File size: 2,984 Bytes
59936ca | 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 | import torch
import numpy as np
import matplotlib.pyplot as plt
print("\n--- EXPERIMENT 3: CHANNEL ISOLATION (Multitasking) ---")
print("Validating Biology/Mamba: 'Lateral Inhibition prevents Leaking'")
def run_parallel_channels():
steps = 100
# Channel A: Sine Wave
# Channel B: Cosine Wave
t = np.linspace(0, 4*np.pi, steps)
input_a = np.sin(t)
input_b = np.cos(t) # Orthogonal approx
# System with Cross-Talk (Leakage)
leakage_factor = 0.2
inhibition_factor = 0.5 # Lateral Inhibition
out_a = []
out_b = []
# State
curr_a = 0
curr_b = 0
output_path = "/home/daroch/SOLITONES/EXPERIMENTOS/"
log_file = "exp03_parallel_channels.log"
with open(output_path + log_file, "w") as f:
f.write("--- EXPERIMENT 3: CHANNEL ISOLATION (Multitasking) ---\n")
# Visualization
import matplotlib.animation as animation
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 6))
ims = []
for i in range(steps):
# 1. Inputs arrive
ia = input_a[i]
ib = input_b[i]
# 2. Physics with Leakage
# Without inhibition, A leaks to B
next_a = ia + leakage_factor * curr_b
next_b = ib + leakage_factor * curr_a
# 3. Lateral Inhibition (The Solitonic Fix)
# "If I am strong, I suppress my neighbor"
inhib_a = inhibition_factor * np.abs(curr_a)
inhib_b = inhibition_factor * np.abs(curr_b)
# Apply inhibition to the cross-talk
final_a = next_a - (np.abs(next_b) * leakage_factor * inhibition_factor)
final_b = next_b - (np.abs(next_a) * leakage_factor * inhibition_factor)
curr_a = final_a
curr_b = final_b
out_a.append(curr_a)
out_b.append(curr_b)
# Visualization
line_a, = ax1.plot(range(len(out_a)), out_a, color='red')
line_b, = ax2.plot(range(len(out_b)), out_b, color='blue')
pt_a, = ax1.plot(len(out_a)-1, curr_a, 'ro')
pt_b, = ax2.plot(len(out_b)-1, curr_b, 'bo')
if i % 2 == 0: # Downsample
ims.append([line_a, line_b, pt_a, pt_b])
output_path = "/home/daroch/SOLITONES/EXPERIMENTOS/"
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True)
ani.save(output_path + "exp03_parallel_channels.gif", writer='pillow')
print(f"🎥 Saved {output_path}exp03_parallel_channels.gif")
# Analysis: Correlation
out_a = np.array(out_a)
out_b = np.array(out_b)
corr = np.corrcoef(out_a, out_b)[0,1]
msg = f"Final Correlation (A vs B): {corr:.4f}\n"
if abs(corr) < 0.3:
msg += "✅ SUCCESS: Signals stayed separable."
else:
msg += f"⚠️ WARNING: High correlation ({corr}). Leakage detected."
print(msg)
with open(output_path + log_file, "a") as f: f.write(msg + "\n")
if __name__ == "__main__":
run_parallel_channels()
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