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interactive.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="viewport" content="width=device-width, initial-scale=1.0">
<title>Neural Network Animation</title>
<style>
body, html {
margin: 0;
padding: 0;
height: 100%;
display: flex;
justify-content: center;
align-items: center;
background: radial-gradient(circle, #1a1a1a, #000);
overflow: hidden;
font-family: Arial, sans-serif;
}
canvas {
display: block;
width: 100%;
height: 100%;
}
.controls {
position: absolute;
top: 10px;
left: 10px;
background: rgba(255, 255, 255, 0.1);
padding: 10px;
border-radius: 5px;
}
.controls button, .controls select {
margin: 5px;
padding: 5px 10px;
background: #fff;
border: none;
border-radius: 3px;
cursor: pointer;
}
</style>
</head>
<body>
<div class="controls">
<select id="agentTypeSelector">
<option value="creativity">Creativity</option>
<option value="ethical">Ethical</option>
<option value="linguistic">Linguistic</option>
</select>
<button id="rewardButton">Reward</button>
<button id="punishButton">Punish</button>
</div>
<canvas id="neuralNetCanvas"></canvas>
<script>
function createNeuralNetAnimation() {
const canvas = document.getElementById('neuralNetCanvas');
const ctx = canvas.getContext('2d');
const neurons = [];
const mentalAnchors = [];
const chaosAgents = [];
const permanentNodes = [];
const initialNeuronCount = 100;
const chaosAgentLifespan = 10000; // 10 seconds
const maxDistance = 150;
const friction = 0.9;
const pulseSpeed = 0.2;
const expansionFactor = 1.5;
const trailLength = 20;
const attractionStrength = 0.02;
const repulsionStrength = 0.1;
const repellentStrength = 0.15; // Increased repellent strength
const creativityStrength = 0.7;
const ethicalStrength = 0.5;
const linguisticStrength = 0.6;
const minDistanceToAnchor = 50;
const neuronCreationRate = 5000; // 5 seconds
const neuronDeletionRate = 10000; // 10 seconds
const neuronsPerDeletion = 1;
const maxConnectionsPerNeuron = 5;
const minDistanceBetweenNeurons = 20;
const maxNeurons = 200;
const learningRate = 0.01;
const survivalCyclesThreshold = 5; // Threshold for permanent node
let mouse = { x: canvas.width / 2, y: canvas.height / 2 };
let rewardMode = false;
let punishMode = false;
let reductionCycleCount = 0;
let scale = 1;
let panX = 0, panY = 0;
let isDragging = false;
let startX, startY;
const colors = {
creativity: '#ff6b6b',
ethical: '#32cd32',
linguistic: '#8a2be2'
};
function resizeCanvas() {
canvas.width = window.innerWidth;
canvas.height = window.innerHeight;
}
window.addEventListener('resize', resizeCanvas);
resizeCanvas();
canvas.addEventListener('mousemove', event => {
const rect = canvas.getBoundingClientRect();
mouse.x = event.clientX - rect.left;
mouse.y = event.clientY - rect.top;
});
canvas.addEventListener('mousedown', event => {
isDragging = true;
startX = event.clientX - panX;
startY = event.clientY - panY;
});
canvas.addEventListener('mousemove', event => {
if (isDragging) {
panX = event.clientX - startX;
panY = event.clientY - startY;
ctx.setTransform(scale, 0, 0, scale, panX, panY);
}
});
canvas.addEventListener('mouseup', () => isDragging = false);
canvas.addEventListener('mouseout', () => isDragging = false);
canvas.addEventListener('wheel', event => {
scale += event.deltaY * -0.01;
scale = Math.min(Math.max(.125, scale), 4);
ctx.setTransform(scale, 0, 0, scale, panX, panY);
});
canvas.addEventListener('click', event => {
const rect = canvas.getBoundingClientRect();
const agentType = document.getElementById('agentTypeSelector').value;
chaosAgents.push({
x: event.clientX - rect.left,
y: event.clientY - rect.top,
radius: Math.random() * 10 + 5,
speedX: (Math.random() - 0.5) * 2,
speedY: (Math.random() - 0.5) * 2,
