It has long been known that learning and long-term memories are produced by the strengthening and weakening of synaptic connections between neurons, called "neuron plasticity", but it has not been clear what determines which synapses are modified during learning in memory formation and by how much. Two articles in the April 18, 2025 issue of the Journal Science cast some light on that mystery:
It turns out which of the many dendrites that a neuron that receives an input signal is important in choosing what rules that neuron will follow, which in turn determines whether the entire neuron will fire or not. Some neurons pay more attention to signals from nearby neurons while others find distant neurons to be more interesting. And synapses in different parts of the brain have different rules. This increases the information storage capacity of a single neuron.
William J Wright, the lead author of the paper says:
“When people talk about synaptic plasticity, it’s typically regarded as uniform within the brain, our research provides a clearer understanding of how synapses are being modified during learning, with potentially important health implications since many diseases in the brain involve some form of synaptic dysfunction.”
Takaki Komiyama another author of the paper says:
“This discovery fundamentally changes the way we understand how the brain solves the credit assignment problem, with the concept that individual neurons perform distinct computations in parallel in different subcellular compartments.”
I wouldn't be surprised if AI scientists take note of this and make a neural net in a similar way to see if that improves performance, but just because nature produces intelligence in a certain way is no guarantee that is the best way to do it.
John K Clark See what's on my new list at Extropolis rew