Have you ever heard of Organic Neuromorphic Electronics? When we talk about the working of the human brain it works differently from a computer – while the human brain works with biological cells and electrical impulses whereas a computer uses silicon-based transistors.
One experiment where scientists have provided a toy robot with a sharp and commutable electrical circuit that has been prepared with soft organic materials, similar to the biological matter has been conducted. With this biological point of view, they were able to upskill the robot to steer independently through a maze using visual signs for direction.
The processor is considered as the brain of a computer, but when we compare processors, the human brain both works fundamentally differently. If we talk of the transistors, they perform logical operations with the help of electronic signals. Whereas the brain coordinates with nerve cells neurons, which are connected through biological conductive paths, called synapses. All this signaling of nerves cells is used to control the body and perceive the environment.
The reaction of the body/brain system when certain stimuli are perceived through the eyes, ears, or sense of touch is triggered through one input stimulus that leads to a learning process with a transparent behavioral outcome.
The leader Gkoupidenis, in Paul Blom’s department at the Max Planck Institute for Polymer Research, has now put in this fundamental principle of learning through experience in a disentangle form and navigated a robot through a knot using an organic neuromorphic circuit. The experiment was an ample alliance between the Universities of Eindhoven, Stanford, Brescia, Oxford, and KAUST.
“We wanted to use this plain construct to convey how powerful such ‘organic neuromorphic devices’ can be in real-world state,” stated Imke Krauhausen, a doctoral student in Gkoupidenis’ group and at TU Eindhoven, and first author of the scientific paper.
To achieve the steering of the robot inside the knot, the researchers nourished the sharp commutive circuit with sensory signals looming from the environment. The path of the knot towards the end is shown visually as each knot intersects. Initially, the robot often misreads the visual signs, thus it makes the wrong “turning” decisions as the knot intersects and hence loses the way out. When the robot makes these wrong decisions and follows dead-end paths, it is being cast down to make these wrong choices by receiving corrective stimuli.
With each following effecting of the experiment, the robot slowly picks to make the right turning decisions at the intersections, to avoid receiving corrective stimuli, and after a few checks, it finds the way out of the knots. This learning process happens purely on the organic commutable circuit.
“We were really happy to ascertain the transformation in less time within the operation of the robot. The robot can solve the knot after some trials by learning on a simple organic circuit. We have shown here a first, very plain structure. In the future, there is hope that organic neuromorphic devices could also be used for computing/learning. This will create entirely new possibilities for applications in real-world robotics, human-machine interfaces, and point-of-care diagnostics. Big platforms for rapid prototyping and education, at the intersection of materials science and robotics, also are expected to emerge.“ Gkoupidenis stated.