strength: agentType === 'creativity' ? creativityStrength : agentType === 'ethical' ? ethicalStrength : linguisticStrength,
type: agentType,
color: colors[agentType],
lifespan: chaosAgentLifespan
});
});
document.getElementById('rewardButton').addEventListener('click', () => {
rewardMode = true;
punishMode = false;
});
document.getElementById('punishButton').addEventListener('click', () => {
rewardMode = false;
punishMode = true;
});
function createNeuron(x, y) {
return {
x: x !== undefined ? x : Math.random() * canvas.width,
y: y !== undefined ? y : Math.random() * canvas.height,
baseRadius: Math.random() * 3 + 2,
radius: Math.random() * 3 + 2,
speedX: (Math.random() - 0.5) * 2,
speedY: (Math.random() - 0.5) * 2,
pulseOffset: Math.random() * 2 * Math.PI,
influenced: false,
connections: 0,
trail: [],
age: 0,
energy: 1,
lifespan: 10000, // Initial lifespan
survivalCycles: 0,
isPermanent: false,
connectionStrengths: {}
};
}
function createNeurons(count, x, y) {
for (let i = 0; i < count; i++) {
neurons.push(createNeuron(x, y));
}
}
function createMentalAnchors() {
const brainShape = [
{ x: 0.2, y: 0.3 }, { x: 0.8, y: 0.3 }, // top-left, top-right
{ x: 0.2, y: 0.7 }, { x: 0.8, y: 0.7 }, // bottom-left, bottom-right
{ x: 0.5, y: 0.1 }, { x: 0.5, y: 0.9 }, // center-top, center-bottom
{ x: 0.35, y: 0.5 }, { x: 0.65, y: 0.5 }, // mid-left, mid-right
{ x: 0.4, y: 0.25 }, { x: 0.6, y: 0.25 } // inner-top-left, inner-top-right
];
brainShape.forEach(pos => {
mentalAnchors.push({
x: pos.x * canvas.width,
y: pos.y * canvas.height
});
});
}
function identifyClusters() {
const clusters = [];
const visited = new Set();
neurons.forEach((neuron, i) => {
if (!visited.has(i)) {
const cluster = [];
const stack = [i];
visited.add(i);
while (stack.length > 0) {
const currentIndex = stack.pop();
const currentNeuron = neurons[currentIndex];
cluster.push(currentNeuron);
neurons.forEach((otherNeuron, j) => {
if (!visited.has(j) && Math.hypot(currentNeuron.x - otherNeuron.x, currentNeuron.y - otherNeuron.y) < maxDistance) {
stack.push(j);
visited.add(j);
}
});
}
clusters.push(cluster);
}
});
return clusters;
}
function updateNeurons() {
neurons.forEach((neuron, i) => {
neuron.influenced = false;
// Calculate distance from mouse
const dx = neuron.x - mouse.x;
const dy = neuron.y - mouse.y;
const distance = Math.hypot(dx, dy);
const maxMouseEffectDistance = 150;
// Move neuron away from mouse and expand radius
if (distance < maxMouseEffectDistance) {
const angle = Math.atan2(dy, dx);
const force = (maxMouseEffectDistance - distance) / maxMouseEffectDistance;
neuron.speedX += Math.cos(angle) * force * 1.5;
neuron.speedY += Math.sin(angle) * force * 1.5;
neuron.radius = neuron.baseRadius * (1 + force * expansionFactor);
} else {
neuron.radius = neuron.baseRadius + Math.sin(neuron.pulseOffset) * pulseSpeed;
}
// Apply attractive force towards the nearest mental anchor
let nearestAnchor = null;
let nearestDistance = Infinity;
mentalAnchors.forEach(anchor => {
const dx = neuron.x - anchor.x;
const dy = neuron.y - anchor.y;
const distance = Math.hypot(dx, dy);
if (distance < nearestDistance) {
nearestDistance = distance;
nearestAnchor = anchor;
}
});
if (nearestAnchor) {
const anchorDx = nearestAnchor.x - neuron.x;
const anchorDy = nearestAnchor.y - neuron.y;
const angle = Math.atan2(anchorDy, anchorDx);
if (nearestDistance > minDistanceToAnchor) {
neuron.speedX += Math.cos(angle) * attractionStrength;
neuron.speedY += Math.sin(angle) * attractionStrength;
} else {
neuron.speedX -= Math.cos(angle) * repulsionStrength;
neuron.speedY -= Math.sin(angle) * repulsionStrength;
}
}
// Apply chaos agent influence
chaosAgents.forEach(agent => {
const agentDx = agent.x - neuron.x;
const agentDy = agent.y - neuron.y;
const agentDistance = Math.hypot(agentDx, agentDy);
if (agentDistance < agent.radius * 10) {
