PMID-32381648 A model for structured information representation in neural networks in the brain
- Using randomly connected E/I networks, suggests that information can be "bound" together using fast Hebbian STDP.
- That is, 'assemblies' in higher-level areas reference sensory information through patterns of bidirectional connectivity.
- These patterns emerge spontaneously following disinihbition of the higher-level areas.
- Find the results underwhelming, but the discussion is more interesting.
- E.g. there have been a lot of theoretical and computational-experimental work for how concepts are bound together into symbols or grammars.
- The referenced fMRI studies are interesting, too: they imply that you can observe the results of structural binding in activity of the superior temporal gyrus.
- I'm more in favor of dendritic potentials or neuronal up/down states to be a fast and flexible way of maintaining 'symbol membership' --
- But it's not as flexible as synaptic plasticity, which, obviously, populates the outer product between 'region a' and 'region b' with a memory substrate, thereby spanning the range of plausible symbol-bindings.
- Inhibitory interneurons can then gate the bindings, per morphological evidence.
- But but, I don't think anyone has shown that you need protein synthesis for perception, as you do for LTP (modulo AMPAR cycling).
- Hence I'd argue that localized dendritic potentials can serve as the flexible outer-product 'memory tag' for presence in an assembly.
- Or maybe they are used primarily for learning, who knows!
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