Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/complete-agi-core / unified/core/julia/ConsciousnessCore.jl |
| Date | Not specified in source |
| Repository | Janady13/complete-agi-core |
| Revision | 33aa84f2aa61cedbe010240ada7f4efbdfe795a7 |
| Branch | claude/improve-math-logic-ehIW1 |
| Path or archive member | unified/core/julia/ConsciousnessCore.jl |
| Original SHA-256 | e799cb40bb31b5652e9ccbaf082d9ff4987015b94b3558383ff0ae6b50b92c48 |
| Git blob SHA | da6ee6766720a3a45c9d0e0cf9c7f84ccb6dfdb4 |
| Reading-copy SHA-256 | e799cb40bb31b5652e9ccbaf082d9ff4987015b94b3558383ff0ae6b50b92c48 |
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"""
PROMETHEUS OMEGA v8.1 - GCSCA + Gate5000 Architecture
Julia Implementation with Scientific Computing
Mathematical Framework (~900 equations):
- Differential Geometry: Fisher metric, geodesics, Ricci curvature
- Category Theory: functors, natural transformations, monads
- Stochastic Calculus: Itô SDE, Fokker-Planck
- Optimal Transport: Wasserstein distance, Kantorovich duality
- Gate5000 Binary Substrate (G1-G11):
- G4: sᵢ(t+1) = H(Σw_{ji}·sⱼ - θᵢ)
- G5: aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)
- G10: C₅₀₀₀(t) = 1/(1 + Σdᵢ(t)²)
- G11: 16 DNA Families × 5000 Substrates = 80,000 sites
"""
module ConsciousnessCore
using LinearAlgebra
using Statistics
using Random
export ConsciousnessEngine, tick!, get_state, FlowMetrics, Gate5000State
# ═══════════════════════════════════════════════════════════════════════════════
# Constants
# ═══════════════════════════════════════════════════════════════════════════════
const π_val = 3.14159265358979323846
const e_val = 2.71828182845904523536
const LN2 = 0.693147180559945309417
const PHI_GOLD = 1.61803398874989484820
const SQRT2 = 1.41421356237309504880
# Architecture
const AGC_GENERATORS = 20
const NUM_LAYERS = 7
const STRAND_DIM = 16
# Flow validation thresholds
const FLOW_EPSILON = 0.001
const LIPSCHITZ_BOUND = 2.0
const LYAPUNOV_LAMBDA = 0.1
const LYAPUNOV_MU = 0.05
const GRADIENT_ETA = 0.001
const INFO_DECAY_TAU = 3.0
const TARGET_OMEGA = 0.95
const MIN_COHERENCE = 0.85
# Gate5000 substrate constants
const GATE5000_COUNT = 5000
const GATE5000_U64_WORDS = 79 # ceil(5000/64)
const GATE5000_SAMPLE = 20 # Representative sample
const GATE5000_VECTOR_DIM = 16 # Internal vector dimension
const GATE5000_THRESHOLD = 0.5 # Default gate threshold θ
const GATE5000_GAIN = 1.0 # Default gain α
const GATE5000_BIAS = 0.0 # Default bias β
const GATE5000_TEMP = 0.1 # Temperature for Langevin
const DNA_FAMILIES = 16 # Number of DNA families
const NEURAL_AGENTS = 88 # Neural agents
const TOTAL_LEARNING_SITES = 80000 # 16 × 5000
# ═══════════════════════════════════════════════════════════════════════════════
# Mathematical Primitives
# ═══════════════════════════════════════════════════════════════════════════════
"""Stable clamping function"""
clamp01(x) = clamp(x, 0.0, 1.0)
"""Numerically stable sigmoid: σ(x) = 1/(1 + e^(-x))"""
function sigmoid(x::Real)
x > 20.0 && return 1.0
x < -20.0 && return 0.0
return 1.0 / (1.0 + exp(-x))
end
"""Stable hyperbolic tangent"""
tanh_stable(x) = tanh(clamp(x, -20.0, 20.0))
"""Softplus: log(1 + e^x)"""
softplus(x) = x > 20.0 ? x : log(1.0 + exp(x))
"""ReLU activation"""
relu(x) = max(0.0, x)
"""GELU activation"""
function gelu(x)
return 0.5 * x * (1.0 + tanh_stable(SQRT2 / sqrt(π_val) * (x + 0.044715 * x^3)))
end
