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Notebook 1 — The recording

SWC ENC 2026 · ephys-pop module

Before we can sort spikes we need to know exactly what a recording is. This notebook builds the forward model the rest of the day inverts: a probe with channels at known positions, neurons with known waveforms and spike times, and the voltage that results. Because we choose the ground truth, every later stage can be graded against it.

In this notebook you will:

1. Build a probe and see that a spike lands on several channels at once.
2. Read a unit's footprint and locate it on the probe.
3. Generate a full recording and find the true spikes buried in it.
4. Recover a clean waveform from noisy data by averaging — a preview of the whole pipeline's logic.

import numpy as np
import matplotlib.pyplot as plt
import picosort as ps

1. The probe

A Neuropixels probe is a thin shank studded with hundreds of recording sites. We use a simpler stand-in: a single column of n_channels sites stacked vertically, pitch micrometres apart. The only thing that matters is that channels have positions — so a spike near one site also shows up, more weakly, on its neighbours.

probe = ps.make_probe(n_channels=32, pitch=20.0)
print(f"{probe.n_channels} channels spanning {probe.y.min():.0f}–{probe.y.max():.0f} µm")

# Drawn sideways: depth runs left-to-right, channel 0 (top of probe) at the left.
ps.plotting.plot_probe(probe)
plt.show()

32 channels spanning 0–620 µm

2. A unit and its template

A single neuron shows up as a template: its stereotyped spike shape, spread across channels. We build one from two pieces — a temporal waveform (the canonical biphasic extracellular spike: a sharp negative trough, then a slower positive rebound) and a spatial footprint (amplitude falls off with distance from the cell body). Their product is the spatiotemporal template.

# Place a unit at depth 300 µm with a 150 µV peak amplitude.
unit_xy = np.array([0.0, 300.0])
waveform = ps.spike_waveform()
template = ps.make_template(probe, unit_xy, amplitude=150.0, waveform=waveform)
print("template shape (channels, samples):", template.shape)

fig, axes = plt.subplots(1, 3, figsize=(11, 5), width_ratios=[1.4, 2, 1.4])
axes[0].plot(waveform, np.arange(len(waveform)))
axes[0].set_title("temporal waveform"); axes[0].set_xlabel("amplitude (norm.)")
axes[0].set_ylabel("time (samples)"); axes[0].invert_yaxis()
ps.plotting.plot_template(template, probe, ax=axes[1])
ps.plotting.plot_footprint(template, probe, ax=axes[2])
plt.tight_layout(); plt.show()

template shape (channels, samples): (32, 61)

The spike is biggest on the channels nearest the cell and fades away above and below — that spatial spread is both a blessing (it lets us localise the unit) and a curse (nearby units overlap). The peak-to-peak amplitude per channel is the footprint; the channel where it's largest is the unit's peak channel.

Exercise 1 (~4 min · easy). Complete footprint (peak-to-peak per channel) and peak_channel (the channel with the biggest footprint). The peak channel should be the one physically nearest the unit's true depth.

Check / unstuck. The unit is at 300 µm with 20 µm pitch, so expect peak channel 15 (≈ 300/20). Stuck? Use ps.footprint(template) and ps.peak_channel(template).

def footprint(template):
    return template.max(axis=1) - template.min(axis=1)

def peak_channel(template):
    return int(np.argmax(footprint(template)))

pc = peak_channel(template)
print(f"peak channel: {pc}  (at depth {probe.y[pc]:.0f} µm; unit truly at {unit_xy[1]:.0f} µm)")

peak channel: 15  (at depth 300 µm; unit truly at 300 µm)

3. A full recording

Now the real thing. make_recording scatters several units at random depths, draws a Poisson spike train for each — spikes fired at random times at some average rate per second, the simplest stand-in for a real neuron's firing — and superposes their templates into the traces. Then it buries them in two kinds of background noise, both of which Notebook 2 is devoted to cleaning up:

• spatially correlated noise. "Correlated" means "tends to move together"; "spatially" means "between nearby channels." Neighbouring sites sit in the same patch of tissue and pick up overlapping electrical fields from far-off sources, so when one channel's noise swings up its neighbours tend to swing up too. (Notebook 2 makes this precise and removes it.)
• a slow common-mode fluctuation: a drifting signal shared by every channel at once, like the tissue's local field potential.

Finally it hands back the traces and the ground truth — every spike time, label, and template, the answers a real recording never gives you.

▸ Go deeper: the generative model the whole pipeline inverts (optional)

Every stage of this module is undoing one equation. The recorded voltage on channel $c$ at time $t$ is a linear superposition of all neurons' spikes plus noise:

$$V_c(t) \;=\; \sum_{j}\ \sum_{s \in \text{spikes of unit } j} W_{j,c}(t - t_s)\ \;+\; \eta_c(t),$$

where $W_{j,c}$ is unit $j$'s template on channel $c$ (a spatial footprint × a temporal waveform), $t_s$ are its spike times (a Poisson process), and $\eta$ is the noise. Reading it left to right is the forward model — what we just built. Spike sorting reads it right to left: given only $V$, recover the unit count, the templates $W_j$, and every spike time $t_s$.

