In some ways, we do work like computers and use distributed networks of firing neurons in important ways.
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“I also think that our brains use far different learning algorithms than our current deep learning systems,” sai Jay McClelland, a noted cognitive scientist at Stanford University. “I’m taking inspiration from a fairly-recently discovered new form of learning called Behavioral Time Scale Synaptic plasticity [BTSP] to think about our brains might have come to be able to learn with far less training data than we currently need to train contemporary AI systems.”
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This is the basic understanding that underlies deep learning models used in AI. But the authors of the Cell study propose that in human brains, what’s actually going on is a different form of synaptic plasticity, BTSP, which allows for far fewer firings of neurons to create a memory — in fact, you might just need a single “event” to result in learning. [B]TSP works well because it doesn’t need the kind of overlap that Hebbian-style [AI] plasticity requires.
