Neuroscience

A neural mechanism for compositional generalization of structure in humans

1. Lennart Luettgau
2. Nan Chen
3. Tore Erdmann
4. Sebastijan Veselic
5. Zeb Kurth-Nelson
6. Rani Moran
7. Raymond J Dolan

• Curated by eLife

  eLife Assessment

  This study provides valuable insights into humans' ability to generalize knowledge of learned graph structures to new experiences that share the same structure but are built from different stimuli. However, the evidence for the authors' claims is incomplete, with the main claims of structural generalization and compositionality only partially supported by MEG and behavioral data. This study will be of interest to cognitive neuroscientists studying structure learning and generalization.

Reviewed by eLife

Compensation of Hyperexcitability with Simulation-Based Inference

1. Daniel Müller-Komorowska
2. Tomoki Fukai

• Curated by eLife

  eLife Assessment

  This study introduces a valuable simulation-based inference (SBI) framework to identify degenerate compensatory mechanisms that stabilize network activity despite neuronal hyperexcitability, a feature common to many brain disorders. By estimating posterior distributions of network parameters, the authors highlight factors such as threshold potential and interneuron-to-principal cell connectivity as key compensators for increased intrinsic excitability and interneuron loss. While the approach is promising and could become a key tool for probing network degeneracy, the study is currently incomplete. To fully realize its potential, the framework requires improved scalability and more rigorous cross-validation.

Reviewed by eLife

High frequency spike inference with particle Gibbs sampling

1. Giovanni Diana
2. B Semihcan Sermet
3. Gerard J Broussard
4. Samuel S.-H Wang
5. David A DiGregorio

• Curated by eLife

  eLife Assessment

  In their study, Diana et al. introduce a novel method for spike inference from calcium imaging data using a Monte Carlo-based approach, emphasizing the quantification of uncertainties in spike time estimates through a Bayesian framework. This method employs particle Gibbs sampling for estimating model parameter probabilities, offering accuracy comparable to existing methods with the added benefit of directly assessing uncertainties. The presentation of the underlying methods and its characterization is convincing and it presents a valuable advancement for neuroscientists interested in new approaches for parameter estimation from calcium imaging data.

Reviewed by eLife

Structural and functional evidence supports re-defining mouse higher order visual areas into a single area V2

1. Declan P Rowley
2. Madineh Sedigh-Sarvestani

• Curated by eLife

  eLife Assessment

  This paper performs a valuable critical reassessment of anatomical and functional data, proposing a reclassification of the mouse visual cortex in which almost all the higher visual areas are consolidated into a single area V2. However, the evidence supporting this unification is incomplete, as the key experimental observations that the model attempts to reproduce do not accurately reflect the literature. This study will likely be of interest to neuroscientists focused on the mouse visual cortex and the evolution of cortical organization.

Reviewed by eLife

The promise and peril of comparing fluorescence lifetime in biology revealed by simulations

1. Pingchuan Ma
2. Peter Chen
3. Scott Sternson
4. Yao Chen

• Curated by eLife

  eLife Assessment

  This study presents an important computational framework, FLiSimBA (Fluorescence Lifetime Simulation for Biological Applications), for modeling experimental limitations in Fluorescence Lifetime Imaging Microscopy (FLIM). FLiSimBA is readily available in MATLAB and Python, enables users to simulate effects of noise and varying sensor expression levels, and provides practical guidance for both lifetime imaging experiments and biosensor development. The analyses are robust, and the evidence supporting the tool's utility in distinguishing between multiple lifetime signals is compelling, indicating strong potential for multiplexed dynamic imaging. However, users should also consider that the tool's effectiveness depends on the suitability of a two-component discrete exponential model.

Reviewed by eLife

Activity in serotonergic axons in visuomotor areas of cortex is modulated by the recent history of visuomotor coupling

1. Baba Yogesh
2. Georg B Keller

Reviewed by Peer Community in Neuroscience

PTBP1 Depletion in Mature Astrocytes Reveals Distinct Splicing Alterations Without Neuronal Features

1. Min Zhang
2. Naoto Kubota
3. David Nikom
4. Ayden Arient
5. Sika Zheng

• Curated by eLife

  eLife Assessment

  This study reports important negative results by showing that genetic removal of the RNA-binding protein PTBP1 in astrocytes is not sufficient to induce their conversion into neurons, challenging prior claims in the field. It also provides a systematic and insightful analysis of the role of PTBP1 in regulating astrocyte-specific splicing. The evidence is convincing, as the experiments are technically robust, rigorously controlled, and supported by both imaging and transcriptomic analyses.

Reviewed by eLife

Formation of Task Representations and Replay in Mouse Medial Prefrontal Cortex

1. Hamed Shabani
2. Hannah Muysers
3. Yuk-Hoi Yiu
4. Jonas-Frederic Sauer
5. Marlene Bartos
6. Christian Leibold

• Curated by eLife

  eLife Assessment

  This useful study characterizes the evolution of medial prefrontal cortex activity during the learning of an odor-based choice task. While the evidence for an increase in task-informative cells with learning, the emergence of population sequences, and the presence of replay events is intriguing, it remains incomplete; notably, the study does not adequately consider the extensive literature on the role of olfactory and hippocampal networks in similar odor-guided tasks. Furthermore, the experimental design appears insufficient to support strong conclusions regarding pre-existing representations or the functional relevance of neural sequences. The study will be of interest to neuroscientists investigating learning and decision-making processes.

Reviewed by eLife

Premature vision drives aberrant development of response properties in primary visual cortex

1. Sophie V Griswold
2. Stephen D Van Hooser

• Curated by eLife

  eLife Assessment

  This carefully conducted study aims to understand how the early visual experience of premature infants induces lasting deficits, including compromised motion processing. The authors address this important question in a ferret animal model, exposing the developing visual system prematurely to patterned visual input by opening one or both eyes at a time when both retinal waves and light traveling through closed lids can drive sensory responses. Convincing evidence is presented, suggesting that eye opening at this time impacts temporal frequency tuning and elevates spontaneous firing rates. These findings will have great relevance for neuroscientists studying visual system development, particularly in the context of premature birth.

Reviewed by eLife

Online reinforcement learning of state representation in recurrent network: the power of random feedback and biological constraints

1. Takayuki Tsurumi
2. Ayaka Kato
3. Arvind Kumar
4. Kenji Morita

• Curated by eLife

  eLife Assessment

  In this important study, the authors model reinforcement-learning experiments using a recurrent neural network. The work examines if the detailed credit assignment necessary for back-propagation through time can be replaced with random feedback. The authors provide solid evidence that the solution is adequate within relatively simple tasks.

Reviewed by eLife