Physiologists Have Created A Controlled Neural Network - Alternative View

Physiologists Have Created A Controlled Neural Network - Alternative View
Physiologists Have Created A Controlled Neural Network - Alternative View

Video: Physiologists Have Created A Controlled Neural Network - Alternative View

Video: Physiologists Have Created A Controlled Neural Network - Alternative View
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So far, only two cells have been able to connect in it, but in the future, scientists hope to form a multicellular structure.

Researchers at the University of Tokyo have described a method for connecting neurons using microscopic plates. These devices allowed them to form a guided neural network of two cells, in which biologists monitored the growth of axons.

One of the authors of Shotaro Yoshida's work explained:

“In vitro models are important tools because they are very similar to simple neural networks. The problem is that they are difficult to control when neurons make random connections with each other. If we could find a way to control the synthesis of neural networks, we would come closer to understanding how the brain works."

The team has already taken the first step towards achieving the goal. Specialists have developed tiny plates for nerve cells. They consist of a central part, whose radius is 15 micrometers, an axon tube, its length is 100 micrometers, and a compartment for dendrites, its length is 20 micrometers. The shape of this device determines the growth of the processes. Aligning the opposite ends of the tubes allowed two neurons to form a synaptic connection. Neurophysiologists have observed this process using fluorescence microscopy and scanning electron microscope (SEM).

Cultivation of single neurons on microplates / Micromachines
Cultivation of single neurons on microplates / Micromachines

Cultivation of single neurons on microplates / Micromachines.

Scientists noted that this is the first use of plates to control neurons. They believe the technology will enable the development of simple neural networks with one cell resolution.

Alexey Evglevsky

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