diff --git a/nwbwidgets/icephys.py b/nwbwidgets/icephys.py
index 2f052183..a2e88bed 100644
--- a/nwbwidgets/icephys.py
+++ b/nwbwidgets/icephys.py
@@ -2,20 +2,20 @@
 from .timeseries import show_timeseries_mpl
 from ipywidgets import widgets
 import matplotlib.pyplot as plt
-from ndx_icephys_meta.icephys import SweepSequences
+from ndx_icephys_meta.icephys import SequentialRecordingsTable
 from functools import partial
 import numpy as np
 from matplotlib.pyplot import Figure
 import pandas as pd
 
 
-def show_single_sweep_sequence(sweep_sequence, axs=None, title=None, **kwargs) -> Figure:
+def show_single_sequential_recording(sequential_recording, axs=None, title=None, **kwargs) -> Figure:
     """
     Show a single rep of a single stimulus sequence
 
     Parameters
     ----------
-    sweep_sequence
+    sequential_recording
     axs: [matplotlib.pyplot.Axes, matplotlib.pyplot.Axes], optional
     title: str, optional
     kwargs: dict
@@ -28,57 +28,57 @@ def show_single_sweep_sequence(sweep_sequence, axs=None, title=None, **kwargs) -
 
     """
 
-    nsweeps = len(sweep_sequence)
+    nsweeps = len(sequential_recording)
     if axs is None:
         fig, axs = plt.subplots(2, 1, sharex=True)
     else:
         fig = axs[0].get_figure()
     for i in range(nsweeps):
-        start, stop, ts = sweep_sequence['recordings'].iloc[i]['response'].iloc[0]
+        start, stop, ts = sequential_recording['recordings'].iloc[i]['responses']['response'].iloc[0]
         show_timeseries_mpl(ts, istart=start, istop=stop, ax=axs[0], zero_start=True, xlabel='', title=title, **kwargs)
 
-        start, stop, ts = sweep_sequence['recordings'].iloc[i]['stimulus'].iloc[0]
+        start, stop, ts = sequential_recording['recordings'].iloc[i]['stimuli']['stimulus'].iloc[0]
         show_timeseries_mpl(ts, istart=start, istop=stop, ax=axs[1], zero_start=True, **kwargs)
     return fig
 
 
-def show_sweep_sequence_reps(stim_df: pd.DataFrame, **kwargs) -> Figure:
+def show_sequential_recordings_reps(stim_df: pd.DataFrame, **kwargs) -> Figure:
     """
-    Show data from multiple reps of the same stimulus type
+    Show data from multiple repetitions of the same stimulus type
 
     Parameters
     ----------
     stim_df: pandas.DataFrame
     kwargs: dict
-        passed to show_single_sweep_sequence
+        passed to show_single_sequential_recording
 
     Returns
     -------
     matplotlib.pyplot.Figure
 
     """
-    nsweeps = len(stim_df['sweeps'])
+    nsweeps = len(stim_df['simultaneous_recordings'])
 
     if 'repetition' in stim_df:
         stim_df = stim_df.sort_values('repetition')
     fig, axs = plt.subplots(2, nsweeps, sharex='col', sharey='row', figsize=[6.4 * nsweeps, 4.8])
     if nsweeps == 1:
         axs = np.array([axs]).T
-    for i, (sweep, sweep_axs) in enumerate(zip(stim_df['sweeps'], axs.T)):
+    for i, (sweep, sweep_axs) in enumerate(zip(stim_df['simultaneous_recordings'], axs.T)):
         if i:
             kwargs.update(ylabel='')
-        show_single_sweep_sequence(sweep, axs=sweep_axs, title='rep {}'.format(i+1), **kwargs)
+        show_single_sequential_recording(sweep, axs=sweep_axs, title='Repetition {}'.format(i+1), **kwargs)
     return fig
 
 
-def show_sweep_sequences(node: SweepSequences, *args, style: GroupingWidget = widgets.Accordion, **kwargs) -> \
+def show_sequential_recordings(node: SequentialRecordingsTable, *args, style: GroupingWidget = widgets.Accordion, **kwargs) -> \
         GroupingWidget:
     """
-    Visualize the sweep sequences table with a lazy accordion of sweep sequence repetitions
+    Visualize the sequential recordings table with a lazy accordion of stimulus types
 
     Parameters
     ----------
-    node: SweepSequences
+    node: SequentialRecordingsTable
     style: widgets.Accordion or widgets.Tabs
 
     Returns
@@ -89,7 +89,7 @@ def show_sweep_sequences(node: SweepSequences, *args, style: GroupingWidget = wi
     if 'stimulus_type' in node:
         labels, data = zip(*[(stim_label, stim_df)
                              for stim_label, stim_df in node.to_dataframe().groupby('stimulus_type')])
-        func_ = show_sweep_sequence_reps
+        func_ = show_sequential_recordings_reps
     else:
         data = node['sweeps']
         labels = None
diff --git a/nwbwidgets/view.py b/nwbwidgets/view.py
index a3f69825..0aecf7a9 100644
--- a/nwbwidgets/view.py
+++ b/nwbwidgets/view.py
@@ -4,12 +4,12 @@
 from nwbwidgets import behavior, misc, base, ecephys, image, ophys, icephys, timeseries, file
 import hdmf
 from functools import partial
-from ndx_icephys_meta.icephys import SweepSequences
+from ndx_icephys_meta.icephys import SequentialRecordingsTable
 
 
 default_neurodata_vis_spec = {
     pynwb.NWBFile: file.show_nwbfile,
-    SweepSequences: icephys.show_sweep_sequences,
+    SequentialRecordingsTable: icephys.show_sequential_recordings,
     pynwb.behavior.BehavioralEvents: behavior.show_behavioral_events,
     pynwb.ecephys.LFP: ecephys.show_lfp,
     pynwb.misc.Units: OrderedDict({
diff --git a/setup.py b/setup.py
index 3183c120..1edafbc4 100644
--- a/setup.py
+++ b/setup.py
@@ -28,7 +28,7 @@
                       'plotly',
                       'scikit-image',
                       'tqdm>=4.36.0',
-                      'ndx-icephys-meta'],
+                      'ndx-icephys-meta>0.1.0'],
     license='MIT',
     keywords=['jupyter', 'hdf5', 'notebook', 'nwb'],
     long_description=long_description,