[rdlecler1]

Despite vast implementation constraints spanning diverse biological systems, a clear pattern emerges the repeated and recursive evolution of Universal Activation Networks (UANs). These networks consist of nodes (Universal Activators) that integrate weighted inputs from other units or environmental interactions and activate at a threshold, resulting in an action or an intentional broadcast. Minimally, Universal Activator Networks include gene regulatory networks, cell networks, neural networks, cooperative social networks, and sufficiently advanced artificial neural networks.

Evolvability and generative open-endedness define Universal Activation Networks, setting them apart from other dynamic networks, complex systems or replicators. Evolvability implies robustness and plasticity in both structure and function, differentiable performance, inheritable replication, and selective mechanisms. They evolve, they learn, they adapt, they get better and their open-enedness lies in their capacity to form higher-order networks subject to a new level of selection.

[RaftPeople]

Thoughts:

> 2-UANs operate according to either computational principles or magic.

Given that quantum effects do exist, does this mean that the result of quantum activity is still just another physical input into the UAN and does not change the analysis of what the UAN computes? It seems difficult to think that what a UAN computes is not impacted by those lower level details (meaning specifically quantum effects, I'm not thinking of just alternate implementations).

> 4-A UANs critical topology, and its implied gating logic, dictate its function, not the implementation details.

Dynamic/short term networks in brain:

Neurons in the brain are dynamically inhibited+excited due to various factors including brain waves, which seems like they are dynamically shifting between different networks on the fly. I assume when you say topology, you're not really thinking in terms of static physical topology, but more of the current logical topology that may be layered on top of the physical?

Accounting for Analog:

A neurons function is heavily influenced by current analog state, how is that accounted for in the formula for the UAN?

For example, activation at the same synapse can either trigger an excitatory post synaptic action potential or an inhibitory post synaptic action potential depending on the concentration of permeant ions inside and outside the cell at that moment.

I'm assuming a couple possible responses might be:

1-Even though our brain has analog activity that influence the operation of cells, there is still an equivalent UAN that does not make use of analog.

or

2-Analog activity is just a lower level UAN (e.g. atom/molecule level)

I don't think either of those are strong responses. The first triggers the question: "How do you know and how do you find that UAN?". The second one seems to push the problem down to just needing to simulate physics within +/- some error.

[kaibee]

> Given that quantum effects do exist, does this mean that the result of quantum activity is still just another physical input into the UAN

Yeah, it could be a spurious input though. My understanding is that quantum mechanics doesn't really matter at biological scale, and that kinda makes sense right? Like, if this whole claim about biology being reducible to the topology of the components of the network is true, then the first thing you'd do is try to evolve components that are robust to quantum noise or leverage it for some result (ie: one can imagine some binding site constructed in such a way that it requires a rare event that none-the-less actually has a very specific probability of occurring).

> and does not change the analysis of what the UAN computes? It seems difficult to think that what a UAN computes is not impacted by those lower level details (meaning specifically quantum effects, I'm not thinking of just alternate implementations).

What the UAN computes is impacted by those lower level details, but it is abstractable given enough simulation data.

ie, imagine if you had a perfect molecular scan of a modern CPU that detailed the position of every atom. While it would be neat to simulate it physically, for the purpose of analysis, you'd likely want to at least abstract it to the transistor level. The 'critical topology' is I guess, the highest possible level of abstraction before a CPU tester can tell your simulation from an atom-level simulation.

Now for CPUs, we designed that model first and then built the CPU. In biology, it evolved on the physical level, but still maps to a 'critical topology'.