Researchers have developed "infomorphic neurons" that learn independently, mimicking their biological counterparts more accurately than previous artificial neurons. A team of researchers from the Göttingen Campus Institute for Dynamics of Biological Networks (CIDBN) at the University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) has programmed these infomorphic neurons and constructed artificial neural networks from them.
The special feature is that the individual artificial neurons learn in a self-organized way and draw the necessary information from their immediate environment in the network. Their findings are published in the journal Proceedings of the National Academy of Sciences.
Both the human brain and modern artificial neural networks are extremely powerful. At the lowest level, the neurons work together as rather simple computing units.
An artificial neural network typically consists of several layers composed of individual neurons. An input signal passes through these layers and is processed by artificial neurons in order to extract relevant information. However, conventional artificial neurons differ significantly from their biological models in the way they learn.
While most artificial neural networks depend on overarching coordination outside the network in order to learn, biological neurons only receive and process signals from other neurons in their immediate vicinity in the network. Biological neural networks are still far superior to artificial ones in terms of both flexibility and energy efficiency.
The new artificial neurons, known as "infomorphic neurons," are capable of learning independently and self-organizing among their neighboring neurons. This means that the smallest unit in the network has to be controlled no longer from the outside, but decides itself which input is relevant and which is not.
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