Neuroscience

A new ‘CFS tracking’ paradigm reveals uniform suppression depth regardless of target complexity or salience

1. David Alais
2. Jacob Coorey
3. Randolph Blake
4. Matthew J Davidson

• Curated by eLife

  eLife assessment

  This valuable study introduces an innovative method for measuring interocular suppression depth, which implicates mechanisms underlying subconscious visual processing. The evidence is solid in suggesting that the new method yields provocative uniform suppression depth results across image categories that differ from conventional bCFS threshold. It will be of interest not only to cognitive psychologists and neuroscientists who study sensation and perception but also to philosophers who work on theories of consciousness.

Reviewed by eLife

Functional diversity of dopamine axons in prefrontal cortex during classical conditioning

1. Kenta Abe
2. Yuki Kambe
3. Kei Majima
4. Zijing Hu
5. Makoto Ohtake
6. Ali Momennezhad
7. Hideki Izumi
8. Takuma Tanaka
9. Ashley Matunis
10. Emma Stacy
11. Takahide Itokazu
12. Takashi R Sato
13. Tatsuo Sato

• Curated by eLife

  eLife assessment

  This important study shows that distinct midbrain dopaminergic axons in the medial prefrontal cortex respond to aversive and rewarding stimuli and suggest that they are biased toward aversive processing. The use of innovative microprism based two-photon calcium imaging to study single axon heterogeneity is convincing, although the experimental design makes it difficult to definitively distinguish aversive valence from stimulus salience in this dopamine projection. This work will be of interest to neuroscientists working on neuromodulatory systems, cortical function and decision making.

Reviewed by eLife

Photophysics-informed two-photon voltage imaging using FRET-opsin voltage indicators

1. F. Phil Brooks
2. Hunter C. Davis
3. Pojeong Park
4. Yitong Qi
5. Adam E. Cohen

Reviewed by Arcadia Science

Exosomes promote axon outgrowth by engaging the Wnt-Planar Cell Polarity pathway

1. Samar Ahmad
2. Melanie Pye
3. Masahiro Narimatsu
4. Siyuan Song
5. Tania Christova
6. Jeffrey L Wrana
7. Liliana Attisano

Reviewed by Review Commons

Jointly looking to the past and the future in visual working memory

1. Baiwei Liu
2. Zampeta-Sofia Alexopoulou
3. Freek van Ede

• Curated by eLife

  eLife assessment

  This important study advances our understanding of how past and future information is jointly considered in visual working memory by studying gaze biases in a memory task that dissociates the locations during encoding and memory tests. The evidence supporting the conclusions is convincing, with state-of-the-art gaze analyses that build on a recent series of experiments introduced by the authors. This work will be of broad interest to vision scientists interested in the interplay of vision, eye movements, and memory.

Reviewed by eLife

Targeting resident astrocytes attenuates neuropathic pain after spinal cord injury

1. Qing Zhao
2. Yanjing Zhu
3. Yilong Ren
4. Lijuan Zhao
5. Jingwei Zhao
6. Shuai Yin
7. Haofei Ni
8. Rongrong Zhu
9. Liming Cheng
10. Ning Xie

• Curated by eLife

  eLife assessment

  This important study demonstrated that ablation of astrocytes in the lumbar spinal cord not only reduced neuropathic pain but also caused microglia activation. The findings presented add considerable value to the current understanding of the role of astrocyte elimination in neuropathic pain, offering convincing evidence that supports existing hypotheses and insights into the interactions between astrocytes and microglial cells, likely through IFN-mediated mechanisms. This study may also offer a new therapeutic strategy for the treatment of debilitating neuropathic pain in patients with SCI.

Reviewed by eLife

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

1. Dan Biderman
2. Matthew R Whiteway
3. Cole Hurwitz
4. Nicholas Greenspan
5. Robert S Lee
6. Ankit Vishnubhotla
7. Richard Warren
8. Federico Pedraja
9. Dillon Noone
10. Michael Schartner
11. Julia M Huntenburg
12. Anup Khanal
13. Guido T Meijer
14. Jean-Paul Noel
15. Alejandro Pan-Vazquez
16. Karolina Z Socha
17. Anne E Urai
18. The International Brain Laboratory
19. John P Cunningham
20. Nathaniel B Sawtell
21. Liam Paninski

Reviewed by Arcadia Science

Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity

1. Toshitake Asabuki
2. Claudia Clopath

• Curated by eLife

  eLife assessment

  This is an important study that investigates how neural networks can learn to stochastically replay presented sequences of activity according to learned transition probabilities. The authors use error-based excitatory plasticity to minimize the difference between internally predicted activity and stimulus-driven activity, and inhibitory plasticity to maintain E-I balance. The approach is solid but the choice of learning rules and parameters is not always always justified, lacking a formal derivation and concrete experimental predictions.

Reviewed by eLife

A neuronal least-action principle for real-time learning in cortical circuits

1. Walter Senn
2. Dominik Dold
3. Akos F. Kungl
4. Benjamin Ellenberger
5. Jakob Jordan
6. Yoshua Bengio
7. João Sacramento
8. Mihai A. Petrovici

• Curated by eLife

  eLife assessment

  This manuscript describes a potentially important theoretical framework to link predictive coding, error-based learning, and neuronal dynamics. The provided evidence is solid, but some details would benefit from additional clarification. The exposition of the manuscript is targeted for a specialist audience.

Reviewed by eLife

Representational drift as a result of implicit regularization

1. Aviv Ratzon
2. Dori Derdikman
3. Omri Barak

• Curated by eLife

  eLife assessment

  This study presents a new and important theoretical account of spatial representational drift in the hippocampus. The evidence supporting the claims is convincing, with a clear and accessible explanation of the phenomenon. Overall, this study will likely attract researchers exploring learning and representation in both biological and artificial neural networks.

Reviewed by eLife