Does Monty implement a biological mechanism for strengthening synaptic connections between neurons?

[HumbleTraveller]

@vclay

I am not 100% sure what you mean with

whether the system relies only on statistical updates of probabilities and connections, without a separate “plasticity layer”

I think he might be referring to some kind of mechanistic layer of control, one which helps modulate learning. Something analogous to neuromodulation, or perhaps the brain’s glial systems?

@srgg6701

Just wanted to chime in on your latest reponse

@srgg6701

All key functions of the brain responsible for producing intelligence must be understood and reproduced to the extent that we can claim genuine intelligence

The TBP team’s primary focus has been on the neocortex, and they’re just now starting to expand out into things like the thalamus. I suspect they’re refrain from recreating large portions of the limbic system though, as many of those systems serve as motivational subsystems. To me, it seems like the team wants to outsource goal generation to external control sources (e.g., biological humans). If you’re interested, heres a conversation we had a while ago (pertaining to incorrporating hippocampal-enterihnal complex principles): Hippocampal - Entorhinal Complex - #3 by nleadholm

@srgg6701

which matters a lot for such an energy-hungry organ.
However, for a digital “brain,” this probably doesn’t apply, does it?

In my opinion, one of the things which most sets Monty apart is its energy efficency (compared to a conventional LLM). I’d argue that this is just as important a topic in digital brains as they are biological ones, especially when we factor in things like robotics.

Re. Neuroplasticity analog in Monty’s codebase

I’m curious to know vclays thoughts too, but here is how I’ve come to view it: Each learning module within a Monty instance maintains a graph-based memory of sensory features and spatial relationships. In a way, these kind of act like cortical microcircuits.

For instance, Nodes might be considered analagous to ‘neurons’ in that they can represent surface points, curvatures, object parts, et cetera.

The Edges found between those Nodes act kind of like synaptic associations. They represent the spatial displacements and co-activation relations between shared features.

Edge weights then are adjusted when the system updates evidence for or against a given hypothesis. So when new sensory data arrive, these graphs can become expanded, pruned, strengthed depending on statistical consistancy between those inputs. Not dissimilar from principles of neuroplasticity.

I’m not sure if they plan on incorporating a form of neurogenesis. But I don’t believe they have that implemented yet.