التفاصيل البيبلوغرافية
| العنوان: |
Architecture of the Joint Autoencoder. |
| المؤلفون: |
Ege Altan (11768156), Sara A. Solla (8163024), Lee E. Miller (11768159), Eric J. Perreault (11768162) |
| سنة النشر: |
2021 |
| المجموعة: |
Smithsonian Institution: Digital Repository |
| مصطلحات موضوعية: |
Neuroscience, Pharmacology, Biotechnology, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, specific function controlled, massive redundancy implies, several representative algorithms, linear algorithms overestimate, manifold underlying multi, nonlinearly embedded data, neural activity within, analyzing neural activity, important challenges associated, denoising algorithm based, algorithms &# 8217, dimensional manifold within, known intrinsic dimensionality, neural activity, population activity, many algorithms, intrinsic dimensionality, dimensional recordings, algorithms overestimated, algorithms failed, &# 8220, free data, experimental data, available data, %22">xlink "> |
| الوصف: |
Channels of the 96-dimensional simulated datasets were randomly partitioned into two sets of signals (blue and yellow). Each 48-dimensional set was reconstructed through the corresponding D -dimensional subspace, Z 1 and Z 2 (green). The reconstructed outputs of the networks were the denoised channels. |
| نوع الوثيقة: |
still image |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/figure/Architecture_of_the_Joint_Autoencoder_/17096234 |
| DOI: |
10.1371/journal.pcbi.1008591.g002 |
| الاتاحة: |
https://doi.org/10.1371/journal.pcbi.1008591.g002 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.6E08C2D1 |
| قاعدة البيانات: |
BASE |