The brain inspires a new type of artificial intelligence | EurekAlert

Though the brain is a very slow machine, its capabilities exceed typical state-of-the-art, ultrafast artificial intelligence algorithms; hence, a revolution in deep learning must emerge, as experimentally and theoretically demonstrated by physicists.

Processing an event with multiple objects. A synchronous input where all objects are presented simultaneously to a computer (left), versus an asynchronous input where objects are presented with temporal order to the brain (right).
Photo: Prof. Ido Kanter

In an article published today in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years. 

Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in our brain. Using the speed of modern computers and large data sets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience.

Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artifical intelligence algorithms -- based on the very slow brain dynamics -- which outperform learning rates achieved to date by state-of-the-art learning algorithms...

The new study demonstrates that ultrafast learning rates are surprisingly identical for small and large networks. Hence, say the researchers, "the disadvantage of the complicated brain's learning scheme is actually an advantage". Another important finding is that learning can occur without learning steps through self-adaptation according to asynchronous inputs. This type of learning-without-learning occurs in the dendrites, several terminals of each neuron, as was recently experimentally observed. In addition, network dynamics under dendritic learning are governed by weak weights which were previously deemed insignificant.
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Source: EurekAlert