Post #3195791

@GabrielBena@neuromatch.social Finally, we made use of the fact that our networks are recurrent (which was a necessary restriction of the simple architecture that we used) and checked how specialisation changed over time. Intriguingly, it decreases over time. But there's more. This drop in specialisation happens faster the more synapses between the modules and the less noise there is. This looks like maybe specialisation falls simply as a result of how much net communication bandwidth there is between the modules. This raises the question: maybe specialisation isn't as simple as we think. Perhaps to some extent it's just a measurement artifact of limited communication bandwidth? Or maybe, understanding information flow is key to building systems that can specialise and generalise? These are the sorts of questions we're following up on now, hopefully we'll have more to say about that soon, but in the meantime we'd love to discuss some of the issues and questions raised with you all. What do you think?