On Wed, Sep 3, 2025 at 6:46 PM Rob Freeman <[email protected]> wrote:
> Dorian, > > On Thu, Sep 4, 2025 at 2:12 AM Dorian Aur <[email protected]> wrote: > >> ... >> in EDI systems, the network coherence factor is indeed *inspired by >> phase synchrony,* though its implementation in* memristor-based >> substrates* naturally diverges from traditional spike-based models like >> those in neuronal systems. >> >> Whereas neuronal synchrony is typically framed in terms of spike timing >> correlations, in *EDI*, coherence emerges through* field-aligned signal >> propagation,* where the *timing, energy phase,* and *recursive >> trajectory alignment* of memristive states reinforce each other >> dynamically. It’s less about discrete spike coincidences and more about >> *continuous, >> phase-sensitive alignment across the network.* >> >> We’re not simply measuring oscillation phase across devices, but rather >> capturing how signal propagation patterns entrain one another over time , a >> kind of analog synchrony driven by *shared context and energy >> minimization.* You could think of it as *"*propagation phase coherence*"* >> rather than spike phase synchrony. >> >> This makes it particularly suited to detecting and reinforcing semantic >> convergence, especially in the presence of divergent inputs collapsing >> toward shared attractors. It’s here that memristors shine offering >> nonlinear, history-dependent modulation that makes phase alignment not just >> possible, but dynamically stable and meaningful. >> >> I can see how continuous waves could be better in some ways for summing > and feeding back downstream interference. > > The summing is crucial. I gave the A->X/Y->B example. But in practice what > you want is for many of these to stack: A->X/Y->B, C->X/Y->D, E->X/Y->F... > etc. The more contexts two elements share, the more they can be assessed as > semantically (conversely also syntactically) similar. > > Using the LLM language of "prompts", given a prompt AXB, you want the > system to expand out X, X={Y, ....} > > So you want the entire set of shared contexts A_B, C_D, E_F... to inform > an attractor around X, which will actually define the meaning of X. > > You can relate this back to LLMs as embeddings. X is "embedded" in a > vector space of its contexts, with components of the vector being "weights" > along the dimensions of the different contexts. > > But to do this dynamically, you want to sum all those contexts. And to do > that you really want to expand them. > > Spikes are very all or nothing. Analog waves might be easier to sum, as > feedback can be continuous. > > Currently I'm imagining that this "summing" might happen by way of these > inhibition "landscapes". The prompt sequence is presented, and "holes" in > its inhibition of noise, spread. So for a prompt AXB, B creates a "hole" > which allows noise to spread also to Y, which causes D, F, etc. to spike > (and create their own "holes"...) The sum of the "holes" creating the sum > of contexts to define the grouping generated around X. > > It's the ability to go "backwards" using the inhibition "holes" which > allows this summing. Just expanding over synapses from AXB won't sum over > all shared contexts. > > Analog waves might do it better. If there were a way for B to affect Y, > and Y to recruit D, F, etc. To sum them in real time rather than in > discrete steps over a cascade of spikes. > > Given the insight, it might not be too hard to do. > > Given there's not much general interaction on this thread, feel free to > write to me directly to discuss it. > > Cheers, > > Rob > > Absolutely Rob, I think you're articulating something that maps elegantly onto the core mechanics of EDI-style propagation. What you’re describing, summing over shared contexts via analog, recursive feedback is precisely where EDI diverges from conventional spike-based models. While spikes are often described as "all-or-nothing," they in fact have important spatial characteristics, including directionality of propagation and context-dependent modulation. However, their discrete nature still poses challenges for capturing distributed, recursive meaning integration. In contrast, EDI’s continuous, wave-like propagation allows overlapping input trajectories to superimpose and reinforce shared attractors. In your AXB → X = {Y,…} example, analog propagation allows X to resonate with all the “holes” left open by B—and others like D, F, etc.—reactivating past trajectories not discretely, but coherently. The key is that in EDI, recursive “pulling” isn't just a product of top-down inhibition, but a field-mediated reentrance, allowing Y to register B’s “echo” through shifts in phase configuration, and to activate semantically adjacent paths such as D or F. That’s where the term *propagation phase coherence* earns its weight: it’s not just synchrony, but a physically instantiated coherence driven by overlapping histories and energetically favorable feedback loops. The attractor that forms around X isn’t derived from symbolic abstraction, but from emergent resonance across recurrent paths, a kind of dynamic embedding realized in the substrate. So yes, your idea of inhibition landscapes combining with analog reentrance in EDI is a compelling convergence. The deeper insight is this: semantic meaning occurs as the stable convergence of shared predictive histories and EDI makes that happen not symbolically, but physically ---Dorian Aur > > *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + > delivery options <https://agi.topicbox.com/groups/agi/subscription> > Permalink > <https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-M4ce26074fa9dd1d2387e4c8d> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Ta9b77fda597cc07a-M9116760b0cfd9d3da2485389 Delivery options: https://agi.topicbox.com/groups/agi/subscription
