Fix: subthreshold dynamics equation of refractory lif (#842)

* Fix: subthreshold dynamics equation of refractory lif * Fix: RefractoryLIF unit test to test the voltage dynamics
lava-nc · Jun 20, 2024 · 5b0883c · 5b0883c
1 parent 700a681
commit 5b0883c
Show file tree

Hide file tree

Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/src/lava/proc/lif/models.py b/src/lava/proc/lif/models.py
@@ -477,9 +477,8 @@ def subthr_dynamics(self, activation_in: np.ndarray):
         self.u[:] = self.u * (1 - self.du)
         self.u[:] += activation_in
         non_refractory = self.refractory_period_end < self.time_step
-        self.v[non_refractory] = (self.v[non_refractory] * (
-            (1 - self.dv) + self.u[non_refractory])
-            + self.bias_mant[non_refractory])
+        self.v[non_refractory] = self.v[non_refractory] * (1 - self.dv) + (
+            self.u[non_refractory] + self.bias_mant[non_refractory])
 
     def process_spikes(self, spike_vector: np.ndarray):
         self.refractory_period_end[spike_vector] = (self.time_step

diff --git a/tests/lava/proc/lif/test_models.py b/tests/lava/proc/lif/test_models.py
@@ -837,12 +837,13 @@ def test_float_model(self):
         refractory_period = 1
 
         # Two neurons with different biases
+        # No Input current provided to make the voltage dependent on the bias
         lif_refractory = LIFRefractory(shape=(num_neurons,),
-                                       u=np.arange(num_neurons),
+                                       u=np.zeros(num_neurons),
                                        bias_mant=np.arange(num_neurons) + 1,
                                        bias_exp=np.ones(
                                            (num_neurons,), dtype=float),
-                                       vth=4.,
+                                       vth=4,
                                        refractory_period=refractory_period)
 
         v_logger = io.sink.Read(buffer=num_steps)
@@ -856,6 +857,6 @@ def test_float_model(self):
 
         # Voltage is expected to remain at reset level for two time steps
         v_expected = np.array([[1, 2, 3, 4, 0, 0, 1, 2],
-                               [2, 0, 0, 2, 0, 0, 2, 0]], dtype=float)
+                               [2, 4, 0, 0, 2, 4, 0, 0]], dtype=float)
 
         assert_almost_equal(v, v_expected)