diff --git a/src/lava/lib/dnf/inputs/rate_code_spike_gen/models.py b/src/lava/lib/dnf/inputs/rate_code_spike_gen/models.py
index 4dc8de7..026912c 100644
--- a/src/lava/lib/dnf/inputs/rate_code_spike_gen/models.py
+++ b/src/lava/lib/dnf/inputs/rate_code_spike_gen/models.py
@@ -97,6 +97,10 @@ def _compute_spike_distances(self, pattern: np.ndarray) -> np.ndarray:
             np.rint(TIME_STEPS_PER_MINUTE / pattern[idx_non_negligible])\
             .astype(int)
 
+        idx_saturated = np.all([idx_non_negligible, distances == 0.], axis=0)
+
+        distances[idx_saturated] = 1
+
         return distances
 
     def _compute_spike_onsets(self, distances: np.ndarray) -> np.ndarray:
diff --git a/tests/lava/lib/dnf/inputs/rate_code_spike_gen/test_models.py b/tests/lava/lib/dnf/inputs/rate_code_spike_gen/test_models.py
index 4e6c8ce..9471820 100644
--- a/tests/lava/lib/dnf/inputs/rate_code_spike_gen/test_models.py
+++ b/tests/lava/lib/dnf/inputs/rate_code_spike_gen/test_models.py
@@ -227,6 +227,41 @@ def test_generate_spikes(self) -> None:
         finally:
             source.stop()
 
+    def test_spike_rate_saturation(self) -> None:
+        """Tests whether the spike rate is saturated when high pattern
+        amplitude is given (i.e that neuron spikes every time step when its
+        corresponding incoming pattern is high)"""
+        num_steps = 10
+        shape = (5,)
+
+        pattern = np.zeros(shape)
+        pattern[2:3] = 20000.
+
+        expected_spikes = np.array(
+            [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+        )
+
+        source = SourceProcess(shape=shape, data=pattern)
+        spike_gen = RateCodeSpikeGen(shape=shape, seed=42)
+        sink = SinkProcess(shape=(shape[0], num_steps))
+
+        source.out_ports.a_out.connect(spike_gen.in_ports.a_in)
+        spike_gen.out_ports.s_out.connect(sink.in_ports.s_in)
+
+        try:
+            source.run(condition=RunSteps(num_steps=num_steps),
+                       run_cfg=Loihi1SimCfg())
+
+            received_spikes = sink.data.get()
+
+            np.testing.assert_array_equal(received_spikes, expected_spikes)
+        finally:
+            source.stop()
+
 
 if __name__ == '__main__':
     unittest.main()