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In the animal kingdom, energy conservation is essential for survival. That’s why our brains have evolved to be extremely energy and resource efficient at storing and processing information. Since the 1980s, computational scientists have tried to mimic brain structure and function in the hopes of achieving such efficient, fast processing of complex data. However, researchers have not yet succeeded in creating functional, low-memory solutions that work in real-world scenarios, where energy is limited and training data is received over time.
Published in Neuromorphic Computing & Engineering, a new brain-inspired algorithm developed by researchers at the Okinawa Institute of Science and Technology (OIST) demonstrates promise for achieving practical applications under realistic constraints. Based on an olfactory (scent classification) system, the algorithm was trained and tested on experimental odor data sets, showing accurate classification of these scents, particularly in scenarios with limited training data.
The fruit fly is not to be sniffed at
To create their new algorithm, the researchers looked to a tiny source of inspiration. Smaller than a poppy seed, yet stuffed full of around 140,000 neurons, the fruit fly brain is ubiquitous within modern neuroscience research.