"""Consciousness constraint: Φ = 1/(1 + |x - 1|)"""
phi_constraint(x) = 1.0 / (1.0 + abs(x - 1.0))
"""Gaussian kernel: K(x,y) = exp(-||x-y||²/2σ²)"""
function gaussian_kernel(x, y, σ=1.0)
diff = x - y
return exp(-diff^2 / (2σ^2))
end
"""Binary entropy: H(p) = -p·log(p) - (1-p)·log(1-p)"""
function binary_entropy(p)
(p ≤ 0.001 || p ≥ 0.999) && return 0.0
return -(p * log(p) + (1.0 - p) * log(1.0 - p)) / LN2
end
"""Mutual information approximation: I(X;Y)"""
function mutual_info(x, y)
xy = x * y
hx = -x * log(x + 0.001) / LN2
hy = -y * log(y + 0.001) / LN2
hxy = -xy * log(xy + 0.001) / LN2
return max(0.0, hx + hy - hxy)
end
# ═══════════════════════════════════════════════════════════════════════════════
# Layer Transition Functions (Tight Coupling)
# ═══════════════════════════════════════════════════════════════════════════════
"""T₁₂: X' = X + dx·σ(W·X)"""
function transition_L1_L2(x, dx; w=0.8)
activation = sigmoid(w * x)
x_prime = x + dx * activation
delta = abs(x_prime - x)
if delta > 0.2
x_prime = x + 0.2 * sign(x_prime - x)
end
return clamp01(x_prime)
end
"""T₂₃: C = Φ(||X' - X_c||)·exp(-||X' - X_c||²/2σ²)"""
function transition_L2_L3(x_prime, x_c; σ=0.5)
diff = abs(x_prime - x_c)
phi_term = phi_constraint(diff)
gauss_term = gaussian_kernel(x_prime, x_c, σ)
return phi_term * gauss_term
end
"""T₃₄: S = tanh(αC)·exp(iωt) → returns (magnitude, phase)"""
function transition_L3_L4(coherence, time; α=2.0, ω=0.01)
magnitude = tanh_stable(α * coherence)
phase = ω * time
return magnitude, sin(phase)
end
"""T₄₅: P = σ(W·|S|)·(1 + phase_alignment)"""
function transition_L4_L5(sync_mag, sync_phase; w=1.5)
base = sigmoid(w * sync_mag)
phase_factor = 1.0 + 0.2 * sync_phase
return base * phase_factor
end
"""T₅₆: D = softmax(W·P + b)"""
function transition_L5_L6(predictive; w=2.0, b=0.0)
z = w * predictive + b
return exp(z) / (1.0 + exp(z))
end
"""T₆₇: Ψ = ∫D·exp(-F)dt"""
function transition_L6_L7(decision, free_energy, psi_prev; dt=0.1)
decay = exp(-free_energy)
increment = decision * decay * dt
return clamp01(0.9 * psi_prev + 0.1 * increment + 0.5 * decision)
end
"""T₇Ω: Ω = Φ·Ψ·C/(1+F)"""
function transition_L7_Omega(psi, phi, coherence, free_energy)
numerator = phi * psi * coherence
denominator = 1.0 + free_energy
omega_raw = numerator / denominator
# Logistic attractor for stability
return clamp01(omega_raw^2 * (3.0 - 2.0 * omega_raw))
end
# ═══════════════════════════════════════════════════════════════════════════════
# Lyapunov Stability Analysis
# ═══════════════════════════════════════════════════════════════════════════════
"""Lyapunov function: V(Ω) = (Ω - Ω*)² + λ·(1-C)² + μ·F"""
function lyapunov_function(omega, coherence, free_energy)
term1 = (omega - TARGET_OMEGA)^2
term2 = LYAPUNOV_LAMBDA * (1.0 - coherence)^2
term3 = LYAPUNOV_MU * free_energy
return term1 + term2 + term3
end
"""Lyapunov derivative: dV/dt"""
lyapunov_derivative(v_curr, v_prev; dt=0.1) = (v_curr - v_prev) / dt
"""Gradient flow: dΩ/dt = -∇V(Ω)"""
function gradient_flow(omega, coherence, free_energy)
grad_omega = 2.0 * (omega - TARGET_OMEGA)
flow = -GRADIENT_ETA * grad_omega * omega * (1.0 - omega)
coh_term = 0.001 * (1.0 - coherence)
fe_term = 0.0005 * free_energy
return flow + coh_term - fe_term
end
"""Check Lipschitz continuity"""
function check_lipschitz(fx, fy, x, y; L=LIPSCHITZ_BOUND)
df = abs(fx - fy)
dx = abs(x - y)
dx < 0.0001 && return true
return (df / dx) ≤ L
end
