Back

Mechanical characterization of compliant neural probes, their insertion regimes and a rule of thumb design

Smith, Z.

2019-09-18 neuroscience
10.1101/770107 bioRxiv
Show abstract

Investigates mechanical theory of buckling compliant neural probes, how this relates to insertion forces using normalized force data and discusses potential strategies to improve the insertion ability of neural probes. This report also provides a guide for mechanically testing compliant neural probes and demonstrates a design rule of thumb that insertion forces should be 1/3 lower than the buckling force to enable reliable insertion.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 8%
19.3%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 4%
10.1%
Similar papers in this journal
3
Journal of Neural Engineering
○ 221 papers in training set
Top 0.4%
8.2%
Similar papers in this journal
4
Journal of the Mechanical Behavior of Biomedical Materials
○ 24 papers in training set
Top 0.1%
5.1%
Similar papers in this journal
5
Acta Biomaterialia
○ 92 papers in training set
Top 0.4%
3.6%
Similar papers in this journal
6
Biomedical Physics & Engineering Express
○ 11 papers in training set
Top 0.1%
2.8%
7
Frontiers in Neuroscience
○ 256 papers in training set
Top 2%
2.8%
50% of probability mass above
8
eneuro
● 439 papers in training set
Top 3%
2.5%
9
Biophysical Journal
○ 631 papers in training set
Top 2%
2.2%
10
Bioengineering
○ 29 papers in training set
Top 0.2%
2.0%
11
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 0.9%
1.8%
12
Sensors
○ 43 papers in training set
Top 0.6%
1.8%
13
HardwareX
○ 18 papers in training set
Top 0.1%
1.8%
14
Journal of Neuroscience Methods
○ 122 papers in training set
Top 1%
1.4%
15
IEEE Transactions on Biomedical Engineering
● 40 papers in training set
Top 0.7%
1.2%
16
RSC Advances
● 22 papers in training set
Top 0.6%
1.1%
17
European Journal of Neuroscience
○ 189 papers in training set
Top 3%
1.1%
18
eLife
● 5828 papers in training set
Top 61%
1.1%
19
Journal of The Royal Society Interface
● 235 papers in training set
Top 3%
1.0%
20
Annals of Biomedical Engineering
○ 37 papers in training set
Top 0.9%
1.0%
21
Computers in Biology and Medicine
○ 128 papers in training set
Top 4%
1.0%
22
Biofabrication
○ 36 papers in training set
Top 0.6%
0.9%
23
MethodsX
○ 16 papers in training set
Top 0.2%
0.9%
24
Advanced Materials Technologies
○ 29 papers in training set
Top 0.5%
0.9%
25
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 1%
0.6%
26
Biomechanics and Modeling in Mechanobiology
○ 29 papers in training set
Top 0.5%
0.6%
27
Nanoscale
● 42 papers in training set
Top 0.8%
0.6%
28
Biology Open
● 156 papers in training set
Top 4%
0.6%
29
ACS Biomaterials Science & Engineering
● 37 papers in training set
Top 1.0%
0.6%
30
Materials Today Bio
○ 20 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.