Memory capacity of brain is about 10 times more than previous estimations, which is in the petabyte range, i.e. almost equivalent to the entire Web.
Memories and thoughts in our brain are caused by a distinct pattern of chemical and electrical activity. Our brain has branches of neurons, which are connected to each other through synapses. Signals and information travel through these synapses with the help of neurotransmitters. Each neuron can be connected to thousands of other neurons through these synapses. Memory capacity of neurons is thought to be dependent on synapse size and strengths.
“We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse,” Researchers wrote in the paper.
Researchers from Salk Institute found that our brain can store a huge amount of information, which is equivalent to the entire Web, and this storage of information can be accomplished without utilization of much energy, i.e. our brain generates about 20 watts of continuous power. So, the study of brain could help scientists and engineers to create computers that can store a huge amount of information without using much energy.
“This is a real bombshell in the field of neuroscience,” says Terry Sejnowski, who is a Salk professor and author of the paper. “We discovered the key to unlocking the design principle for how hippocampal neurons function with low energy but high computation power. Our new measurements of the brain’s memory capacity increase conservative estimates by a factor of 10 to at least a petabyte, in the same ballpark as the World Wide Web.”
“The implications of what we found are far-reaching,” said Sejnowski. “Hidden under the apparent chaos and messiness of the brain is an underlying precision to the size and shapes of synapses that was hidden from us.”
Salk (2016). Memory capacity of brain is 10 times more than previously thought. http://goo.gl/ShA3Gs
Bartol, T., Bromer, C., Kinney, J., Chirillo, M., Bourne, J., Harris, K., & Sejnowski, T. (2015). Nanoconnectomic upper bound on the variability of synaptic plasticity eLife, 4 DOI: 10.7554/eLife.10778