Compact self-wiring in cultured neural networks

We present a novel approach for patterning cultured neural networks in which a particular geometry is achieved via anchoring of cell clusters (tens of cells/each) at specific positions. In addition, compact connections among pairs of clusters occur spontaneously through a single non-adherent straigh...

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Bibliographic Details
Published in:Journal of neural engineering 2006-06, Vol.3 (2), p.95-101
Main Authors: Sorkin, R, Gabay, T, Blinder, P, Baranes, D, Ben-Jacob, E, Hanein, Y
Format: Article
Language:English
Subjects:
Animals
Animals, Newborn
Biocompatible Materials - chemistry
Cell Adhesion - physiology
Cell Aggregation - physiology
Cell Culture Techniques - instrumentation
Cell Culture Techniques - methods
Cells, Cultured
Hippocampus - cytology
Hippocampus - physiology
Nerve Net - cytology
Nerve Net - physiology
Neurons - cytology
Neurons - physiology
Rats
Rats, Sprague-Dawley
Surface Properties
Synapses - physiology
Synapses - ultrastructure
Tissue Engineering - instrumentation
Tissue Engineering - methods
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Summary:We present a novel approach for patterning cultured neural networks in which a particular geometry is achieved via anchoring of cell clusters (tens of cells/each) at specific positions. In addition, compact connections among pairs of clusters occur spontaneously through a single non-adherent straight bundle composed of axons and dendrites. The anchors that stabilize the cell clusters are either poly-D-lysine, a strong adhesive substrate, or carbon nanotubes. Square, triangular and circular structures of connectivity were successfully realized. Monitoring the dynamics of the forming networks in real time revealed that the self-assembly process is mainly driven by the ability of the neuronal cell clusters to move away from each other while continuously stretching a neurite bundle in between. Using the presented technique, we achieved networks with wiring regions which are made exclusively of neuronal processes unbound to the surface. The resulted network patterns are very stable and can be maintained for as long as 11 weeks. The approach can be used to build advanced neuro-chips for bio-sensing applications (e.g. drug and toxin detection) where the structure, stability and reproducibility of the networks are of great relevance.
ISSN:1741-2552
1741-2560
1741-2552
DOI:10.1088/1741-2560/3/2/003