Locomotion is a feat executed effortlessly in nature, so much so that it wouldn't be farfetched to say we make it look easy. Us humans have had a hard time conceptualizing a system to produce a fluid locomotion system.
The neurons in our brain are specialized cells that communicate impulses, throughout the last century, neuroscience research has brought about a false belief that neuronal cells are the only ones that process and retain information. Slowly but surely, this "dogma of neurocomputation" is caving in.
Apart from neurons, our brains also feature astrocytes (the astro part comes from their shape resembling a star), for over a century, neuroscience community thought their role is just to "fill up space" between neurons. Studies that are more recent, however, have demonstrated that these cells play important roles in several brain functions such as learning and Central Pattern Recognition (CPG).
The latter is the basis for many significant cyclical actions such as breathing, walking, swimming, and whatnot.
While we are aware of the astrocytes and their function now, we still build our artificial neural networks modeling the neurons only. Seeing the gap in this field, academics at Rutgers University are utilizing Intel Loihi chips to develop algorithms that mimic the brain function that take astrocytes into the picture as well. The article is pre-published at arXiv and the team plans to present it in July on ICONS 2020 conference.
Konstantinos Michmizos, lead researcher on the study and assistant professor at Rutgers told TechXplore: "Everything that artificial neural nets do, and they do a lot these days, is based on the neurocomputing dogma that 'brain equals neurons. Astrocytes are two to 10 times more plentiful than neurons. The impact of understanding or mimicking what more of half the brain is doing is enormous."
First of its kind
The team led by Michmizos has conducted the first-ever study that aimed to understand and replicate the human brain in its entirety. Utilizing astrocytes as a second processing system has not been tried before in neural networking.
Drawing attention on the rewarding and fascinating nature of their new direction Michmizos also added: "The main objective behind our recent study was to understand the mysterious language that neurons and astrocytes use to talk to each other as we learn, think and act on our world, by building algorithms inspired by this mysterious dialog in our brain."