That framing tells you exactly what each later notebook is for. The noise $\eta$ is spatially correlated → whiten it (NB2). The $t_s$ are sparse threshold-crossing events → detect them (NB3). The templates $W_j$ are unknown → cluster similar spikes and average them (NB4–5). And because the model is a sum, overlapping spikes add linearly → they can be pulled apart by fitting the templates back, one at a time (matching pursuit, NB6). Because it's synthetic, we know every term on the right, so we can grade the inversion exactly (NB8).

rec = ps.make_recording(n_units=6, duration_s=20.0, seed=0)
gt = rec.ground_truth

The two objects you'll work with

Almost everything in this module comes out of these two objects, so it's worth a moment to see what's inside them — you'll reach into them in the exercises.

rec — a Recording, the data you'd have from a real probe:

attribute	what it is
rec.traces	the voltage: a (n_samples, n_channels) array
rec.fs	the sampling rate (samples per second)
rec.probe	the Probe from section 1 — rec.probe.y holds the channel depths
rec.duration_s	the length of the recording, in seconds

gt = rec.ground_truth — a GroundTruth, the answers (a real recording never gives you these; we use them only to build intuition now and to grade the sort at the end):

attribute	what it is
gt.spike_times	the sample index of every spike, all units pooled together
gt.spike_labels	which unit fired each of those spikes
gt.templates	each unit's template: (n_units, n_channels, n_samples)
gt.unit_xy, gt.unit_amplitude	each unit's position and peak amplitude

The key idiom you'll use often: the spike times of one unit are gt.spike_times[gt.spike_labels == unit]. Let's peek at a few values:

print(f"rec.traces: {rec.traces.shape}   rec.fs: {rec.fs/1e3:.0f} kHz   duration: {rec.duration_s:.0f} s")
print(f"ground truth: {gt.n_units} units, {len(gt.spike_times)} spikes total")
print("first 5 spike times :", gt.spike_times[:5])
print("first 5 spike labels:", gt.spike_labels[:5], "(the unit that fired each)")
print("spikes per unit     :", np.bincount(gt.spike_labels))
print("true unit depths (µm):", np.round(np.sort(gt.unit_xy[:, 1])).astype(int))

# the idiom in action: pull unit 0's spike times
unit0_times = gt.spike_times[gt.spike_labels == 0]
print(f"\nunit 0 fired {len(unit0_times)} times; first few at samples {unit0_times[:3]}")

rec.traces: (600000, 32)   rec.fs: 30 kHz   duration: 20 s
ground truth: 6 units, 396 spikes total
first 5 spike times : [3244 3874 4851 6002 8445]
first 5 spike labels: [5 3 2 2 5] (the unit that fired each)
spikes per unit     : [64 89 87 51 65 40]
true unit depths (µm): [ 91 165 244 333 459 554]

unit 0 fired 64 times; first few at samples [29154 29926 31239]

Zoom in on a single spike from one unit, on the band of channels around it. The red line is the true spike time. Notice the two things that make sorting hard: a single spike appears on a band of neighbouring channels (so the same event must not be counted on each channel separately), and its amplitude peaks on the channel nearest the cell and fades away — the footprint from section 2, now buried in noise.

# Center on one spike of the largest-amplitude unit and show its channel band.
big_unit = int(np.argmax(gt.unit_amplitude))
ps.plotting.plot_unit_spike(rec, unit=big_unit, which=5, n_channels=13)
plt.show()

That's one spike seen clearly. The catch is that the whole recording — every channel, all 20 seconds — is a dense wall of these events layered on noise, and sometimes two spikes land almost on top of each other so their waveforms add (the problem matching pursuit solves in Notebook 6). Here's a wider window across a band of channels to give a feel for the density:

ps.plotting.plot_traces(rec, channels=range(0, 14), t0=0.90, t1=0.99,
                        mark_spikes=gt.spike_times)
plt.show()

4. Recover a clean waveform by averaging

Here's the whole module's logic in miniature. A single spike is deep in the noise, but the ground truth tells us when every spike of a given unit fired. If we cut a short snippet around each of that unit's spike times and average them, the noise (which is random) cancels and the waveform (which repeats) survives. This is exactly how, later, we'll turn a cluster of detected spikes into a template — the only difference is that here the spike times are handed to us.

Exercise 2 (~7 min · meaty). Complete average_waveform: for a given unit and channel, cut a 2*half+1-sample snippet around each ground-truth spike time and return the average. We'll overlay it on the unit's true template row.

Check / unstuck. The recovered waveform should sit almost on top of the true template (a clean biphasic spike), despite the raw single snippets being noisy. Stuck? Use ps.average_waveform(rec, unit, channel).

def average_waveform(rec, unit, channel, half=30):
    gt = rec.ground_truth
    times = gt.spike_times[gt.spike_labels == unit]
    snippets = np.array([rec.traces[t - half:t + half + 1, channel] for t in times])
    return snippets.mean(axis=0), snippets

unit = 0
pc = int(np.argmax(gt.templates[unit].max(1) - gt.templates[unit].min(1)))
avg, snippets = average_waveform(rec, unit, pc)

plt.figure(figsize=(7, 4))
for s in snippets[:40]:
    plt.plot(s, color="0.8", lw=0.6)
plt.plot(avg, color="tab:blue", lw=2, label="averaged (recovered)")
plt.plot(gt.templates[unit][pc], color="tab:red", lw=1.5, ls="--", label="true template")
plt.title(f"unit {unit}, peak channel {pc}: averaging {len(snippets)} noisy spikes")
plt.xlabel("time (samples)"); plt.ylabel("µV"); plt.legend()
plt.show()

The single snippets (grey) are a mess; their average (blue) lands right on the true template (red). That's the engine underneath everything: noise averages away, signal doesn't. The catch is that on a real recording nobody hands you the spike times or the labels — you have to find the spikes and group them yourself. That's the rest of the module.

Wrap-up

You built the forward model: a probe, spatiotemporal templates, Poisson spike trains, and noisy traces — plus the ground truth to grade against. You saw a spike spread across channels, located a unit from its footprint, and recovered a clean waveform by averaging aligned snippets.

Next (Notebook 2 — preprocessing): before detecting anything, we clean the traces — high-pass filter out the slow common-mode drift, then whiten away the spatial noise correlations so a detection threshold means the same thing on every channel.

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