Added the recurrence strength parameter r in BernoulliGLM to document…

…ation and examples

docs/introduction/introduction.rst

@@ -88,14 +88,15 @@ in the :class:`BernoulliGLM` class, using the same parameters as in the original
 .. code-block:: python
 
     model = BernoulliGLM(
-        alpha= 0.2,               # Decay rate of the coupling strength between neurons (1/ms)
-        beta= 0.5,                # Decay rate of the self-inhibition during the relative refractory period (1/ms)
+        alpha=0.2,               # Decay rate of the coupling strength between neurons (1/ms)
+        beta=0.5,                # Decay rate of the self-inhibition during the relative refractory period (1/ms)
         abs_ref_scale=3,          # Absolute refractory period in time steps
         rel_ref_scale=7,          # Relative refractory period in time steps
         abs_ref_strength=-100,    # Strength of the self-inhibition during the absolute refractory period
         rel_ref_strength=-30,     # Initial strength of the self-inhibition during the relative refractory period
         coupling_window=5,        # Length of coupling window in time steps
         theta=5,                  # Threshold for firing
+        r=1,                    # Parameter controlling the recurrence strength
         dt=1,                     # Length of time step (ms)
     )
 

docs/tutorials/stimuli.rst

@@ -38,6 +38,7 @@ After we've defined the model and the stimulus, we can simulate the network and
         rel_ref_strength=-30,    
         alpha=0.2,
         beta=0.5,
+        r=1
     )
 
     # Define stimulus and add it to the model
@@ -92,6 +93,7 @@ Before we add the stimulus to the model, we'll run a simulation without it to se
         rel_ref_strength=-30,    
         alpha=0.2,
         beta=0.5,
+        r=1
     )
 
     spikes = model.simulate(network, n_steps=n_steps)
@@ -156,6 +158,7 @@ that is close to the frequency of the stimulus.
         rel_ref_strength=-30,    
         alpha=0.2,
         beta=0.5,
+        r=1
     )
 
     stimulus = SinStimulus(

examples/large_scale_example.ipynb

@@ -550,6 +550,7 @@
     "    rel_ref_scale=5,\n",
     "    rel_ref_strength=-30,\n",
     "    beta=0.1,\n",
+    "    r=1\n",
     ")"
    ]
   },
@@ -842,7 +843,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4
  },

examples/working_with_stimulus.ipynb

@@ -84,6 +84,7 @@
     "    rel_ref_strength=-30,    \n",
     "    alpha=0.2,\n",
     "    beta=0.5,\n",
+    "    r=1,\n",
     ")\n",
     "model.add_stimulus(stim)\n",
     "\n",
@@ -233,6 +234,7 @@
     "    rel_ref_strength=-30,    \n",
     "    alpha=0.2,\n",
     "    beta=0.5,\n",
+    "    r=1,\n",
     ")"
    ]
   },
@@ -428,6 +430,7 @@
     "    rel_ref_strength=-30,    \n",
     "    alpha=0.2,\n",
     "    beta=0.5,\n",
+    "    r=1,\n",
     ")\n",
     "\n",
     "\n",