diff options
Diffstat (limited to 'neurobench_testing/kaevin_memristor.py')
| -rw-r--r-- | neurobench_testing/kaevin_memristor.py | 134 |
1 files changed, 0 insertions, 134 deletions
diff --git a/neurobench_testing/kaevin_memristor.py b/neurobench_testing/kaevin_memristor.py deleted file mode 100644 index b4a4364..0000000 --- a/neurobench_testing/kaevin_memristor.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np -from scipy.integrate import solve_ivp - -# TEAM (Threshold Adaptive Memristor) model as a reusable class -class TEAMMemristor: - - def __init__( - self, - k_off=1, # switching rate for off-state - k_on=-1, # switching rate for on-state - alpha_off=5, # exponent controlling nonlinearity when switching off - alpha_on=5, # exponent controlling nonlinearity when switching on - i_off=0.5e-3, # threshold current to trigger off state switching - i_on=-0.5e-3, # threshold current to trigger on state switching - g_on=1/1e3, # maximum conductance (1 kohm = 1 ms) - g_off=1/10e3, # minimum conductance (10 kohm = 0.1 ms) - w_init=0.5, # initial state variable (0=off, 1=on) - p=2 # window function exponent - ): - self.k_off = k_off - self.k_on = k_on - self.alpha_off = alpha_off - self.alpha_on = alpha_on - self.i_off = i_off - self.i_on = i_on - self.G_on = G_on - self.G_off = G_off - self.w_init = w_init - self.p = p - - def set_state(self, w): - # Update initial condition for next simulation - self.w_init = np.clip(w, 0, 1) - - def window(self, w, i): - # Nonlinear window function: reduces switching rate near the boundaries - w = np.clip(w, 0.0, 1.0) - if i >= 0: - return 1 - w**(2*self.p) # switching off: slower near w=1 - else: - return 1 - (1-w)**(2*self.p) # switching on: slower near w=0 - - def conductance(self, w): - # Linear interpolation between off and on conductance based on state w - w = np.clip(w, 0.0, 1.0) - return self.G_off + w*(self.G_on - self.G_off) - - def dw_dt(self, w, i): - # TEAM state dynamics: dw/dt depends on current magnitude and direction - w = np.clip(w, 0.0, 1.0) - - if i >= self.i_off: # positive current above threshold = switch off - dw = ( - self.k_off - * ((i/self.i_off)-1)**self.alpha_off - * self.window(w, i) - ) - elif i <= self.i_on: # negative current below threshold = switch on - dw = ( - self.k_on - * (((-i)/abs(self.i_on))-1)**self.alpha_on - * self.window(w, i) - ) - else: # between thresholds = no switching - dw = 0.0 - - # Enforce physical bounds: prevent state from leaving [0,1] - if w <= 0 and dw < 0: - dw = 0 - if w >= 1 and dw > 0: - dw = 0 - - return dw - - def simulate(self, - freq=1, # excitation frequency (Hz) - V_amp=1.5, # sinusoid amplitude (V) - cycles=3): # number of periods to simulate - # Solve the memristor ODE for given frequency and voltage amplitude - - def voltage(t): # sinusoidal excitation signal - return V_amp*np.sin(2*np.pi*freq*t) - - def ode(t, y): # dy/dt: current through memristor - w = y[0] - v = voltage(t) - G = self.conductance(w) - i = G*v # Ohm's law: i = G*v - return [self.dw_dt(w, i)] - - T = 1/freq # period - t_end = cycles*T - t_eval = np.linspace(0, t_end, 10000) # dense time grid for smooth curves - - # Solve with RK45, small max step - sol = solve_ivp( - ode, - [0, t_end], - [self.w_init], - t_eval=t_eval, - method='RK45', - max_step=T/1000, # max step keeps resolution within one period - rtol=1e-8, - atol=1e-10 - ) - - raw_w = sol.y[0] - - # Check if numerical solver violated physical bounds - eps = 1e-6 - if np.any(raw_w < -eps) or np.any(raw_w > 1+eps): - print( - "WARNING: solver left bounds " - f"min={raw_w.min():.12f}, " - f"max={raw_w.max():.12f}" - ) - - w = np.clip(raw_w, 0, 1) # enforce bounds just in case - t = sol.t - v = voltage(t) - G = self.conductance(w) - i = G*v # current throughout simulation - - return t, w, v, i - - def resistance(self, w): - # Compute resistance as reciprocal of conductance - return 1/self.conductance(w) - - def reset(self): - # Reset state to default initial condition - self.w_init = 0.5 - - |