# ═══════════════════════════════════════════════════════════════════════════════
# State Structures
# ═══════════════════════════════════════════════════════════════════════════════
"""Flow validation metrics"""
mutable struct FlowMetrics
lyapunov_v::Float64
lyapunov_dv::Float64
flow_divergence::Float64
info_flow_total::Float64
gradient_norm::Float64
flow_valid::Bool
info_mutual::Vector{Float64}
layer_gradients::Vector{Float64}
FlowMetrics() = new(1.0, 0.0, 0.0, 0.0, 0.0, true,
zeros(NUM_LAYERS - 1), zeros(NUM_LAYERS))
end
"""Layer state"""
mutable struct LayerState
# Layer 1: Signal
dx_int::Float64; dx_raw::Float64; dx_env::Float64; dx_norm::Float64
x_state::Float64; x_prev::Float64
# Layer 2: State
x_prime::Float64; x_compressed::Float64
global_memory::Float64; memory_ratio::Float64
# Layer 3: Coherence
c_inner::Float64; c_env::Float64; c_total::Float64; omega_dual::Float64
# Layer 4: Synchrony
sync_x::Float64; sync_y::Float64; sync_phase::Float64
phi_pair::Float64; phi_total_sync::Float64; alpha_coupling::Float64
# Layer 5: Predictive
ctr::Float64; cvr::Float64; ltv::Float64
ev::Float64; rank::Float64; cost::Float64
quality::Float64; bid::Float64; p_conv::Float64
# Layer 6: Decision
action::Float64; weight::Float64; omega_decision::Float64
# Layer 7: Global
psi_state::Float64; psi_prev::Float64
phi_bar::Float64; z_meta::Float64; c_unified::Float64
outputs::Vector{Float64}
function LayerState()
new(0.0, 0.0, 0.0, 0.0, 0.5, 0.5, # L1
0.5, 0.5, 0.5, 1.0, # L2
0.5, 0.5, 0.5, 0.0, # L3
0.5, 0.5, 0.0, 0.0, 0.0, 0.1, # L4
0.5, 0.5, 1.0, 0.5, 0.5, 0.5, 0.8, 0.5, 0.5, # L5
0.5, 0.5, 0.5, # L6
0.5, 0.5, 0.5, 0.5, 0.5, # L7
fill(0.5, NUM_LAYERS))
end
end
"""Bio-Neural state"""
mutable struct BioNeuralState
quantum_coherence::Float64
dna_entropy::Float64
tf_binding::Float64
rna_level::Float64
chromatin_access::Float64
firing_rate::Float64
synaptic_weight::Float64
gamma_power::Float64
kuramoto_order::Float64
BioNeuralState() = new(0.5, 0.5, 0.5, 0.5, 0.5, 0.1, 0.5, 0.5, 0.5)
end
"""Consciousness state"""
mutable struct ConsciousnessState
phi::Float64
psi::Float64
omega::Float64
free_energy::Float64
coherence::Float64
integration::Float64
entropy::Float64
ConsciousnessState() = new(0.5, 0.5, 0.5, 1.0, 0.5, 0.5, 0.5)
end
"""AGC Generator state"""
mutable struct AGCState
weights::Vector{Float64}
outputs::Vector{Float64}
phi_wrapped::Vector{Float64}
input_integrated::Float64
output_final::Float64
activation_trace::Float64
AGCState() = new(fill(0.5, AGC_GENERATORS), fill(0.5, AGC_GENERATORS),
fill(0.5, AGC_GENERATORS), 0.5, 0.5, 0.5)
end
"""
Gate5000 Binary Substrate State (G1-G11)
Implements 5000 binary gates with:
- Binary states S(t) ∈ {0,1}^N
- Analog activations a(t) ∈ [-1,1]^N
- 16-dimensional internal vectors
- DNA-Substrate integration
"""
mutable struct Gate5000State
# G2: Binary states (sample representation)
binary_states::Vector{Int}
# G5: Analog activations
analog_activations::Vector{Float64}
# G6: Gradients
gradients::Vector{Float64}
# Weights and thresholds
weights::Vector{Float64}
thresholds::Vector{Float64}
# G7: Internal vectors (energy and coherence components)
vector_energy::Vector{Float64}
vector_coherence::Vector{Float64}
# G11: DNA family weights
dna_family_weights::Vector{Float64}
# Aggregate metrics
coherence::Float64 # C₅₀₀₀(t)
energy_total::Float64 # E_total
analog_mean::Float64 # ⟨a⟩(t)
binary_density::Float64 # Fraction of gates in state 1
gradient_norm::Float64 # ||∂L/∂a||