const angle = Math.atan2(agentDy, agentDx);
neuron.speedX += Math.cos(angle) * agent.strength;
neuron.speedY += Math.sin(angle) * agent.strength;
neuron.influenced = true;
// Different behavior based on the type of chaos agent
if (agent.type === 'creativity') {
neuron.radius += Math.random() * 2; // Increase size
neuron.speedX += (Math.random() - 0.5) * 0.5; // Increase randomness in speed
neuron.speedY += (Math.random() - 0.5) * 0.5;
} else if (agent.type === 'ethical') {
neuron.speedX *= 1.1; // Increase speed
neuron.speedY *= 1.1;
} else if (agent.type === 'linguistic') {
neuron.radius += Math.random(); // Moderate size increase
neuron.speedX += (Math.random() - 0.5) * 0.2; // Slightly increase randomness
neuron.speedY += (Math.random() - 0.5) * 0.2;
}
}
});
// Apply repellent force to other neurons
neurons.forEach((otherNeuron, j) => {
if (i !== j) {
const dx = neuron.x - otherNeuron.x;
const dy = neuron.y - otherNeuron.y;
const distance = Math.hypot(dx, dy);
if (distance < minDistanceBetweenNeurons) {
const angle = Math.atan2(dy, dx);
const force = (minDistanceBetweenNeurons - distance) / minDistanceBetweenNeurons;
neuron.speedX += Math.cos(angle) * force * repellentStrength;
neuron.speedY += Math.sin(angle) * force * repellentStrength;
otherNeuron.speedX -= Math.cos(angle) * force * repellentStrength;
otherNeuron.speedY -= Math.sin(angle) * force * repellentStrength;
}
}
});
// Apply friction
neuron.speedX *= friction;
neuron.speedY *= friction;
// Update position
neuron.x += neuron.speedX;
neuron.y += neuron.speedY;
// Keep neurons within canvas bounds
if (neuron.x < 0 || neuron.x > canvas.width) neuron.speedX *= -1;
if (neuron.y < 0 || neuron.y > canvas.height) neuron.speedY *= -1;
// Update trail
neuron.trail.push({ x: neuron.x, y: neuron.y });
if (neuron.trail.length > trailLength) neuron.trail.shift();
// Pulsate neuron
neuron.pulseOffset += 0.1;
// Age neuron and reduce energy
neuron.age++;
neuron.energy -= 0.001;
// Reward or punish neuron based on mode
if (rewardMode) {
neuron.energy += learningRate;
neuron.lifespan += 500; // Increase lifespan on reward
} else if (punishMode) {
neuron.energy -= learningRate;
neuron.lifespan -= 500; // Decrease lifespan on punish
}
// Strengthen connections based on activity
if (neuron.influenced) {
neuron.connections++;
neuron.connectionStrengths[neuron.connections] = (neuron.connectionStrengths[neuron.connections] || 0) + 1;
}
// Convert to permanent node if survival cycles threshold is reached
if (neuron.survivalCycles >= survivalCyclesThreshold && !neuron.isPermanent) {
neuron.isPermanent = true;
permanentNodes.push(neuron);
}
// Increment survival cycles if neuron survives
if (neuron.energy > 0) {
neuron.survivalCycles++;
}
// Remove neuron if energy is depleted or lifespan is over, unless it's a permanent node
if ((neuron.energy <= 0 || neuron.age >= neuron.lifespan) && !neuron.isPermanent) {
neurons.splice(i, 1);
}
});
// Reset reward and punish modes after each update
rewardMode = false;
punishMode = false;
}
function updateChaosAgents() {
chaosAgents.forEach((agent, index) => {
// Calculate distance from mouse
const dx = agent.x - mouse.x;
const dy = agent.y - mouse.y;
const distance = Math.hypot(dx, dy);
const maxMouseEffectDistance = 150;
// Move chaos agent away from mouse
if (distance < maxMouseEffectDistance) {
const angle = Math.atan2(dy, dx);
const force = (maxMouseEffectDistance - distance) / maxMouseEffectDistance;
agent.speedX += Math.cos(angle) * force * 1.5;
agent.speedY += Math.sin(angle) * force * 1.5;
}
// Update position
agent.x += agent.speedX;
agent.y += agent.speedY;
// Apply friction
agent.speedX *= friction;
agent.speedY *= friction;
// Reduce lifespan
agent.lifespan -= 1000 / 60; // Assuming 60 FPS
// Remove if lifespan is over
