quickspikes 2.0.7
Advanced Recording Format Tools
License: BSD-3-Clause
Requires Python: >=3.9

quickspikes
-----------

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This is a very basic but fast window discriminator for detecting
and extracting spikes in a time series. It was developed for analyzing
extracellular neural recordings, but also works with intracellular data
and probably other kinds of time series.

Here’s how it works:

.. figure:: algorithm.png
   :alt: detection diagram

   detection diagram

The algorithm iterates through the time series. When the signal crosses
the threshold (1) going away from zero, the algorithm then looks for a
peak (2) that occurs within some number of samples from the threshold
crossing. The number of samples can be adjusted to filter out broad
spikes that are likely to be artifacts. If a peak occurs, its sample
index is added to an array. These times can be used as-is, or they can
be used to extract samples to either side of the peak for further
analysis (e.g. spike sorting).

The algorithm uses a streaming pattern (i.e., it processes chunks of
data and keeps its state between chunks), so it’s suitable for realtime
operations. Many signals of interest will require highpass filtering to
remove slow variations.

Installation and Use
~~~~~~~~~~~~~~~~~~~~

The algorithm is written in cython. You can get a python package from
PyPI:

::

   pip install quickspikes

Or to build from a copy of the repository:

::

   pip install .

To detect peaks, you instantiate the detector with parameters that match
the events you want to detect, and then send the detector chunks of
data. For example, an extracellular recording at 20 kHz stored in 16-bit
integers may have noise with a peak-to-peak amplitude around 2000, and the spikes will be on
the order of 20 samples wide:

.. code:: python

   import quickspikes as qs
   det = qs.detector(1000, 30)
   times = det.send(samples)

You can continue sending chunks of data by calling ``send()``. The
detector will keep its state between calls, so you can detect spikes
that occur on chunk boundaries. For example, if you’re receiving data
from some kind of generator, you could use a pattern like this:

.. code:: python

   for chunk in my_data_generator():
       times = det.send(chunk)
       # process times

Conversely, if the data are not contiguous, you should reinitialize the
detector for each chunk.

You can adjust the detector’s threshold at any point, for example to
compensate for shifts in the mean and standard deviation of the signal:

.. code:: python

   reldet = qs.detector(2.5, 30)
   reldet.scale_thresh(samples.mean(), samples.std())
   # (or just set .thresh property directly)
   times = reldet.send(samples)

To detect negative-going events, you’ll need to invert the signal.

There are also some functions you can use to extract and align spike
waveforms. Given a list of times returned from the ``detector.send()``
method, to extract waveforms starting 30 samples before the peak and
ending 270 samples after:

.. code:: python

   f_times = qs.filter_times(times, 30, samples.size - 270)
   spikes = qs.peaks(samples, f_times, 30, 270)
   times, aligned = qs.realign_spikes(f_times, spikes, upsample=3, jitter=4)

Note that the list of event times may need to be filtered to avoid
trying to access data points outside the bounds of the input time
series. If you care about these events, you’ll need to pad your input
signal. The ``realign_spikes`` function uses a sinc-based resampling to
more accurately locate the peak of the event.

There is also a reference copy of an ANSI C implementation and an
``f2py`` wrapper in ``f2py/``. This algorithm is slightly less efficient
and flexible, but may give better results if included directly in a C
codebase.

Intracellular spikes
~~~~~~~~~~~~~~~~~~~~

There are some specialized tools for working with intracellular data.
Intracellular recordings present some special challenges for detecting
spikes. The data are not centered, and spike waveforms can change over
the course of stimulation. The approach used here is to find the largest
spike in the recording, (typically the first) and locate the onset of
the spike based on when the derivative begins to rapidly increase. The
peak and onset are used to set a threshold for extracting subsequent
spikes.

The following is an example for a recording at 50 kHz of a neuron with
spikes that have a rise time of about 1 ms (50 samples). Spikes
waveforms will start 7 ms before the peak and end 40 ms after, and will
be trimmed to avoid any overlap with subsequent spikes.

.. code:: python

   from quickspikes.intracellular import SpikeFinder
   detector = SpikeFinder(n_rise=50, n_before=350, n_after=2000)
   detector.calculate_threshold(samples)
   times, spikes = zip(*detector.extract_spikes(samples, min_amplitude=10))

License
~~~~~~~

Free for use under the terms of the GNU General Public License. See
[[COPYING]] for details.

If you use this code in an academic work, citations are appreciated.
There is no methods paper describing the algorithm, but the most
relevant reference is:

C. D. Meliza and D. Margoliash (2012). Emergence of selectivity and
tolerance in the avian auditory cortex. Journal of Neuroscience,
doi:10.1523/JNEUROSCI.0845-12.2012