Back

Learning receptive field properties of complex cells in V1

Lian, Y.; Almasi, A.; Grayden, D. B.; Kameneva, T.; Burkitt, A. N.; Meffin, H.

2020-08-31 neuroscience
10.1101/2020.05.18.101873 bioRxiv
Show abstract

The authors have withdrawn their manuscript due to a duplicate posting on our website. Please visit doi.org/10.1101/2020.05.18.101881 to access the current version of this preprint on bioRxiv

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
eLife
● 5828 papers in training set
Top 1%
26.3%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 11%
16.9%
Similar papers in this journal
3
Neuron
○ 337 papers in training set
Top 0.3%
11.8%
Similar papers in this journal
50% of probability mass above
4
Nature Neuroscience
○ 252 papers in training set
Top 0.8%
7.8%
Similar papers in this journal
5
PLOS Computational Biology
● 1863 papers in training set
Top 6%
6.2%
Similar papers in this journal
6
Current Biology
○ 665 papers in training set
Top 4%
3.2%
7
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 20%
2.6%
8
Communications Biology
○ 993 papers in training set
Top 8%
2.4%
9
The Journal of Neuroscience
● 1025 papers in training set
Top 6%
2.4%
10
Science Advances
● 1243 papers in training set
Top 17%
2.1%
11
Cell Reports
○ 1498 papers in training set
Top 17%
2.1%
12
Neural Networks
○ 35 papers in training set
Top 0.3%
1.9%
13
eneuro
● 439 papers in training set
Top 5%
1.5%
14
Nature
○ 645 papers in training set
Top 9%
1.1%
15
Brain Structure and Function
○ 93 papers in training set
Top 1%
1.0%
16
PLOS Biology
● 486 papers in training set
Top 9%
1.0%
17
Cerebral Cortex
◐ 396 papers in training set
Top 5%
0.8%
18
PLOS ONE
● 5266 papers in training set
Top 59%
0.8%
19
iScience
○ 1154 papers in training set
Top 36%
0.8%
20
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 1%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.