Artificial synapse that works with living cells created


Researchers have created a device that can integrate and interact with neuron-like cells. This could be an early step toward an artificial synapse for use in brain-computer interfaces.

Now, in a paper published June 15 in Nature Materials, they have tested the first biohybrid version of their artificial synapse and demonstrated that it can communicate with living cells. Future technologies stemming from this device could function by responding directly to chemical signals from the brain. The research was conducted in collaboration with researchers at Istituto Italiano di Tecnologia (Italian Institute of Technology — IIT) in Italy and at Eindhoven University of Technology (Netherlands).

«This paper really highlights the unique strength of the materials that we use in being able to interact with living matter,» said Alberto Salleo, professor of materials science and engineering at Stanford and co-senior author of the paper. «The cells are happy sitting on the soft polymer. But the compatibility goes deeper: These materials work with the same molecules neurons use naturally.»

While other brain-integrated devices require an electrical signal to detect and process the brain’s messages, the communications between this device and living cells occur through electrochemistry — as though the material were just another neuron receiving messages from its neighbor.

How neurons learn

The biohybrid artificial synapse consists of two soft polymer electrodes, separated by a trench filled with electrolyte solution — which plays the part of the synaptic cleft that separates communicating neurons in the brain. When living cells are placed on top of one electrode, neurotransmitters that those cells release can react with that electrode to produce ions. Those ions travel across the trench to the second electrode and modulate the conductive state of this electrode. Some of that change is preserved, simulating the learning process occurring in nature.


Story Source: Materials provided by Stanford University. Original written by Taylor Kubota. Note: Content may be edited for style and length.


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