From f861fb87daaebdcb9e0d9bd6f7a3755728fd4184 Mon Sep 17 00:00:00 2001 From: ocrampal Date: Wed, 8 Jul 2026 11:44:44 +0200 Subject: [PATCH] Create 2026-07-08-traditional-approach-simulation.md --- ...2026-07-08-traditional-approach-simulation.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 elements/neuron/appunti/2026-07-08-traditional-approach-simulation.md diff --git a/elements/neuron/appunti/2026-07-08-traditional-approach-simulation.md b/elements/neuron/appunti/2026-07-08-traditional-approach-simulation.md new file mode 100644 index 0000000..5621730 --- /dev/null +++ b/elements/neuron/appunti/2026-07-08-traditional-approach-simulation.md @@ -0,0 +1,16 @@ +1. Analog / continuous-time physical computation (the most direct help, and real) +The cleanest fit is the oldest idea: don't simulate the dynamics, build a device whose native physics is the dynamics. This is analog computation, and it's not a metaphor — it's a tradition. + +Neuromorphic hardware (memristor crossbars, analog VLSI) is built exactly for this. A memristor's conductance is a physical synaptic weight that changes as a continuous function of the current through it — the structure variable is a material property, updated by the physics of the device, not by a CPU writing to memory. No scheduler: every device updates simultaneously and continuously because they're all just obeying their I-V physics at once. No counted clock: the dynamics evolve in real physical time. This directly answers (a) and (c), and partially (b) — the weights evolve physically. + +Where it helps: this genuinely removes the global state, the scheduler, and the counted clock. A memristor crossbar undergoing your day-dynamics is doing coincidence-detection and integration as physics, for free, in parallel, in real time. This is not speculative — it exists. +Where it stops: standard neuromorphic hardware changes weights, but your model changes structure — it prunes and grows synapses, changing the dimension and connectivity, not just the values. Memristor arrays have fixed topology. So analog hardware solves the "no scheduler / no clock / continuous" problem but not yet the "self-rewriting dimension" problem. That's the frontier — and there is work on structurally reconfigurable and self-organizing neuromorphic substrates, but it's much less mature. So: real help, incomplete. +2. Self-organizing / dissipative systems (help for the self-rewriting part) +The part physics helps most with conceptually is the thing you found hardest: a system whose own structure is built by its own running. This is the domain of non-equilibrium thermodynamics and dissipative structures (Prigogine), and more broadly self-organization. +The key idea you can borrow: structure that is maintained by dissipation. A dissipative structure (a convection cell, a chemical pattern, a flame) is not a fixed object — it is a pattern held in place by a continuous flow of energy through the system. Cut the flow and it vanishes. This is exactly your model's structure: coverage, active-zone capacity, receptor slots are all maintained by ongoing metabolic flow (energy that ratchets, material that circulates), and decay without maintenance. Your "structure builds where flow sustains it, releases where it doesn't" is a dissipative-structure principle almost verbatim. +Why this helps: it tells you the self-rewriting isn't mysterious or unphysical — physics has a whole theory of systems whose organization is a dynamic steady state of matter/energy flow, not a fixed configuration. The equations of your model are the local rules; the structure is the emergent dissipative pattern. You don't implement the structure directly — you implement the flows and the local rules, and let the structure be what the flows sustain. That reframes your implementation problem: don't try to represent the changing program; implement the flows whose sustained patterns are the program. The structure stops being something you update and becomes something that persists only while used — which is what the model already says. +Where it stops: dissipative-structure theory is strong on pattern formation and maintenance but weak on the specific, addressed, memory-like structures your model builds (this synapse, not that one). Convection cells are generic; your synapses are individuated by history. Bridging generic self-organization to individuated, history-dependent memory is not solved. So it gives you the right category of physics but not a ready equation. +3. Field theory / continuum descriptions (help for "no global state, yet coordinated") +Your worry about simultaneity and no-global-state is, in physics, the ordinary situation of a field. A field has no global controller — each point evolves by local rules (the field equations) reading only its immediate neighborhood, yet the whole exhibits coordinated, coherent behavior (waves, coherence, propagation) with no scheduler. Simultaneity is not imposed; it's what "the field at time t" means, and locality is built in (nothing propagates faster than the field's characteristic speed). +Why this helps: it's a proof-of-concept that "purely local rules, no global state, no controller, yet globally coordinated behavior" is not only possible but is how most of physics already works. Your replay-coherence (a pattern carries only where every link is primed) is a propagation phenomenon — it's a field/excitable-medium concept. Excitable media (the theory behind waves in heart tissue, the Belousov-Zhabotinsky reaction, forest-fire models) are the precise physics of "a disturbance propagates only where the medium is primed, and dies at unprimed gaps." That is your night replay, exactly. So excitable-media math (reaction-diffusion, wave propagation in heterogeneous media) is a directly applicable tool for the coherence-is-mechanical claim. +Where it stops: fields are usually fixed-parameter (the medium's properties don't change as the wave passes). Your medium rewrites itself. So you'd need an excitable medium with plastic parameters — reaction-diffusion where the diffusion constants and reaction rates are themselves slow dynamical variables driven by the fast activity. This exists in pockets (adaptive reaction-diffusion, self-modifying excitable media) but is not standard. Again: the right tool, needing an extension. \ No newline at end of file