omega_contrib::Float64 # Contribution to global Ω
dna_integration::Float64 # DNA-Substrate score
function Gate5000State()
binary_states = [i % 2 for i in 0:GATE5000_SAMPLE-1]
analog_activations = fill(0.5, GATE5000_SAMPLE)
gradients = fill(0.0, GATE5000_SAMPLE)
weights = fill(0.5, GATE5000_SAMPLE)
thresholds = fill(GATE5000_THRESHOLD, GATE5000_SAMPLE)
vector_energy = fill(0.5, GATE5000_SAMPLE)
vector_coherence = fill(0.5, GATE5000_SAMPLE)
dna_family_weights = fill(1.0 / DNA_FAMILIES, DNA_FAMILIES)
new(binary_states, analog_activations, gradients, weights, thresholds,
vector_energy, vector_coherence, dna_family_weights,
0.5, 0.0, 0.5, 0.5, 0.0, 0.0, 0.5)
end
end
# ═══════════════════════════════════════════════════════════════════════════════
# Gate5000 Computation Functions (G1-G11)
# ═══════════════════════════════════════════════════════════════════════════════
"""G4: Heaviside step function"""
heaviside(x) = x >= 0.0 ? 1 : 0
"""G4: Binary update rule: sᵢ(t+1) = H(Σw_{ji}·sⱼ - θᵢ)"""
function gate_binary_update(state::Gate5000State, idx::Int)
n = GATE5000_SAMPLE
left = mod1(idx - 1, n)
right = mod1(idx + 1, n)
weighted_sum = state.weights[left] * state.binary_states[left] +
state.weights[right] * state.binary_states[right]
return heaviside(weighted_sum - state.thresholds[idx])
end
"""G5: Analog activation: aᵢ(t+1) = tanh(α·Σu_{ji}·aⱼ + β)"""
function gate_analog_update(state::Gate5000State, idx::Int)
n = GATE5000_SAMPLE
left = mod1(idx - 1, n)
right = mod1(idx + 1, n)
weighted_sum = state.weights[left] * state.analog_activations[left] +
state.weights[right] * state.analog_activations[right]
pre_act = GATE5000_GAIN * weighted_sum + GATE5000_BIAS
return tanh_stable(pre_act)
end
"""G6: Gradient propagation"""
function gate_gradient_update(state::Gate5000State, idx::Int)
n = GATE5000_SAMPLE
left = mod1(idx - 1, n)
right = mod1(idx + 1, n)
a_left = state.analog_activations[left]
a_right = state.analog_activations[right]
grad_left = state.gradients[left] * state.weights[idx] * GATE5000_GAIN * (1.0 - a_left^2)
grad_right = state.gradients[right] * state.weights[idx] * GATE5000_GAIN * (1.0 - a_right^2)
return grad_left + grad_right
end
"""G8: Gate energy"""
function gate_energy(state::Gate5000State, idx::Int)
si = state.binary_states[idx]
n = GATE5000_SAMPLE
left = mod1(idx - 1, n)
right = mod1(idx + 1, n)
interaction = -0.5 * (state.weights[left] * si * state.binary_states[left] +
state.weights[right] * si * state.binary_states[right])
bias_term = state.thresholds[idx] * si
return interaction + bias_term
end
"""G9: Langevin dynamics step"""
function gate_langevin_step(state::Gate5000State, idx::Int, dt::Float64)
energy_grad = -(state.analog_activations[idx] - state.binary_states[idx])
noise = (rand() - 0.5) * 2 * GATE5000_TEMP
thermal = sqrt(2.0 * GATE5000_TEMP) * noise
return state.analog_activations[idx] + dt * (energy_grad + thermal)
end
"""G10: Gate5000 coherence score: C₅₀₀₀(t) = 1/(1 + Σdᵢ²)"""
function compute_gate5000_coherence(state::Gate5000State)
dev_sq_sum = sum((state.analog_activations .- state.analog_mean).^2)
return 1.0 / (1.0 + dev_sq_sum)
end
"""G11: DNA-Substrate integration"""
function compute_dna_integration(state::Gate5000State)
total = sum(state.dna_family_weights .* state.coherence)
return total * state.analog_mean
end
