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import __main__
BaseNode = __main__.BaseNode
QtGui = __main__.QtGui
import numpy as np
import cv2
class FitzHughNagumoNode(BaseNode):
"""
Simulates a FitzHugh-Nagumo neuron model.
"""
NODE_CATEGORY = "AI / Physics"
NODE_COLOR = QtGui.QColor(100, 180, 180)
def __init__(self, a=0.7, b=0.8, tau=12.5):
super().__init__()
self.node_title = "FHN Neuron"
self.inputs = {'pain_stimulus': 'signal'}
self.outputs = {
'pain_out': 'signal',
'stability_metric': 'signal'
}
self.a = float(a)
self.b = float(b)
self.tau = float(tau)
self.v = 0.0 # Membrane potential ("pain")
self.w = 0.0 # Recovery variable ("stability")
self.dt = 0.1 # Simulation time step
def step(self):
# Get input current
I = self.get_blended_input('pain_stimulus', 'sum') or 0.0
# Model equations (Euler integration)
dv = self.v - (self.v**3 / 3) - self.w + I
dw = (self.v + self.a - self.b * self.w) / self.tau
self.v += dv * self.dt
self.w += dw * self.dt
# Clamp values to prevent explosion
self.v = np.clip(self.v, -5, 5)
self.w = np.clip(self.w, -5, 5)
def get_output(self, port_name):
if port_name == 'pain_out':
return self.v
if port_name == 'stability_metric':
return self.w
return None
def get_display_image(self):
w, h = 256, 128
img = np.zeros((h, w, 3), dtype=np.uint8)
# Map v and w to screen
v_y = int(h/2 - (self.v / 3.0) * (h/2))
w_y = int(h/2 - (self.w / 3.0) * (h/2))
cv2.circle(img, (w//2, v_y), 8, (0, 255, 255), -1) # 'v' (pain)
cv2.circle(img, (w//2, w_y), 4, (255, 0, 0), -1) # 'w' (stability)
cv2.line(img, (0, h//2), (w, h//2), (50,50,50), 1)
cv2.putText(img, f"Pain (v): {self.v:.2f}", (5, 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)
cv2.putText(img, f"Stability (w): {self.w:.2f}", (5, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1)
return QtGui.QImage(img.data, w, h, w*3, QtGui.QImage.Format.Format_RGB888)
def get_config_options(self):
return [
("a", "a", self.a, None),
("b", "b", self.b, None),
("tau", "tau", self.tau, None)
] |