample assessment. This work is timely and will be of interest to neuroscientists working on functional connectivity for brain-behaviour association.

eLife Assessment

This important modeling study alters a previous model of the intact cat spinal locomotor network to simulate a lateral hemi-section of the spinal cord. The modeling and experimental work described provide convincing evidence that this model is capable of qualitatively predicting alterations to the swing and stance phase durations during locomotion at different speeds on intact or split-belt treadmills. This paper will interest neuroscientists studying vertebrate motor systems, including researchers working on motor dysfunction after spinal cord injury.

eLife Assessment

This important manuscript sets out to identify sleep/arousal phenotypes in larval zebrafish carrying mutations in Alzheimer's disease (AD)-associated genes. The authors provide detailed phenotypic data for F0 knockouts of each of 7 AD-associated genes and then compare the resulting behavioral fingerprints to those obtained from a large-scale chemical screen to generate new hypotheses about underlying molecular mechanisms. The data presented are solid, although extensive interpretation of pharmacological screen data does not necessarily reflect the limited mechanistic data. Nonetheless, the authors address most reviewer concerns in their revised version, providing invaluable new analyses. Phenotypic characterization presented is comprehensive, and the authors develop a well-designed behavioral analysis pipeline that will provide considerable value for zebrafish neuroscientists.

eLife Assessment

This work models reinforcement-learning experiments using a recurrent neural network. It examines if the detailed credit assignment necessary for back-propagation through time can be replaced with random feedback. In this useful study the authors show that it yields a satisfactory approximation but the evidence to support that it holds in general is incomplete. As only short temporal delays are used and the examples simulated are overly simple, the approximation would need to be tested on more complex task and with larger networks.

eLife Assessment

In this valuable study the authors use electrophysiology in brain slices and computer modeling and suggest that layer 2/3 pyramidal neurons of the mouse cortex have functional HCN channels on the proximal apical dendrite which allows distinct processing of input at that location from the input to distal apical dendrites. The revisions improved the solid paper but some of the concerns were not addressed sufficiently and many of these concerns could be addressed by further revision.

eLife Assessment

The manuscript provides important new insights into the mechanisms of statistical learning in early human development, showing that statistical learning in neonates occurs robustly and is not limited to linguistic features but occurs across different domains. The evidence is convincing and the findings are highly relevant for researchers working in several domains, including developmental cognitive neuroscience, developmental psychology, linguistics, and speech pathology.

eLife Assessment

The revised report provides valuable findings for the field, suggesting a relationship between CRF1 receptors, sociability deficits in morphine-treated male mice yet not females, and a potential mechanism involving oxytocin neurons in the paraventricular nucleus of the hypothalamus. Generally, the strength of evidence is solid in terms of the methods, data, and analyses. This work will be of interest to those interested in social behavior and addiction.

eLife Assessment

This study is important, advancing our understanding of how humans adapt to uncertainty in dynamic environments by investigating the interplay between two types of uncertainty-volatility (systematic changes in outcomes) and noise (random variability in outcomes). Using an innovative experimental task, reinforcement learning (RL) models, and Bayesian Observer Models (BOM), the authors demonstrate that humans exhibit approximate rationality, often misattributing noise as volatility and adopting suboptimal learning rates in noisy conditions. The evidence is compelling, supported by a well-designed experimental task that independently manipulates noise and volatility, robust behavioral data, and computational modeling; the inclusion of BOM lesioning and physiological validation through pupillometry provides a nuanced understanding of suboptimal human learning. While the study could benefit from expanding the model space (e.g., by including latent state models) and offering greater clarity in task instructions and raw behavioral data, these limitations do not undermine the strength of the findings.