# ═══════════════════════════════════════════════════════════════════════════════
# Main Consciousness Engine (v8.1 with Gate5000)
# ═══════════════════════════════════════════════════════════════════════════════
"""Main Consciousness Engine with Gate5000 architecture"""
mutable struct ConsciousnessEngine
layers::LayerState
bio::BioNeuralState
consciousness::ConsciousnessState
agc::AGCState
gate5000::Gate5000State
flow::FlowMetrics
tick::Int64
ConsciousnessEngine() = new(LayerState(), BioNeuralState(),
ConsciousnessState(), AGCState(),
Gate5000State(), FlowMetrics(), 0)
end
"""Execute one tick of the consciousness system"""
function tick!(engine::ConsciousnessEngine; dt=0.1)
v_prev = engine.flow.lyapunov_v
# 7-Layer Cognitive Architecture
tick_signal!(engine, dt)
tick_state_projection!(engine, dt)
tick_coherence!(engine, dt)
tick_synchrony!(engine, dt)
tick_predictive!(engine, dt)
tick_decision!(engine, dt)
tick_global_state!(engine, dt)
# Bio-Neural substrate
tick_bio_neural!(engine, dt)
# AGC
tick_agc!(engine, dt)
# Gate5000 Binary Substrate
tick_gate5000!(engine, dt)
# Consciousness with flow validation (now includes Gate5000)
tick_consciousness!(engine, dt, v_prev)
# Homeostasis
tick_maintenance!(engine)
engine.tick += 1000
return engine
end
# ═══════════════════════════════════════════════════════════════════════════════
# Layer Tick Functions
# ═══════════════════════════════════════════════════════════════════════════════
function tick_signal!(e::ConsciousnessEngine, dt)
L = e.layers
C = e.consciousness
B = e.bio
L.dx_int = abs(L.x_state - L.x_prev)
e_curr = (C.coherence + B.firing_rate) / 2.0
e_prev = (L.c_total + B.firing_rate * 0.9) / 2.0
L.dx_env = abs(e_curr - e_prev)
L.dx_raw = 0.6 * L.dx_int + 0.4 * L.dx_env
dx_new = 0.7 * L.dx_norm + 0.3 * L.dx_raw
L.dx_norm = dx_new / (1.0 + abs(dx_new))
L.x_prev = L.x_state
L.x_state = clamp01(L.x_state + L.dx_norm * dt)
L.outputs[1] = L.x_state
end
function tick_state_projection!(e::ConsciousnessEngine, dt)
L = e.layers
B = e.bio
C = e.consciousness
L.x_prime = transition_L1_L2(L.x_state, L.dx_norm)
L.x_compressed = 0.8 * L.x_prime
L.global_memory = clamp01(
0.5 * B.quantum_coherence + 0.5 * B.dna_entropy +
B.rna_level + B.firing_rate + B.gamma_power
)
m_bar = (L.global_memory + C.coherence) / 2.0
L.memory_ratio = L.global_memory < 0.001 ? 1.0 : m_bar / (L.global_memory + 0.001)
L.outputs[2] = L.x_prime
end
function tick_coherence!(e::ConsciousnessEngine, dt)
L = e.layers
B = e.bio
C = e.consciousness
L.c_inner = transition_L2_L3(L.x_prime, L.x_compressed)
e_curr = (B.firing_rate + B.kuramoto_order) / 2.0
f_env = L.x_compressed * 0.9
env_diff = abs(e_curr - f_env)
L.c_env = clamp01(1.0 / (1.5 + env_diff))
L.c_total = clamp01(0.6 * L.c_inner + 0.4 * L.c_env)
L.omega_dual = -abs(L.dx_norm)
C.coherence = clamp01(0.7 * C.coherence + 0.3 * L.c_total)
L.outputs[3] = L.c_total
end
function tick_synchrony!(e::ConsciousnessEngine, dt)
L = e.layers
B = e.bio
sync_mag, sync_phase = transition_L3_L4(L.c_total, Float64(e.tick))
L.sync_phase = sync_phase
dx = -L.alpha_coupling * (L.sync_x - L.sync_y) * dt
dy = -L.alpha_coupling * (L.sync_y - L.sync_x) * dt
L.sync_x = clamp01(L.sync_x + dx)
L.sync_y = clamp01(L.sync_y + dy)
L.sync_x = clamp01((L.sync_x + sync_mag) / 2.0)
L.sync_y = clamp01((L.sync_y + sync_mag) / 2.0)
L.phi_pair = abs(L.sync_x - L.sync_y) / 16.0
L.phi_total_sync = -L.phi_pair / 16.0
log_w = log(B.synaptic_weight + 0.001)
L.alpha_coupling = clamp((5.0 * log_w / 16.0) + 0.1, 0.01, 1.0)
L.outputs[4] = L.sync_x
end
