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Maximum-entropy and representative samples of neuronal activity: a dilemma

Porta Mana, P.; Rostami, V.; Torre, E.; Roudi, Y.

2020-10-19 neuroscience
10.1101/329193 bioRxiv
Show abstract

The present work shows that the maximum-entropy method can be applied to a sample of neuronal recordings along two different routes: (1) apply to the sample; or (2) apply to a larger, unsampled neuronal population from which the sample is drawn, and then marginalize to the sample. These two routes give inequivalent results. The second route can be further generalized to the case where the size of the larger population is unknown. Which route should be chosen? Some arguments are presented in favour of the second. This work also presents and discusses probability formulae that relate states of knowledge about a population and its samples, and that may be useful for sampling problems in neuroscience.

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The top 8 journals account for 50% of the predicted probability mass.

1
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4.3%
8
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Top 25%
4.0%
50% of probability mass above
9
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● 49 papers in training set
Top 0.2%
3.2%
10
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Top 36%
3.2%
11
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Top 0.1%
2.6%
12
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13
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○ 40 papers in training set
Top 0.3%
2.1%
14
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○ 29 papers in training set
Top 0.2%
1.7%
15
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Top 1%
1.7%
16
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○ 15 papers in training set
Top 0.1%
1.5%
17
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○ 49 papers in training set
Top 0.8%
1.3%
18
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● 214 papers in training set
Top 4%
1.1%
19
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○ 5641 papers in training set
Top 50%
1.1%
20
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● 302 papers in training set
Top 3%
1.0%
21
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Top 4%
0.8%
22
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Top 5%
0.8%
23
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24
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25
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Top 2%
0.8%
26
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○ 46 papers in training set
Top 1%
0.6%
27
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28
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○ 32 papers in training set
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29
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"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.