A Potential Mechanism for Gibsonian Resonance: Behavioral Entrainment Emerges from Local Homeostasis in an Unsupervised Reservoir Network

Thanks Daniel for, oh sorry. Hello and welcome. It is February 9th, 2023. We're in Octave Guest Stream number 36.1 with Ben Falundace. So Ben, thank you for joining. We're going to have a presentation and then a discussion period. So thanks again for joining. Looking forward to your presentation. Yeah, thanks so much for having me. I'm really excited to connect with the Active Inference community on this stuff. So hopefully some people are in the live stream and we can have some discussion at the end of this. So I'm going to talk about this recent preprint that we dropped with my collaborators, Jeff Yoshimi, Bill Warren, and Michael Spivey. Titles right there. So I'll just jump into it. So the backdrop that we're thinking about when we came up with this paper was that most cognitive scientists think that cognition is about mental representations. And most of them think that those mental representations are constituted by brain dynamics or in philosophy jargon you might say supervene on brain dynamics. And so hopefully that's not a very controversial statement. And what we're trying to do in this work is take one step towards a non-representational account of the central nervous system, meaning the brain and kind of connected parts of the central nervous system. So why should we want to do this? What's the problem with representations? So I'm just going to present a sample of four problems. These are not, this is not a comprehensive view of what could be wrong with representations, but these are some things that might encourage you to look for an alternative. The first one is that just because you have some encoding or correspondence between internal mental activity and something out in the world doesn't get you to content, to meaning of the representations. And that is basically getting at something that's called the symbol grounding problem I'll talk about in a second. Beyond that, we can note that in central nervous system activity, there's a high degree of context dependence, even down to the tunings of individual neurons. So given a different task or a different setting, you see what look like different encodings appearing. So that seems like it would be a problem if you're actually trying to use that for an encoding, because you have to keep track of how those encodings are changing according to the context. The third problem I'm going to mention is a recent finding in the neuroscience literature. It's called representational drift. And the point here is that when you see what looks like encodings in the brain, if you look over long enough time scales, and these are not actually very long time scales, maybe even a day or a couple of days, you find that those encodings move around in the brain. So again, if they were to be used by the brain as an encoding, it not only has to keep track of what is the mapping, but how is the mapping changing over time. That's a problem because the system that needs to keep track of how it's changing is also the thing that's changing. So it's not clear to me if that could even work or what kind of you know computational mechanism you'd need to make that work. And the fourth thing is the argument that we just don't need representations, at least for a lot of tasks. So this is a major point in the ecological and embodied literature is that a lot of the major problems that organisms are trying to solve appear solvable without using any complex internal representations. So I'll just briefly give you an example of each of these. The first one I'm going to rely on this really nice 2019 paper from Romain Bretta. I apologize if I'm pronouncing his name incorrectly. And one example that he shows in this paper is we imagine a scenario in which a neuroscientist is doing a single cell recording of the tuning of a visual neuron. And they're varying wavelengths, right? So they're varying colors, presenting various colors, and you're seeing which color elicits the greatest response from this neuron. So…