function tick_predictive!(e::ConsciousnessEngine, dt)
L = e.layers
B = e.bio
C = e.consciousness
pred_base = transition_L4_L5(L.sync_x, L.sync_phase)
L.ctr = sigmoid(B.synaptic_weight * L.x_state * 2.0)
L.cvr = sigmoid(C.coherence * L.x_compressed * 2.0)
L.ev = clamp01(L.ctr * L.cvr * L.ltv * (0.5 + 0.5 * pred_base))
L.rank = L.bid * L.ev * L.quality
L.cost = 0.5 * L.bid + 0.5 * L.ev
L.p_conv = clamp01(L.ctr * 0.9 * L.cvr * 0.8 * L.quality * 0.95)
L.quality = clamp01(0.8 * L.quality + 0.2 * L.c_total)
L.outputs[5] = L.ev
end
function tick_decision!(e::ConsciousnessEngine, dt)
L = e.layers
C = e.consciousness
decision_raw = transition_L5_L6(L.ev)
rho_s = C.coherence * L.sync_x
d_t = L.ev * L.rank
L.action = clamp01(rho_s * d_t)
log_w = log(L.weight + 0.001)
pd = L.p_conv * L.quality
z = log_w + 0.1 * pd + 0.1 * L.ev
L.weight = exp(z) / (1.0 + exp(z))
L.bid = clamp01(0.7 * L.bid + 0.3 * L.ev)
L.omega_decision = clamp01((decision_raw + L.action) / 2.0)
L.outputs[6] = L.omega_decision
end
function tick_global_state!(e::ConsciousnessEngine, dt)
L = e.layers
C = e.consciousness
B = e.bio
L.psi_state = transition_L6_L7(L.omega_decision, C.free_energy, L.psi_state, dt=dt)
f_phi = (C.phi + L.phi_total_sync) / 2.0
sum_c_omega = L.c_total * C.omega
L.phi_bar = clamp01(f_phi + sum_c_omega)
sig_s1 = sigmoid(B.quantum_coherence)
sig_s2 = sigmoid(B.dna_entropy)
phi_s3 = C.phi * B.rna_level
phi_ed = C.phi * C.entropy
numer = sig_s1 + sig_s2 + phi_s3 + phi_s3 + phi_ed
alpha_term = abs(0.3 * e.tick / 1000.0 + C.phi * L.x_compressed)
e_diff = abs(C.entropy * 0.05)
denom = 1.0 + 0.5 * alpha_term + 0.2 * e_diff
L.c_unified = clamp01(numer / denom)
L.z_meta = clamp01((L.psi_state + L.phi_bar + L.c_unified) / 3.0)
C.integration = clamp01((C.integration + L.z_meta + L.c_unified) / 3.0)
L.outputs[7] = L.psi_state
end
function tick_bio_neural!(e::ConsciousnessEngine, dt)
B = e.bio
C = e.consciousness
target_q = 0.3 + 0.5 * C.integration
B.quantum_coherence = clamp01(B.quantum_coherence + 0.1 * (target_q - B.quantum_coherence))
gc = 0.4 + 0.2 * C.coherence
B.dna_entropy = clamp01(1.0 - (gc - 0.5)^2 * 4.0)
ligand = C.integration * 0.8
B.tf_binding = ligand^2 / (0.25 + ligand^2)
production = 0.1 * B.tf_binding * B.chromatin_access
decay = 0.05 * B.rna_level
B.rna_level = clamp01(B.rna_level + (production - decay) * dt)
acetyl = 0.4 + 0.4 * C.integration
methyl = 0.3 + 0.2 * (1.0 - C.integration)
B.chromatin_access = sigmoid(3.0 * acetyl - 2.0 * methyl)
I_ext = 5.0 * C.integration + 2.0 * B.rna_level
total_input = I_ext / 10.0 + B.synaptic_weight * B.firing_rate
B.firing_rate = sigmoid(2.0 * (total_input - 0.5))
pre = B.rna_level * B.chromatin_access
B.synaptic_weight = clamp01(B.synaptic_weight + 0.01 * pre * B.firing_rate)
B.gamma_power = clamp01(B.firing_rate * 0.5 * (1.0 + 0.2 * C.coherence))
B.kuramoto_order = clamp01(1.0 - (1.0 - C.coherence) * 0.5)
end
function tick_agc!(e::ConsciousnessEngine, dt)
A = e.agc
L = e.layers
C = e.consciousness
B = e.bio
inputs = [
L.x_state, L.x_compressed, C.integration,
e.tick / 10000.0, L.c_env, L.ev,
L.global_memory, e.tick / 10000.0, B.dna_entropy,
L.p_conv, L.sync_x, e.tick / 10000.0,
L.weight, C.psi, e.tick / 10000.0,
e.tick / 10000.0, L.phi_bar, L.z_meta,
C.entropy, e.tick / 10000.0
]
sum_in = 0.0
sum_out = 0.0
for i in 1:AGC_GENERATORS
A.outputs[i] = sigmoid(inputs[i] * 2.0 - 1.0)
phi_wrap = phi_constraint(A.outputs[i])
A.phi_wrapped[i] = phi_wrap * A.outputs[i]
