Back

Computational Model for Biological Neural Network for Localisation of Sound in the Vertical Plane

De, A.; Cox, D. L.

2020-08-06 neuroscience
10.1101/2020.08.05.238402 bioRxiv
Show abstract

We build a computational rate model for a biological neural network found in mammals that is thought to be important in the localisation of the sound in the vertical plane. We find the response of neurons in the brain stem that participate in the localisation neural circuit to pure tones, broad band noise and notched noise and compare them to experimentally obtained response of these neurons. Our model is able to reproduce the sensitivity of these neurons in the brain stem to spectral properties of sounds that are important in localisation. This is the first rate based population model that elucidates all the response properties of the neurons in the vertical localisation pathway to our knowledge.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 10%
18.5%
Similar papers in this journal
2
PLOS Computational Biology
● 1863 papers in training set
Top 3%
11.9%
Similar papers in this journal
3
Journal of Computational Neuroscience
○ 29 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
4
Frontiers in Neuroscience
○ 256 papers in training set
Top 0.2%
6.7%
Similar papers in this journal
5
Neural Computation
● 39 papers in training set
Top 0.1%
6.3%
Similar papers in this journal
50% of probability mass above
6
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 0.3%
5.2%
7
Scientific Reports
○ 3612 papers in training set
Top 17%
5.2%
8
Neurocomputing
○ 13 papers in training set
Top 0.1%
3.2%
9
eneuro
● 439 papers in training set
Top 4%
2.1%
10
Physical Review E
● 112 papers in training set
Top 0.6%
2.1%
11
Biological Cybernetics
○ 15 papers in training set
Top 0.1%
1.9%
12
Neural Networks
○ 35 papers in training set
Top 0.3%
1.7%
13
European Journal of Neuroscience
○ 189 papers in training set
Top 2%
1.4%
14
Cognitive Neurodynamics
○ 18 papers in training set
Top 0.2%
1.1%
15
Frontiers in Physiology
○ 106 papers in training set
Top 2%
1.1%
16
Frontiers in Neural Circuits
○ 43 papers in training set
Top 0.5%
1.1%
17
Computers in Biology and Medicine
○ 128 papers in training set
Top 4%
1.1%
18
Frontiers in Systems Neuroscience
○ 22 papers in training set
Top 0.2%
1.0%
19
Journal of Neural Engineering
○ 221 papers in training set
Top 2%
0.8%
20
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.3%
0.8%
21
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.9%
0.8%
22
Vision Research
○ 29 papers in training set
Top 0.3%
0.8%
23
Hearing Research
○ 54 papers in training set
Top 0.4%
0.8%
24
Nonlinear Dynamics
○ 10 papers in training set
Top 0.4%
0.8%
25
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.5%
0.6%
26
Ecological Informatics
○ 33 papers in training set
Top 0.8%
0.6%
27
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.8%
0.6%

"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.