Authors:
Hideyuki Terazono
1
;
Hyonchol Kim
1
;
Masahito Hayashi
1
;
Akihiro Hattori
2
;
Hiroyuki Takei
3
and
Kenji Yasuda
4
Affiliations:
1
Kanagawa Academy of Science and Technology, Japan
;
2
Institute of Biomaterials and Bioengineering and Tokyo Medical and Dental University, Japan
;
3
Kanagawa Academy of Science and Technology, Faculty of Life Sciences and Toyo University, Japan
;
4
Kanagawa Academy of Science and Technology, Institute of Biomaterials and Bioengineering and Tokyo Medical and Dental University, Japan
Keyword(s):
Artificial neuronal networks, Hippocampal neurons, Micro-chip, Agarose, Alginate, Micro-patterning, Multi-electrode.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bio-Inspired and Humanoid Robotics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
We developed three techniques to make artificial neuronal networks constructed from rat hippocampal neurons. 1) a method of non-invasively collecting primary cultured neurons and their deposition, 2) a technique for microprocessing agarose for the purpose of assembling artificial neuronal networks, 3) a multi-electrode array system for measurement of the multi-point extracellular potential of neurons. The three techniques allow us to assemble and evaluate artificial neuronal networks constructed from particular cells. We can manipulate neuro-transmission pathways and investigate roles played by the innate period or stability information for each individual cell in the framework of physiological mechanism. It is thus possible to construct and demonstrated the actual neuronal networks simulated by the computed neural networks.