brainbox.singlecell

Computes properties of single-cells, e.g. the autocorrelation and firing rate.

Functions

acorr

Compute the auto-correlogram of a neuron.

calculate_peths

Calcluate peri-event time histograms; return means and standard deviations for each time point across specified clusters

firing_rate

Computes the instantaneous firing rate of a unit over time by computing a histogram of spike counts over a specified window of time, and summing this histogram over a sliding window of specified time over a specified period of total time.

acorr(spike_times, bin_size=None, window_size=None)[source]

Compute the auto-correlogram of a neuron.

:param : :type : param spike_times: Spike times in seconds. :param : :type : type spike_times: array-like :param : :type : param bin_size: Size of the bin, in seconds. :param : :type : type bin_size: float :param : :type : param window_size: Size of the window, in seconds. :param : :type : type window_size: float :param Returns an (winsize_samples: :param ) array with the auto-correlogram.:

calculate_peths(spike_times, spike_clusters, cluster_ids, align_times, pre_time=0.2, post_time=0.5, bin_size=0.025, smoothing=0.025, return_fr=True)[source]

Calcluate peri-event time histograms; return means and standard deviations for each time point across specified clusters

Parameters
  • spike_times (array-like) – spike times (in seconds)

  • spike_clusters (array-like) – cluster ids corresponding to each event in spikes

  • cluster_ids (array-like) – subset of cluster ids for calculating peths

  • align_times (array-like) – times (in seconds) to align peths to

  • pre_time (float) – time (in seconds) to precede align times in peth

  • post_time (float) – time (in seconds) to follow align times in peth

  • bin_size (float) – width of time windows (in seconds) to bin spikes

  • smoothing (float) – standard deviation (in seconds) of Gaussian kernel for smoothing peths; use smoothing=0 to skip smoothing

  • return_fr (bool) – True to return (estimated) firing rate, False to return spike counts

Returns

peths, binned_spikes

Return type

peths: Bunch({‘mean’: peth_means, ‘std’: peth_stds, ‘tscale’: ts, ‘cscale’: ids})

Return type

binned_spikes: np.array (n_align_times, n_clusters, n_bins)

firing_rate(ts, hist_win=0.01, fr_win=0.5)[source]

Computes the instantaneous firing rate of a unit over time by computing a histogram of spike counts over a specified window of time, and summing this histogram over a sliding window of specified time over a specified period of total time.

Parameters
  • ts (ndarray) – The spike timestamps from which to compute the firing rate..

  • hist_win (float) – The time window (in s) to use for computing spike counts.

  • fr_win (float) – The time window (in s) to use as a moving slider to compute the instantaneous firing rate.

Returns

fr – The instantaneous firing rate over time (in hz).

Return type

ndarray

See also

metrics.firing_rate_cv(), metrics.firing_rate_fano_factor(), plot.firing_rate()

Examples

  1. Compute the firing rate for unit 1 from the time of its first to last spike.
    >>> import brainbox as bb
    >>> import alf.io as aio
    >>> import ibllib.ephys.spikes as e_spks
    (*Note, if there is no 'alf' directory, make 'alf' directory from 'ks2' output directory):
    >>> e_spks.ks2_to_alf(path_to_ks_out, path_to_alf_out)
    # Load a spikes bunch and get the timestamps for unit 1, and calculate the instantaneous
    # firing rate.
    >>> spks_b = aio.load_object(path_to_alf_out, 'spikes')
    >>> unit_idxs = np.where(spks_b['clusters'] == 1)[0]
    >>> ts = spks_b['times'][unit_idxs]
    >>> fr = bb.singlecell.firing_rate(ts)