SCIF neuron model: Minor fixes

src/lava/proc/scif/models.py

@@ -37,19 +37,18 @@ def __init__(self, proc_params):
         super(AbstractPyModelScifFixed, self).__init__(proc_params)
         self.a_in_data = np.zeros(proc_params['shape'])
 
-    def _prng(self, shape_like):
+    def _prng(self, intg_idx_):
         """Pseudo-random number generator
         """
 
         # ToDo: Choosing a 16-bit signed random integer. For bit-accuracy,
         #   need to replace it with Loihi-conformant LFSR function
-        prand = np.zeros_like(shape_like)
+        prand = np.zeros(shape=(len(intg_idx_),))
         if prand.size > 0:
             rand_nums = \
                 np.random.randint(-2 ** 15, 2 ** 15 - 1, size=prand.size)
             # Assign random numbers only to neurons, for which noise is enabled
-            prand = rand_nums * self.noise_ampl
-
+            prand = rand_nums * self.noise_ampl[intg_idx_]
         return prand
 
     def _update_buffers(self):
@@ -71,7 +70,7 @@ def _integration_dynamics(self, intg_idx):
         spk_hist_to_intg = self.spk_hist[intg_idx]  # beta to be integrated
         step_size_to_intg = self.step_size[intg_idx]  # bias to be integrated
 
-        lfsr = self._prng(shape_like=state_to_intg)
+        lfsr = self._prng(intg_idx_=intg_idx)
 
         state_to_intg = state_to_intg + lfsr + cnstr_to_intg + step_size_to_intg
         np.clip(state_to_intg, a_min=0, a_max=2 ** 23 - 1, out=state_to_intg)

src/lava/proc/scif/process.py

@@ -4,6 +4,7 @@
 
 import typing as ty
 from numpy import typing as npty
+
 import numpy as np
 
 from lava.magma.core.process.process import AbstractProcess

tests/lava/proc/scif/test_models.py

@@ -128,12 +128,6 @@ def test_scif_fixed_pt_no_noise(self) -> None:
                                                      neg_tau_ref=neg_tau_ref,
                                                      wt=wt,
                                                      t_inj_spk={})
-        # voltages = np.hstack((np.arange(num_steps).reshape(num_steps, 1),
-        #                      v_scif,
-        #                      v_lif_wta,
-        #                      v_lif_sig))
-        # np.set_printoptions(linewidth=np.inf, threshold=np.inf)
-        # print(voltages)
         spk_idxs = np.array([theta // step_size - 1 + j * total_period for j in
                              range(num_epochs)]).astype(int)
         wta_pos_spk_idxs = spk_idxs + 1
@@ -364,12 +358,6 @@ def test_scif_fixed_pt_no_noise(self) -> None:
                                                      neg_tau_ref=neg_tau_ref,
                                                      wt=wt,
                                                      t_inj_spk={})
-        # voltages = np.hstack((np.arange(num_steps).reshape(num_steps, 1),
-        #                      v_scif,
-        #                      v_lif_wta,
-        #                      v_lif_sig))
-        # np.set_printoptions(linewidth=np.inf, threshold=np.inf)
-        # print(voltages)
         spk_idxs = np.array([theta // step_size - 1 + j * total_period for j in
                              range(num_epochs)]).astype(int)
         wta_pos_spk_idxs = spk_idxs + 1
@@ -410,12 +398,6 @@ def test_scif_fp_no_noise_interrupt_rfct_mid(self) -> None:
                                                      neg_tau_ref=neg_tau_ref,
                                                      wt=wt,
                                                      t_inj_spk=t_inj_spk)
-        # voltages = np.hstack((np.arange(num_steps).reshape(num_steps, 1),
-        #                       v_scif,
-        #                       v_lif_wta,
-        #                       v_lif_sig))
-        # np.set_printoptions(linewidth=np.inf, threshold=np.inf)
-        # print(voltages)
         # Test pre-inhibitory-injection SCIF voltage and spiking
         spk_idxs_pre_inj = np.array([theta // step_size]).astype(int) - 1
         wta_pos_spk_pre_inj = spk_idxs_pre_inj + 1