Neuroscience

The Switchmaze: an open-design device for measuring motivation and drive switching in mice

1. Clara Hartmann
2. Ambika Mahajan
3. Vinicius Borges
4. Lotte Razenberg
5. Yves Thönnes
6. Mahesh Miikael Karnani

Reviewed by Peer Community in 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. Daozheng Gong
3. Hunter C. Davis
4. Pojeong Park
5. Yitong Qi
6. 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

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 M. 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. Larry Abbot
20. Luigi Acerbi
21. Valeria Aguillon-Rodriguez
22. Mandana Ahmadi
23. Jaweria Amjad
24. Dora Angelaki
25. Jaime Arlandis
26. Zoe C. Ashwood
27. Kush Banga
28. Hailey Barrell
29. Hannah M. Bayer
30. Brandon Benson
31. Julius Benson
32. Jai Bhagat
33. Dan Birman
34. Niccolò Bonacchi
35. Kcenia Bougrova
36. Julien Boussard
37. Sebastian A. Bruijns
38. E. Kelly Buchanan
39. Robert Campbell
40. Matteo Carandini
41. Joana A. Catarino
42. Fanny Cazettes
43. Gaelle A. Chapuis
44. Anne K. Churchland
45. Yang Dan
46. Felicia Davatolhagh
47. Peter Dayan
48. Sophie Denève
49. Eric E. J. DeWitt
50. Ling Liang Dong
51. Tatiana Engel
52. Michele Fabbri
53. Mayo Faulkner
54. Robert Fetcho
55. Ila Fiete
56. Charles Findling
57. Laura Freitas-Silva
58. Surya Ganguli
59. Berk Gercek
60. Naureen Ghani
61. Ivan Gordeliy
62. Laura M. Haetzel
63. Kenneth D. Harris
64. Michael Hausser
65. Naoki Hiratani
66. Sonja Hofer
67. Fei Hu
68. Felix Huber
69. Cole Hurwitz
70. Anup Khanal
71. Christopher S. Krasniak
72. Sanjukta Krishnagopal
73. Michael Krumin
74. Debottam Kundu
75. Agnès Landemard
76. Christopher Langdon
77. Christopher Langfield
78. Inês Laranjeira
79. Peter Latham
80. Petrina Lau
81. Hyun Dong Lee
82. Ari Liu
83. Zachary F. Mainen
84. Amalia Makri-Cottington
85. Hernando Martinez-Vergara
86. Brenna McMannon
87. Isaiah McRoberts
88. Guido T. Meijer
89. Maxwell Melin
90. Leenoy Meshulam
91. Kim Miller
92. Nathaniel J. Miska
93. Catalin Mitelut
94. Zeinab Mohammadi
95. Thomas Mrsic-Flogel
96. Masayoshi Murakami
97. Jean-Paul Noel
98. Kai Nylund
99. Farideh Oloomi
100. Alejandro Pan-Vazquez
101. Liam Paninski
102. Alberto Pezzotta
103. Samuel Picard
104. Jonathan W. Pillow
105. Alexandre Pouget
106. Florian Rau
107. Cyrille Rossant
108. Noam Roth
109. Nicholas A. Roy
110. Kamron Saniee
111. Rylan Schaeffer
112. Michael M. Schartner
113. Yanliang Shi
114. Carolina Soares
115. Karolina Z. Socha
116. Cristian Soitu
117. Nicholas A. Steinmetz
118. Karel Svoboda
119. Marsa Taheri
120. Charline Tessereau
121. Anne E. Urai
122. Erdem Varol
123. Miles J. Wells
124. Steven J. West
125. Matthew R. Whiteway
126. Charles Windolf
127. Olivier Winter
128. Ilana Witten
129. Lauren E. Wool
130. Zekai Xu
131. Han Yu
132. Anthony M. Zador
133. Yizi Zhang
134. John P. Cunningham
135. Nathaniel B. Sawtell
136. Liam Paninski

Reviewed by Arcadia Science

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

Deciphering the chemical language of inbred and wild mouse conspecific scents

1. Maximilian Nagel
2. Marco Niestroj
3. Rohini Bansal
4. David Fleck
5. Angelika Lampert
6. Romana Stopkova
7. Pavel Stopka
8. Yoram Ben-Shaul
9. Marc Spehr

• Curated by eLife

  eLife assessment

  This carefully executed study provides a comparison of the chemical composition of mouse urine across strain and sex with the responses of vomeronasal sensory neurons, which are responsible for detecting chemical social cues. While the authors did not examine all molecular classes found in mouse urine or directly test whether the urinary volatile chemicals that vary with sex and strain are effective vomeronasal neuron ligands, solid data are provided that will be of significant interest to those studying chemical communication in rodents. This work should provide a valuable foundation for future research that will determine which molecules drive sex- and strain-specific vomeronasal responses.

Reviewed by eLife