Typical use cases

Recording multiple trials from multiple channels

In this example we suppose that we have recorded from an 8-channel probe, and that we have recorded three trials/episodes. We therefore have a total of 8 x 3 = 24 signals, grouped into three AnalogSignal objects, one per trial.

Our entire dataset is contained in a Block, which in turn contains:

  • 3 Segment objects, each representing data from a single trial,
  • 1 Group.

Segment and Group objects provide two different ways to access the data, corresponding respectively, in this scenario, to access by time and by space.


Segments do not always represent trials, they can be used for many purposes: segments could represent parallel recordings for different subjects, or different steps in a current clamp protocol.

Temporal (by segment)

In this case you want to go through your data in order, perhaps because you want to correlate the neural response with the stimulus that was delivered in each segment. In this example, we’re averaging over the channels.

import numpy as np
from matplotlib import pyplot as plt

for seg in block.segments:
    print("Analyzing segment %d" % seg.index)

    avg = np.mean(seg.analogsignals[0], axis=1)

    plt.title("Peak response in segment %d: %f" % (seg.index, avg.max()))

Spatial (by channel)

In this case you want to go through your data by channel location and average over time. Perhaps you want to see which physical location produces the strongest response, and every stimulus was the same:

# We assume that our block has only 1 Group
group = block.groups[0]
avg = np.mean(group.analogsignals, axis=0)

for index, name in enumerate(group.annotations["channel_names"]):
    plt.plot(avg[:, index])
    plt.title("Average response on channels %s: %s' % (index, name)

Mixed example

Combining simultaneously the two approaches of descending the hierarchy temporally and spatially can be tricky. Here’s an example. Let’s say you saw something interesting on the 6th channel (index 5) on even numbered trials during the experiment and you want to follow up. What was the average response?

index = 5
avg = np.mean([seg.analogsignals[0][:, index] for seg in block.segments[::2]], axis=1)

Recording spikes from multiple tetrodes

Here is a similar example in which we have recorded with two tetrodes and extracted spikes from the extra-cellular signals. The spike times are contained in SpikeTrain objects.

  • 3 Segments (one per trial).
  • 7 Groups (one per neuron), which each contain:
    • 3 SpikeTrain objects
    • an annotation showing which tetrode the spiketrains were recorded from

In total we have 3 x 7 = 21 SpikeTrains in this Block.



In this scenario we have discarded the original signals, perhaps to save space, therefore we use annotations to link the spiketrains to the tetrode they were recorded from. If we wished to include the original extracellular signals, we would add a reference to the three AnalogSignal objects for the appropriate tetrode to the Group for each neuron.

There are three ways to access the SpikeTrain data:

  • by trial (Segment)
  • by neuron (Group)
  • by tetrode

By trial

In this example, each Segment represents data from one trial, and we want a PSTH for each trial from all units combined:

for seg in block.segments:
    print(f"Analyzing segment {seg.index}")
    stlist = [st - st.t_start for st in seg.spiketrains]
    plt.subplot(len(block.segments), 1, seg.index + 1)
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title(f"PSTH in segment {seg.index}")

By neuron

Now we can calculate the PSTH averaged over trials for each unit, using the block.groups property:

for i, group in enumerate(block.groups):
    stlist = [st - st.t_start for st in group.spiketrains]
    plt.subplot(len(block.groups), 1, i + 1)
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title(f"PSTH of unit {group.name}")

By tetrode

Here we calculate a PSTH averaged over trials by channel location, blending all units:

for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]):
    stlist = []
    for unit in block.filter(objects=Group, tetrode_id=tetrode_id):
        stlist.extend([st - st.t_start for st in unit.spiketrains])
    plt.subplot(2, 1, i + 1)
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title(f"PSTH blend of tetrode {tetrode_id}")

Spike sorting

Spike sorting is the process of detecting and classifying high-frequency deflections (“spikes”) on a group of physically nearby recording channels.

For example, let’s say you have recordings from a tetrode containing 4 separate channels. Here is an example showing (with fake data) how you could iterate over the contained signals and extract spike times. (Of course in reality you would use a more sophisticated algorithm.)

# generate some fake data
seg = Segment()
    AnalogSignal([[0.1, 0.1, 0.1, 0.1],
                [-2.0, -2.0, -2.0, -2.0],
                [0.1, 0.1, 0.1, 0.1],
                [-0.1, -0.1, -0.1, -0.1],
                [-0.1, -0.1, -0.1, -0.1],
                [-3.0, -3.0, -3.0, -3.0],
                [0.1, 0.1, 0.1, 0.1],
                [0.1, 0.1, 0.1, 0.1]],
                sampling_rate=1000*Hz, units='V'))

# extract spike trains from all channels
st_list = []
for signal in seg.analogsignals:
    # use a simple threshold detector
    spike_mask = np.where(np.min(signal.magnitude, axis=1) < -1.0)[0]

    # create a spike train
    spike_times = signal.times[spike_mask]
    st = SpikeTrain(spike_times, t_start=signal.t_start, t_stop=signal.t_stop)

    # remember the spike waveforms
    wf_list = []
    for spike_idx in np.nonzero(spike_mask)[0]:
        wf_list.append(signal[spike_idx-1:spike_idx+2, :])
    st.waveforms = np.array(wf_list)


At this point, we have a list of spiketrain objects. We could simply create a single Group object, assign all spiketrains to it, and then also assign the AnalogSignal on which we detected them.

unit = Group()
unit.spiketrains = st_list

Further processing could assign each of the detected spikes to an independent source, a putative single neuron. (This processing is outside the scope of Neo. There are many open-source toolboxes to do it, for instance our sister project OpenElectrophy.)

In that case we would create a separate Group for each cluster, assign its spiketrains to it, and still store in each group a reference to the original recording.