if (agent.lifespan <= 0) {
chaosAgents.splice(index, 1);
}
// Keep chaos agents within canvas bounds
if (agent.x < 0 || agent.x > canvas.width) agent.speedX *= -1;
if (agent.y < 0 || agent.y > canvas.height) agent.speedY *= -1;
});
}
function drawNeurons() {
neurons.forEach(neuron => {
// Draw trail
ctx.beginPath();
ctx.moveTo(neuron.trail[0].x, neuron.trail[0].y);
for (let i = 1; i < neuron.trail.length; i++) {
ctx.lineTo(neuron.trail[i].x, neuron.trail[i].y);
}
ctx.strokeStyle = 'rgba(255, 255, 255, 0.5)';
ctx.lineWidth = 1;
ctx.stroke();
// Draw neuron
ctx.beginPath();
const gradient = ctx.createRadialGradient(neuron.x, neuron.y, 0, neuron.x, neuron.y, neuron.radius);
gradient.addColorStop(0, '#ff6b6b');
gradient.addColorStop(0.5, '#ffdd59');
gradient.addColorStop(1, '#1e90ff');
ctx.arc(neuron.x, neuron.y, neuron.radius, 0, Math.PI * 2);
ctx.fillStyle = gradient;
ctx.shadowBlur = 10;
ctx.shadowColor = gradient;
ctx.fill();
ctx.shadowBlur = 0;
// Highlight permanent neurons
if (neuron.isPermanent) {
ctx.strokeStyle = '#00FF00';
ctx.lineWidth = 2;
ctx.stroke();
}
});
}
function connectNeurons() {
ctx.shadowBlur = 10;
ctx.shadowColor = '#fff';
neurons.forEach(neuron => neuron.connections = 0); // Reset connections count
for (let i = 0; i < neurons.length; i++) {
for (let j = i + 1; j < neurons.length; j++) {
const distance = Math.hypot(neurons[i].x - neurons[j].x, neurons[i].y - neurons[j].y);
if (distance < maxDistance && neurons[i].connections < maxConnectionsPerNeuron && neurons[j].connections < maxConnectionsPerNeuron) {
neurons[i].connections++;
neurons[j].connections++;
ctx.beginPath();
ctx.moveTo(neurons[i].x, neurons[i].y);
ctx.lineTo(neurons[j].x, neurons[j].y);
ctx.strokeStyle = `rgba(255, 255, 255, ${1 - distance / maxDistance})`;
// Ensuring line width is within a reasonable range
let lineWidth = Math.min(Math.max(neurons[i].connectionStrengths[neurons[i].connections] || 1, 1), 5);
ctx.lineWidth = lineWidth;
ctx.stroke();
// Reset line width after drawing
ctx.lineWidth = 1;
}
}
}
ctx.shadowBlur = 0;
}
function drawChaosAgents() {
chaosAgents.forEach(agent => {
ctx.beginPath();
ctx.arc(agent.x, agent.y, agent.radius, 0, Math.PI * 2);
ctx.fillStyle = agent.color;
ctx.shadowBlur = 20;
ctx.shadowColor = agent.color;
ctx.fill();
ctx.shadowBlur = 0;
});
}
function animateBackground() {
const gradient = ctx.createRadialGradient(canvas.width / 2, canvas.height / 2, 0, canvas.width / 2, canvas.height / 2, canvas.width / 2);
gradient.addColorStop(0, '#1a1a1a');
gradient.addColorStop(1, '#000');
ctx.fillStyle = gradient;
ctx.fillRect(0, 0, canvas.width, canvas.height);
}
function animate() {
// Clear and reset the transformation matrix
ctx.setTransform(1, 0, 0, 1, 0, 0);
ctx.clearRect(0, 0, canvas.width, canvas.height);
animateBackground();
updateNeurons();
updateChaosAgents();
drawNeurons();
drawChaosAgents();
connectNeurons();
requestAnimationFrame(animate);
}
function addNeuronsPeriodically() {
setInterval(() => {
if (neurons.length < maxNeurons) {
const clusters = identifyClusters();
clusters.forEach(cluster => {
const newNodesCount = Math.min(cluster.length - 1, maxNeurons - neurons.length);
if (newNodesCount > 0) {
const avgX = cluster.reduce((sum, n) => sum + n.x, 0) / cluster.length;
const avgY = cluster.reduce((sum, n) => sum + n.y, 0) / cluster.length;
createNeurons(newNodesCount, avgX, avgY);
}
});
}
}, neuronCreationRate);
}
function deleteNeuronsPeriodically() {
setInterval(() => {
for (let i = 0; i < neuronsPerDeletion; i++) {
if (neurons.length > 0) {
neurons.pop();
}
}
reductionCycleCount++;
}, neuronDeletionRate);
}
createNeurons(initialNeuronCount);
createMentalAnchors();
animate();
addNeuronsPeriodically();
deleteNeuronsPeriodically();
}
createNeuralNetAnimation();
</script>
</body>
</html>