make spiking work when no neurons are recorded

Author: kjohnsen

cleo/ephys/spiking.py

@@ -245,6 +245,12 @@ def connect_to_neuron_group(self, neuron_group: NeuronGroup, **kwparams):
         # spike sorters don't sort by multi-channel recall; we only do this
         # for computational efficiency, to not get super distant neurons
 
+        # needed even if no neurons are recorded
+        if not self._prev_t:
+            self._prev_t = self.probe.sim.network.t
+        if self._mu_eap is None:
+            self._mu_eap = np.zeros((0, self.n_channels))
+
         n2keep = np.sum(where_to_keep)
         if n2keep == 0:
             warnings.warn(
@@ -271,14 +277,8 @@ def connect_to_neuron_group(self, neuron_group: NeuronGroup, **kwparams):
         assert len(self.i_ng_by_i_probe) == i_probe_start
         self.i_ng_by_i_probe.extend(list(zip([neuron_group] * n2keep, i2keep)))
 
-        if not self._prev_t:
-            self._prev_t = self.probe.sim.network.t
-
         # store neuron-channel mean and stdev of EAPs to use in subclasses
-        if self._mu_eap is None:
-            self._mu_eap = snr[where_to_keep]
-        else:
-            self._mu_eap = np.concatenate((self._mu_eap, snr[where_to_keep]), axis=0)
+        self._mu_eap = np.concatenate((self._mu_eap, snr[where_to_keep]), axis=0)
 
         return snr[where_to_keep].max(axis=1), new_i_probe
 
@@ -642,7 +642,11 @@ def sorted_units_snr(self) -> Float[np.ndarray, "{self.n_sorted}"]:
         return self._mu_eap[self._i_probe_by_i_sorted]
 
     def connect_to_neuron_group(self, neuron_group, **kwparams):
-        snr, i_probe = super().connect_to_neuron_group(neuron_group, **kwparams)
+        connect_result = super().connect_to_neuron_group(neuron_group, **kwparams)
+        if connect_result is None:
+            # No neurons with high-enough multi-channel recall to record
+            return
+        snr, i_probe = connect_result
         n_recorded_ng = len(snr)
         # filter by SNR (measured on peak channel)
         above_cutoff = snr >= self.snr_cutoff

tests/ephys/test_spiking.py

@@ -530,6 +530,26 @@ def test_cutoff(rand_seed):
     )
 
 
+def test_no_neurons():
+    """When neurons are too far away to be picked up at all."""
+    sgg = spike_generator_group([9000, 9001, 9002] * mm, period=1 * ms)
+    sim = CLSimulator(Network(sgg))
+    assert hasattr(sim, "network")
+    ss = SortedSpiking()
+    mua = MultiUnitActivity()
+    probe = Probe([0, 0, 0] * um, [ss, mua])
+    sim.inject(probe, sgg)
+    sim.run(10 * ms)
+    probe.get_state()
+    sim.run(10 * ms)
+    probe.get_state()
+    for sig in [ss, mua]:
+        assert len(sig.i) == 0
+        assert len(sig.t) == 0
+        assert len(sig.t_samp) == 0
+        assert sig.n_neurons == 0
+
+
 def _test_spiking_to_neo(spike_signal_class):
     spike_sig = spike_signal_class()
     n_channels = 50