more corrections

gdetor · gdetor · commit f6f3165edf8b · 2017-08-16T09:23:30.000-07:00
diff --git a/article/detorakis-2017.tex b/article/detorakis-2017.tex
@@ -272,7 +272,7 @@ \section{Introduction}\label{introduction}
 thus we use the exact same number of internal currents in this work. The
 subthreshold dynamics are defined by a set of linear ordinary differential 
 equations, while an instantaneous threshold potential controls when the neuron
-firing an action potential (spike) in a dynamic way. The ability of the MNN 
+fires an action potential (spike) in a dynamic way. The ability of the MNN 
 model to generate 
 such a diverse spiking behavior is due to the complex update rules. In this 
 work the MNN model has been implemented in Python (version 3.6.1) using 
@@ -283,7 +283,7 @@ \section{Introduction}\label{introduction}
 \section{Methods}\label{methods}
 
 In order to implement the model described in \cite{mihalas:2009}, we discretized
-the dynamical system using the forward Euler's integration scheme. The time step
+the dynamical system using the forward Euler integration scheme. The time step
 is fixed to $0.1\, \Rm{ms}$ for all the simulations, and the total simulation
 time $t_f$ varies according to figure 1 of the original paper. Our
 implementation differs from the one in the original paper, since in
@@ -535,10 +535,10 @@ \section{Results}\label{results}
 Figure~\ref{fig:1}, where the black solid line indicates the membrane potential
 ($V(t)$), the red dashed line illustrates the instantaneous threshold potentials
 ($\Theta(t)$), and the gray line shows the input to the neuron ($I_e/C$). 
-The $x$-axis scale in all the panels are exactly the same as in the original
+The $x$-axis scales in all panels are exactly the same as in the original
 paper (indicating the total simulation time ($t_f$), while the $y$-axis scale 
 differs from the one in the original paper. In this work the $y$-axis scale
-is the same same for all the subplots ($[-95, -25]\, \Rm{mV}$), except from
+is the same same for all the subplots ($[-95, -25]\, \Rm{mV}$), except for
 panels G and O ($[-145, -25]\, \Rm{mV}$).
 
 Figures~\ref{fig:2} and~\ref{fig:3} depict the phase space of the phasic
diff --git a/code/neuron_model.py b/code/neuron_model.py
@@ -86,24 +86,24 @@ def mnn_model(pms=None, Iext=np.zeros((200,)), dt=1.0,
                                potential at the spike-event times.
             spikes (ndarray)  : The spike trains.
     """
-    sim_time = Iext.shape[0]                    # Simulation time
+    sim_steps = Iext.shape[0]                    # Total simulation steps
 
     if pms is None:
         pms = default_parameters()
 
     # ODEs Initial conditions
-    I1 = IC[0] * np.ones((sim_time, ))          # Internal current 1
-    I2 = IC[1] * np.ones((sim_time, ))          # Internal current 2
-    V = IC[2] * np.ones((sim_time, ))           # Membrane potential
-    Theta = IC[3] * np.ones((sim_time, ))       # Threshold potential
+    I1 = IC[0] * np.ones((sim_steps, ))          # Internal current 1
+    I2 = IC[1] * np.ones((sim_steps, ))          # Internal current 2
+    V = IC[2] * np.ones((sim_steps, ))           # Membrane potential
+    Theta = IC[3] * np.ones((sim_steps, ))       # Threshold potential
 
     # Auxiliary vectors
-    theta_ = np.zeros((sim_time, ))                 # Threshold track
-    time_ = np.zeros((sim_time, ))                  # Time vector
+    theta_ = np.zeros((sim_steps, ))                 # Threshold track
+    time_ = np.zeros((sim_steps, ))                  # Time vector
 
     spikes = []                                 # Spike-event times
     # Forward Euler integration of MNN
-    for t in range(1, sim_time):
+    for t in range(1, sim_steps):
         # ODEs
         I1[t] = I1[t-1] + dt * (-pms['k1'] * I1[t-1])
         I2[t] = I2[t-1] + dt * (-pms['k2'] * I2[t-1])
