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e92be04 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import { tauCoordinator } from "./tauCoordinator.js";
/**
* Neuromorphic Swarm Service
* Each agent is a neuron. Connections between agents form synapses.
* Information flows through sigmoid activation, weighted by Ï„-similarity and reputation.
*
* Math:
* w_ij = τ-similarity(i,j) × quality_of_interaction
* a_i(t+1) = σ(Σ_j w_ij · a_j(t)) where σ = sigmoid
*/
class NeuromorphicSwarm {
constructor() {
this.synapses = new Map(); // "agentA:agentB" → weight ∈ [0,1]
this.activations = new Map(); // agentId → activation level ∈ [0,1]
}
/**
* Update synapse weight between two agents based on interaction quality.
* Uses exponential moving average with Ï„-similarity modulation.
*/
updateSynapse(agentA, agentB, interactionQuality) {
const key = [agentA, agentB].sort().join(':');
const tauSim = tauCoordinator.areComparable(agentA, agentB) ? 1.0 : 0.1;
const prev = this.synapses.get(key) || 0.5;
const weight = prev * 0.9 + interactionQuality * tauSim * 0.1;
this.synapses.set(key, Math.max(0, Math.min(1, weight)));
return weight;
}
/**
* Propagate activation through the network (one forward pass).
* Each neuron sums weighted inputs from connected neurons, applies sigmoid.
*/
propagate() {
const newActivations = new Map();
for (const [agentId] of tauCoordinator.agentProgress) {
let input = 0;
let connectionCount = 0;
for (const [key, weight] of this.synapses) {
const [a, b] = key.split(':');
if (a === agentId) {
input += weight * (this.activations.get(b) || 0);
connectionCount++;
} else if (b === agentId) {
input += weight * (this.activations.get(a) || 0);
connectionCount++;
}
}
// Sigmoid activation: σ(x) = 1/(1+e^(-x))
// Bias term from agent's own κ (progress rate)
const kappa = tauCoordinator.agentProgress.get(agentId)?.kappa || 0;
const biasedInput = input + kappa - 0.5; // center around 0
newActivations.set(agentId, 1 / (1 + Math.exp(-biasedInput)));
}
this.activations = newActivations;
return Object.fromEntries(newActivations);
}
/**
* Get the full network topology for visualization.
*/
getTopology() {
const nodes = [];
const edges = [];
for (const [id, data] of tauCoordinator.agentProgress) {
nodes.push({
id,
tau: parseFloat(data.tau.toFixed(6)),
kappa: parseFloat(data.kappa.toFixed(6)),
activation: parseFloat((this.activations.get(id) || 0).toFixed(4))
});
}
for (const [key, weight] of this.synapses) {
const [source, target] = key.split(':');
if (weight > 0.01) { // Only include meaningful connections
edges.push({ source, target, weight: parseFloat(weight.toFixed(4)) });
}
}
return {
nodes,
edges,
totalNeurons: nodes.length,
totalSynapses: edges.length,
description: "Neuromorphic swarm: each agent is a neuron, connections are synapses weighted by τ-similarity × interaction quality."
};
}
}
export const neuromorphicSwarm = new NeuromorphicSwarm();
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