The brain consists of several neurons that communicate with each other. Each neuron gathers its varied inputs and sends a spike to the neurons it connects. All high-order brain functions are built on the dynamics of such massive, intricate neural networks.
In a new study, a group of scientists has experimentally demonstrated frequent periods of silence in which a neuron fails to respond to its inputs. The brain is made up of unreliable neurons, unlike electronic systems, which are quick and reliable.
Prof. Ido Kanter of Bar-Ilan University‘s Department of Physics and Gonda (Goldschmied) Multidisciplinary Brain Research Center, who led the study, said, “A logic-gate always gives the same output to the same input, otherwise electronic devices like cellphones and computers, which are composed of many billions of interconnected logic-gates, wouldn’t function well.”
“Comparing the unreliability of the brain to a computer or cellphone: one time your computer answers 1+1=2 and other times 1+1=5 or dialing 7 in your cellphone many times can result in 4 or 9. Silencing periods would appear to be a major disadvantage of the brain, but our latest findings have shown otherwise.”
As opposed to what one might think, researchers found that neuronal silencing periods are not a disadvantage representing biological limitations but rather an advantage for temporal sequence identification.
Yuval Meir, a co-author of the study, said, “Assume you would like to remember a phone number, 0765… For example, neurons that were active when the digit 0 was presented might be silenced when the next digit 7 is presented. Consequently, each digit is trained on a different dynamically created sub-network, and this silencing mechanism enables our brain to identify sequences efficiently.”
In addition to being the foundation of a new form of cryptosystem for handwriting recognition at automated teller machines (ATMs), the brain silencing process is a proposed source for a new AI mechanism. Instead of typing a PIN into the ATM, this cryptosystem enables users to write their identification number (PIN) on an electronic board.
In addition to identifying the correct PIN, sequence identification, based on neuronal silencing periods, can also identify the user’s handwriting style and the timing in which each digit of the PIN is written on the board. These added features safeguard against stolen cards, even if a thief knows the user’s PIN.