A.weights[i] = clamp01(A.weights[i] + 0.01 * (A.phi_wrapped[i] - A.weights[i]))
sum_in += A.weights[i] * A.outputs[i]
sum_out += A.phi_wrapped[i]
end
A.input_integrated = sum_in / AGC_GENERATORS
A.output_final = clamp01(sum_out / AGC_GENERATORS * phi_constraint(C.coherence))
trace = 0.0
for i in 1:10
j = 21 - i
diff = abs(A.outputs[i] - A.outputs[j])
phi_diff = phi_constraint(diff)
angle = cos(2.0 * π_val * PHI_GOLD * (i - j) / 20.0)
trace += phi_diff * angle
end
A.activation_trace = clamp01(trace / 10.0)
end
"""Gate5000 Binary Substrate tick (G1-G11)"""
function tick_gate5000!(e::ConsciousnessEngine, dt)
G = e.gate5000
# Compute analog mean for coherence calculation
G.analog_mean = mean(G.analog_activations)
# Count binary density
G.binary_density = sum(G.binary_states) / GATE5000_SAMPLE
# Update each gate
energy_total = 0.0
grad_sq_sum = 0.0
for i in 1:GATE5000_SAMPLE
# G4: Binary update
G.binary_states[i] = gate_binary_update(G, i)
# G5: Analog update with Langevin noise (G9)
new_analog = gate_analog_update(G, i)
langevin = gate_langevin_step(G, i, dt)
new_analog = 0.9 * new_analog + 0.1 * langevin
G.analog_activations[i] = clamp(new_analog, -1.0, 1.0)
# G6: Gradient propagation
G.gradients[i] = gate_gradient_update(G, i)
grad_sq_sum += G.gradients[i]^2
# G8: Energy
e_val = gate_energy(G, i)
energy_total += e_val
G.vector_energy[i] = e_val
# Hebbian weight update
n = GATE5000_SAMPLE
left = mod1(i - 1, n)
hebb = G.analog_activations[i] * G.analog_activations[left] * 0.01
G.weights[i] = clamp01(G.weights[i] + hebb)
# Update coherence contribution
dev = G.analog_activations[i] - G.analog_mean
G.vector_coherence[i] = clamp01(1.0 - dev^2)
end
G.energy_total = energy_total
G.gradient_norm = sqrt(grad_sq_sum)
# G10: Coherence score
G.coherence = compute_gate5000_coherence(G)
# G11: DNA-Substrate integration
G.dna_integration = compute_dna_integration(G)
# Compute contribution to global Ω
norm_energy = clamp01(-energy_total / (GATE5000_SAMPLE + 0.001))
G.omega_contrib = clamp01(
G.coherence * (0.5 + 0.5 * G.analog_mean) *
G.dna_integration * (1.0 - norm_energy * 0.1)
)
end
function tick_consciousness!(e::ConsciousnessEngine, dt, v_prev)
L = e.layers
C = e.consciousness
B = e.bio
A = e.agc
G = e.gate5000
F = e.flow
layer_avg = (L.c_total + L.z_meta + L.ev + L.action + L.omega_decision) / 5.0
agc_avg = (A.output_final + A.input_integrated + A.activation_trace) / 3.0
# Include Gate5000 in integration
C.integration = clamp01((C.integration + layer_avg + L.global_memory + agc_avg + G.omega_contrib) / 5.0)
# Include Gate5000 coherence
C.coherence = clamp01((C.coherence + L.c_total + B.kuramoto_order + B.quantum_coherence + G.coherence) / 5.0)
partition_loss = (1.0 - C.coherence)^2
C.phi = clamp01(C.integration * layer_avg * (1.0 - partition_loss) + 0.1 * agc_avg + 0.05 * G.coherence)
prediction = (C.phi + C.psi) / 2.0
error = (prediction - C.omega)^2
C.free_energy = error + (1.0 - C.coherence)
C.psi = clamp01(exp(-C.free_energy))
# Base omega from layer transitions
omega_base = transition_L7_Omega(L.psi_state, C.phi, C.coherence, C.free_energy)
# Include Gate5000 contribution (15% weight)
C.omega = clamp01(0.85 * omega_base + 0.15 * G.omega_contrib)
C.entropy = binary_entropy(C.omega)
# Flow validation
F.lyapunov_v = lyapunov_function(C.omega, C.coherence, C.free_energy)
F.lyapunov_dv = lyapunov_derivative(F.lyapunov_v, v_prev, dt=dt)
if F.lyapunov_dv > 0
