scholarly journals Development of two-photon polymerised scaffolds for optical interrogation and neurite guidance of human iPSC-derived cortical neuronal networks

Lab on a Chip ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 1792-1806 ◽  
Author(s):  
J. A. Crowe ◽  
A. El-Tamer ◽  
D. Nagel ◽  
A. V. Koroleva ◽  
J. Madrid-Wolff ◽  
...  
Keyword(s):  
Neural Network ◽  
Neuronal Networks ◽  
Network Development ◽  
Neurite Guidance ◽  
Two Photon ◽  
Human Ipsc

We identified photopolymers for 2-photon polymerisation with biocompatibility for human iPSC-derived neural network development. Generation of microscale scaffold topologies enabled neurite guidance, demonstrating use in reproducing aligned networks.

scholarly journals Two-photon polymerization for production of human iPSC-derived retinal cell grafts

2017 ◽  
Vol 55 ◽  
pp. 385-395 ◽  
Author(s):  
Kristan S. Worthington ◽  
Luke A. Wiley ◽  
Emily E. Kaalberg ◽  
Malia M. Collins ◽  
Robert F. Mullins ◽  
...  
Keyword(s):  
Retinal Cell ◽  
Two Photon ◽  
Human Ipsc ◽  
Two Photon Polymerization

Heterochrony and evolvability in neural network development

2007 ◽  
Vol 11 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Artur Manuel Ribeiro dos Santos Cal de Matos ◽  
Reiji Suzuki ◽  
Takaya Arita
Keyword(s):  
Neural Network ◽  
Network Development

AFIT neural network development tools and techniques for modeling articial neural networks

1990 ◽  
Author(s):  
Gregory L. Tarr ◽  
Dennis W. Ruck ◽  
Steven K. Rogers ◽  
Matthew Kabrisky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Development ◽  
Development Tools ◽  
Tools And Techniques

Prediction of slip in cable-drum systems using structured neural networks

2013 ◽  
Vol 228 (3) ◽  
pp. 441-456 ◽  
Author(s):  
Ergin Kilic ◽  
Melik Dolen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Size ◽  
Network Architectures ◽  
Network Development ◽  
Low Velocity ◽  
Error Band ◽  
Artificial Neural ◽  
Recurrent Type ◽  
Selection Of

This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also highlighting the corresponding challenges associated with the design phase (i.e. selection of architecture, network size, training process parameters, etc.). Prediction performances of the devised networks are evaluated rigorously via an experimental study. Finally, a structured neural network, which embodies the network with the best prediction performance, is further developed to overcome the drift observed at low velocity. The study illustrates that the resulting structured neural network could predict the slip in the mechanism within an error band of 100 µm when an absolute reference is utilized.

Analysis of human iPSC-derived neuronal networks (hiPSCNN) using holographic single cell and full field optogenetic stimulation

2021 ◽  
Author(s):  
Felix Schmieder ◽  
Rouhollah Habibey ◽  
Volker Busskamp ◽  
Lars Büttner ◽  
Jürgen W. Czarske
Keyword(s):  
Single Cell ◽  
Neuronal Networks ◽  
Full Field ◽  
Optogenetic Stimulation ◽  
Human Ipsc

Microscaffolds by Direct Laser Writing for Neurite Guidance Leading to Tailor‐Made Neuronal Networks

2019 ◽  
Vol 3 (5) ◽  
pp. 1800329 ◽  
Author(s):  
Cornelius Fendler ◽  
Christian Denker ◽  
Jann Harberts ◽  
Parisa Bayat ◽  
Robert Zierold ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Direct Laser Writing ◽  
Laser Writing ◽  
Neurite Guidance ◽  
Direct Laser

scholarly journals Epileptiform Activities in Cultured Human IPSC-Derived Neuronal Networks

2018 ◽  
Vol 114 (3) ◽  
pp. 672a
Author(s):  
Ikuro Suzuki ◽  
Aoi Odawara ◽  
Naoki Matsuda ◽  
Yuto Ishibashi ◽  
Remi Yokoi
Keyword(s):  
Neuronal Networks ◽  
Human Ipsc
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