Neuroscience

High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes

1. Li A Zhang
2. Peichao Li
3. Edward M Callaway

• Curated by eLife

  eLife Assessment

  This important study provides insights and strategies for assessing laminar structure in vivo in the visual cortex of the macaque monkey with high-density linear electrode arrays. The paper provides convincing evidence demonstrating that signals in higher frequency bands, related to the discharge of action potentials, are of substantially better use for achieving well-resolved cortical layer identification than are signals in lower frequency bands typically associated with local field potentials and standard-practice Current Source Density (CSD) analyses. These findings are of interest to a wide range of neuroscientists making comparisons between cortical layers or recording with array electrodes.

Reviewed by eLife

In vitro survival and neurogenic potential of central canal-derived neural stem cells depend on spinal cord injury type

1. Lars Erik Schiro
2. Ulrich Stefan Bauer
3. Christiana Bjorkli
4. Axel Sandvig
5. Ioanna Sandvig

Reviewed by PREreview

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning

1. Sarah Jo C Venditto
2. Kevin J Miller
3. Carlos D Brody
4. Nathaniel D Daw

• Curated by eLife

  eLife Assessment

  This important work by Veneditto and colleagues developed a new modeling approach, called a mixture-of-agent hidden Markov model (MoA-HMM), in which choice behaviors are modeled as transitions between discrete states defined by different weighting of several reinforcement learning and decision strategies. The authors apply this approach to their previous data collected from rats performing the two-step task, and show that this method predicts fluctuations in neural and other behavioral data and provides better fits to the data than previous methods. The revision has greatly improved the manuscript, the evidence supporting the conclusions is convincing, and the method is of general interest to the field.

Reviewed by eLife

GEARBOCS: An Adeno Associated Virus Tool for In Vivo Gene Editing in Astrocytes

1. Dhanesh Sivadasan Bindu
2. Justin T Savage
3. Nicholas Brose
4. Luke Bradley
5. Kylie Dimond
6. Christabel Xin Tan
7. Cagla Eroglu

• Curated by eLife

  eLife Assessment

  The present study described GEARBOCS, an adeno-associated virus tool for in vivo gene editing in astrocytes, which is both timely and of importance for glial biologists, who often are troubled by efficient gene targeting in astrocytes. Overall, the finding is valuable, and the strength of the evidence is solid. Presumably, there will be great potential associated with GEARBOCS applications in the future.

Reviewed by eLife

Beyond gradients: Factorized, geometric control of interference and generalization

1. Daniel N Scott
2. Michael J Frank

• Curated by eLife

  eLife Assessment

  This valuable study introduces a novel method for controlling generalization and interference in neural networks undergoing continual learning. The authors provide solid evidence that their parsimonious method performs better than online gradient descent in several continual learning situations while providing biologically plausible links to three-factor learning rules. However, empirical validation is limited to linear networks, which raises questions about the generality of the results in non-linear networks. While the work is interesting to theoretical and experimental neuroscientists, improving the article presentation by clearly defining terminology before using it and providing more details on the setup of the simulation experiments would be vital to make the article more accessible.

Reviewed by eLife