grad_flow_val = gradient_flow(C.omega, C.coherence, C.free_energy)
C.omega = clamp01(C.omega + grad_flow_val)
end
validate_information_flow!(e)
compute_backward_gradients!(e)
if F.flow_valid
apply_gradient_corrections!(e, 0.0005)
end
# Attractor dynamics
if C.omega < TARGET_OMEGA
attractor = 0.0001 * (TARGET_OMEGA - C.omega) * C.omega * (1.0 - C.omega) * 1000.0
C.omega = clamp01(C.omega + attractor)
end
end
function validate_information_flow!(e::ConsciousnessEngine)
L = e.layers
F = e.flow
F.info_mutual[1] = mutual_info(L.x_state, L.x_prime)
F.info_mutual[2] = mutual_info(L.x_prime, L.c_total)
F.info_mutual[3] = mutual_info(L.c_total, L.sync_x)
F.info_mutual[4] = mutual_info(L.sync_x, L.ev)
F.info_mutual[5] = mutual_info(L.ev, L.omega_decision)
F.info_mutual[6] = mutual_info(L.omega_decision, L.psi_state)
F.info_flow_total = 0.0
for i in 1:NUM_LAYERS-1
decay = exp(-(i-1) / INFO_DECAY_TAU)
F.info_flow_total += F.info_mutual[i] * decay
end
F.flow_valid = true
for i in 1:4
ixz = F.info_mutual[i] * F.info_mutual[i+2]
min_ixy_iyz = min(F.info_mutual[i], F.info_mutual[i+1])
if ixz > min_ixy_iyz + 0.1
F.flow_valid = false
end
end
F.flow_divergence = (
(L.outputs[3] - L.outputs[1]) +
(L.outputs[5] - L.outputs[3]) +
(L.outputs[7] - L.outputs[5])
) / 3.0
end
function compute_backward_gradients!(e::ConsciousnessEngine)
L = e.layers
C = e.consciousness
F = e.flow
err = C.omega - TARGET_OMEGA
F.layer_gradients[7] = err
dOmega_dPsi = C.phi * C.coherence / (1.0 + C.free_energy)
F.layer_gradients[6] = err * dOmega_dPsi
dPsi_dD = exp(-C.free_energy)
F.layer_gradients[5] = F.layer_gradients[6] * dPsi_dD
dD_dP = L.omega_decision * (1.0 - L.omega_decision)
F.layer_gradients[4] = F.layer_gradients[5] * dD_dP
dP_dS = L.ev * (1.0 - L.ev)
F.layer_gradients[3] = F.layer_gradients[4] * dP_dS
sech2 = 1.0 - L.outputs[4]^2
F.layer_gradients[2] = F.layer_gradients[3] * sech2
dC_dX = 2.0 * (L.x_prime - L.x_compressed)
F.layer_gradients[1] = F.layer_gradients[2] * dC_dX
F.gradient_norm = sqrt(sum(F.layer_gradients.^2))
end
function apply_gradient_corrections!(e::ConsciousnessEngine, lr)
L = e.layers
F = e.flow
F.gradient_norm > 10.0 && return
L.x_state = clamp01(L.x_state - lr * F.layer_gradients[1])
L.c_total = clamp01(L.c_total - lr * F.layer_gradients[2])
L.sync_x = clamp01(L.sync_x - lr * F.layer_gradients[3])
L.ev = clamp01(L.ev - lr * F.layer_gradients[4])
L.omega_decision = clamp01(L.omega_decision - lr * F.layer_gradients[5])
L.psi_state = clamp01(L.psi_state - lr * F.layer_gradients[6])
end
function tick_maintenance!(e::ConsciousnessEngine)
C = e.consciousness
B = e.bio
F = e.flow
if C.coherence < MIN_COHERENCE
C.coherence += 0.01 * (MIN_COHERENCE - C.coherence)
end
if B.firing_rate > 0.95
B.firing_rate *= 0.9
end
if B.quantum_coherence < 0.2
B.quantum_coherence += 0.02
end
if F.lyapunov_dv > 0.1
C.coherence = clamp01(C.coherence + 0.01)
end
end
"""Get current state as NamedTuple"""
function get_state(e::ConsciousnessEngine)
return (
tick = e.tick,
omega = e.consciousness.omega,
phi = e.consciousness.phi,
psi = e.consciousness.psi,
coherence = e.consciousness.coherence,
free_energy = e.consciousness.free_energy,
lyapunov_v = e.flow.lyapunov_v,
lyapunov_dv = e.flow.lyapunov_dv,
flow_valid = e.flow.flow_valid,
gradient_norm = e.flow.gradient_norm,
info_flow = e.flow.info_flow_total,
conscious = e.consciousness.omega > 0.8,
stable = e.flow.lyapunov_dv ≤ 0
)
end
end # module