diff --git a/herdingspikes/detection_lightning/detect.pyx b/herdingspikes/detection_lightning/detect.pyx
index b4865ed..7ce0052 100644
--- a/herdingspikes/detection_lightning/detect.pyx
+++ b/herdingspikes/detection_lightning/detect.pyx
@@ -433,8 +433,8 @@ class HSDetectionLightning(object):
             )
 
         result: dict[str, RealArray] = {
-            "sample_ind": sample_ind,
-            "channel_ind": channel_ind,
+            "sample_index": sample_ind,
+            "channel_index": channel_ind,
             "amplitude": amplitude,
         }
         if self.localize:
diff --git a/herdingspikes/hs2.py b/herdingspikes/hs2.py
index 9f32d99..b63cebc 100644
--- a/herdingspikes/hs2.py
+++ b/herdingspikes/hs2.py
@@ -517,8 +517,8 @@ def DetectFromRaw(self):
             sp[0]["spike_shape"] = np.zeros(len(sp[0]["sample_ind"]))
         self.spikes = pd.DataFrame(
             {
-                "ch": sp[0]["channel_ind"],
-                "t": sp[0]["sample_ind"],
+                "ch": sp[0]["channel_index"],
+                "t": sp[0]["sample_index"],
                 "Amplitude": sp[0]["amplitude"],
                 "x": sp[0]["location"][:, 0],
                 "y": sp[0]["location"][:, 1],
@@ -540,6 +540,7 @@ def DetectFromRaw(self):
             else:
                 h["cutout_length"] = 0
             h.close()
+        return sp
 
     def PlotTracesChannels(
         self,
@@ -1432,3 +1433,49 @@ def PlotNeighbourhood(
                     ax[1].plot(self.spikes.Shape[i], color=(0.4, 0.4, 0.4))
                 ax[1].plot(np.mean(self.spikes.Shape[spInds].values, axis=0), color="k")
         return ax
+
+
+def detect_peaks_lightning(recording, params=None):
+    """
+    Detect spikes in a recording using the lightning framework. This function is compatible
+    with the SpikeInterface sorting components framework. Note it does not return spike locations.
+
+    Parameters
+    ----------
+    recording : RecordingExtractor
+        The recording extractor object
+    params : dict
+        The parameters for the spike detection. If None, default parameters are used.
+
+    Returns
+    -------
+    peaks : np.array
+        Structured array with the detected peaks. Fields are:
+        * 'sample_index' : int
+            The index of the peak sample
+        * 'channel_index' : int
+            The index of the channel
+        * 'amplitude' : float
+            The amplitude of the peak
+
+    """
+
+    det = HSDetectionLightning(recording, params=params)
+    peaks = det.DetectFromRaw()
+    peaks_array = np.array(
+        list(
+            tuple(
+                map(
+                    tuple,
+                    np.array(
+                        [
+                            (peaks[0][k])
+                            for k in ["sample_index", "channel_index", "amplitude"]
+                        ]
+                    ).T,
+                )
+            )
+        ),
+        dtype=[("sample_index", "<i8"), ("channel_index", "<i8"), ("amplitude", "<f8")],
+    )
+    return peaks_array