Terminology

We give some terminology used in phy (especially in the Template GUI).

Probe

A probe is a multielectrode array used for an electrophysiological recording session.

Shank

A shank is one of the different physical electrodes used in a recording.

Every shank has a unique identifier, the shank_id, ranging from 0 to n_shanks-1.

Channel

In phy, a channel corresponds to the digital signal recorded on a given electrode site.

There are several mappings between electrode sites, channel numbers specified by the acquisition system, column indexes in the raw data 2D array, channel ids used in phy, and channel labels displayed in phy.

Here is the assumptions made by phy:

  • The channel_map.npy file contains a channel_map 1D integer array, of size (n_channels,).
  • The raw data array has n_channels_dat columns (which may be different from n_channels if the user decides to drop some channels in phy).
  • The raw data columns are immediately and virtually swapped using the channel map, as follows: X = raw_data[:, channel_map]. This applies to all views that show multiple channels: trace view, waveform view, template view, etc. In other words, phy always sees the raw data as a NumPy-like array (in fact, a virtual memmapped and column-swapped array) with n_channels columns, which correspond to the channel_map-swapped columns in the binary file.
  • The channel_ids used internally in phy are always ranging from 0 to n_channels - 1.
  • The channel label displayed in phy of channel channel_id is channel_map[channel_id].

Spike

A spike is an action potential emitted by a given neuron, at a given time. It is recorded on a specific set of channels. It has a specific shape (waveform) on each of these channels. It belongs to a given template and cluster. The spike-cluster assignment is the main output of a spike sorting session.

Every spike has a unique identifier, spike_id, ranging from 0 to n_spikes-1.

Spikes themselves cannot be modified or deleted. Only the spike-cluster assignments, and cluster attributes, can be modified in phy.

Template

A template is defined by a set of waveforms (template waveforms) on specific channels. It is obtained by a spike sorting algorithm based on template matching. The algorithm attributes a template for every spike, along with an amplitude. The waveform of every spike is expected to be the template waveform multiplied by the amplitude.

Every template has a unique identifier, template_id, ranging from 0 to n_templates-1.

The spike-template assignments are saved in spike_templates.npy. This 1D array has n_spikes elements, it gives the template id of every spike.

A template's "best channels" correspond to the channels where the template waveform has been detected. The "best channel" (or peak channel) is the channel with the maximum template waveform amplitude.

Cluster

A cluster is a set of spikes, supposed to have been emitted by a single neuron.

Every cluster has a unique integer, cluster_id, ranging from 0 to n_clusters-1.

The cluster id is unique: when the cluster changes (i.e. spikes are removed or added), the cluster id changes. This simplifies the implementation of phy, which uses an internal cache on disk for performance.

The spike-cluster assignments are saved in spike_clusters.npy. This 1D array has n_spikes elements, it gives the cluster id of every spike.

Cluster vs templates

Initially, before running phy, the spike-cluster and spike-template assignments are identical. If spike_clusters.npy does not exist, it is automatically copied from spike_templates.npy. When modifying the spike-cluster assignments in phy, only spike_clusters.npy is modified, while spike_templates.npy is fixed.

As clusters are merged and split, new clusters are created, old ones are deleted. Therefore, whereas the template ids and clusters ids match initially, they no longer do as soon as the user performs manual clustering actions.

Amplitude

There are several slightly different definitions for the amplitude:

  • Per template:
    • Template amplitude: for every template, the maximum amplitude of the template waveforms across all channels.
  • Per spike:
    • Amplitude: for every spike, the scalar found in the file amplitudes.npy, saved by the spike sorting algorithm.
    • Spike raw amplitude: for every spike, the maximum amplitude of the raw waveforms across all channels (extracted from the raw data file).
    • Spike template amplitude: for every spike, the corresponding template amplitude multiplied by the spike's amplitude.
  • Per cluster:
    • Mean spike template amplitude: for every cluster, the average of the spike template amplitudes.
    • Mean spike raw amplitude: for every cluster, the average of the spike raw amplitudes.

Event

(Upcoming feature). Time behavioral events, like stimulus onsets, that may be supported in a future version of phy.