A New Brain-Inspired Learning Method for AI Saves Memory and Energy
Despite the frequent analogies, today’s AI operates on very different principles to the human brain. Now researchers have proposed a new learningmethodmore closely tied to biology, which they think could help us approach the brain’s unrivaled efficiency.
Modern deep learning is at the very least biologically-inspired, encoding information in the strength of connections between large networks of individual computing units known as neurons. Probably the biggest difference, though, is the way these neurons communicate with each other.
Artificial neural networks are organized into layers, with each neuron typically connected to every neuron in the next layer. Information passes between layers in a highly synchronized fashion as numbers falling in a range that determines the strength of the connection between pairs of neurons.
Biological neurons, on the other hand, communicate by firing off electrical impulses known as spikes, and each neuron does so on its own schedule. Connections are not neatly divided into layers and feature many feedback loops that means the output of a neuron often ends up impacting its input somewhere down the line.
This spike-based approach is vastly more energy efficient, which is why training the most powerful AI requireskilowatts of electricitywhile the brain uses just 20 watts. That’s led to growing interest in the development of artificial spiking neural networks as well as so-called neuromorphic hardware—computer chips that mimic the physical organization and principles of the brain—that could run them more efficiently.
But our understanding of these spike-based approaches is still underdeveloped, and they struggle to reach the performance of more conventional artificial neural nets. Now though, researchers from the Graz University of Technology in Austria think they may have found a way to approach the power of deep learning using a biological plausible learning approach that works with spiking neural networks.
In deep learning the network is trained by getting it to make predictions on the data and then assessing how far off it is.
