simplecochlea.spikes package¶

Submodules¶

simplecochlea.spikes.spikelist module¶

This file is part of simplecochlea.

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class simplecochlea.spikes.spikelist.SpikeList(time=[], channel=[], pattern_id=[], potential=[], n_channels=0, name='', tmin=0, tmax=[], pattern_names={})[source]¶

Bases: object

SpikeList Class. A spike is defined by a time and a channel. In addition a pattern ID ad a potential can be assigned to spikes. These characteristics are defined as list.

Attributes:

Attributes:	time : array Time of each spike (in seconds) channel : array [int] Channel of each spike pattern_id : array [int] Pattern ID of each spike (Optional). If not defined is nan potential : array Spikes potential (Optional). If not defined is 0 n_channels : int Number of channels in the spikelist n_spikes : int Number of spikes in the spikelist name : str Spikelist name tmin : float Starting time of the spikelist (default: 0) Note For spikelist generating by running a signal through the cochlea, the tmin attribute is not the time of the first spike but the starting time of the signal (0 usually). tmax : float Ending time of the spikelist. If not given, tmax is set to the maximal spike time Note For spikelist generating by running a signal through the cochlea, the tmax attribute should not be the time of the last spike but the ending time of the signal. pattern_names : dict Dictionnary giving a label to each pattern defined by pattern_id

time : array: Time of each spike (in seconds)
channel : array [int]: Channel of each spike
pattern_id : array [int]: Pattern ID of each spike (Optional). If not defined is nan
potential : array: Spikes potential (Optional). If not defined is 0
n_channels : int: Number of channels in the spikelist
n_spikes : int: Number of spikes in the spikelist
name : str: Spikelist name
tmin : float: Starting time of the spikelist (default: 0)

Note

For spikelist generating by running a signal through the cochlea, the tmin attribute is not the time of the first spike but the starting time of the signal (0 usually).
tmax : float: Ending time of the spikelist. If not given, tmax is set to the maximal spike time

Note

For spikelist generating by running a signal through the cochlea, the tmax attribute should not be the time of the last spike but the ending time of the signal.
pattern_names : dict: Dictionnary giving a label to each pattern defined by pattern_id

Methods

`add_time_offset`(t_offset)	Add a time offset to the spikes time.
`convert_to_packet`(packet_size[, overlap])	Deprecated.
`epoch`(t_start, t_end)	Epoch the spikelist given the time periods defined by `t_start` and `t_end`.
`epoch_on_triggers`(t_triggers, time_pre, …)	Apply the epoch function on each trigger whose time is defined by t_triggers.
`export`([export_path, export_name])	Export the spikelist as a .mat file
`get_channel_selectivity`()	Compute channel selectivity.
`get_mean_isi`()	Compute and return the mean ISI (Inter-Spike-Interval) for each channel.
`get_median_isi`()	Compute and return the median ISI (Inter-Spike-Interval) for each channel.
`get_pattern_results`(t_start, t_end, pattern_id)	Compute the number of spikes per segment.
`plot`([bin_duration, potential_thresh, …])	Plot the spikelist.
`plot_channel_selectivity`([title_str])	Plot channel selectivity.
`select_spike`([time_min, time_max, …])	Select a subset of the spikelist.
`set_pattern_id_from_time_limits`(t_start, …)	Given time limits given by t_start and t_end, set the pattern_id of spikes in this time interval.
`set_pattern_id_from_time_limits_old`(t_start, …)	Deprecated - Use set_pattern_id_from_time_limits
`sort`(field)	Sort the spikelist by increasing attribute, selected by field
`to_dataframe`()	Convert the spikelist to a dataframe containing 3 fields : ‘time’, ‘channel’ and ‘pattern_id’

add_time_offset(t_offset)[source]¶: Add a time offset to the spikes time. Used when creating list of spikelists.

convert_to_packet(packet_size, overlap=0)[source]¶: Deprecated. Used to convert the spikelist to spike packets

epoch(t_start, t_end)[source]¶

Epoch the spikelist given the time periods defined by t_start and t_end. Returns a SpikeList_list instance.

Parameters:	t_start : array Start of each time period (s) t_end : array End of each time period (s)
Returns:	spikelist_list : SpikeList_list A SpikeList_list instance.

epoch_on_triggers(t_triggers, time_pre, time_post)[source]¶

Apply the epoch function on each trigger whose time is defined by t_triggers.

Parameters:	t_triggers : array Time of each trigger (s) time_pre : float Time to keep before triggers time_post : float Time to keep after triggers
Returns:	spikelist_list : SpikeList_list A SpikeList_list instance.

export(export_path='.', export_name='spikelist_0_')[source]¶

Export the spikelist as a .mat file

Parameters:	export_path : str Export directory path (default: ‘.’) export_name : str Export file name

get_channel_selectivity()[source]¶: Compute channel selectivity. Definition ?

get_mean_isi()[source]¶

Compute and return the mean ISI (Inter-Spike-Interval) for each channel.

Returns:	mean_isi : array Mean ISI for each channel of the spikelist.

get_median_isi()[source]¶

Compute and return the median ISI (Inter-Spike-Interval) for each channel.

Returns:	median_isi : array Median ISI for each channel of the spikelist.

get_pattern_results(t_start, t_end, pattern_id, pattern_names=[], min_potential=[], do_plot=True, fig_title='')[source]¶

Compute the number of spikes per segment. Segments are defined by t_start and t_end. There might be multiples patterns, defined by pattern_id and pattern_names. If min_potential is defined, select only the spikes whose potential is higher than this. If do_plot is true, plot the results.

Parameters:

Parameters:	t_start : array Starting times of the segments (s) t_end : array Ending times of the segments (s) pattern_id : array Pattern id of the segments pattern_names : array \| None Pattern name of the segments min_potential : float \| None If defined select only spikes whose potential is higher than this do_plot : bool \| None (default: True) If true, plot the results fig_title : str \| None Figure’s title
Returns:	n_spikes_mean : array (size n_patterns) Mean number of spikes across repetition for each pattern n_spikes_per_chunk : array (size n_segments) Number of spikes for each segment n_active_chan_mean : array (size n_pattern) Mean number of active channel across repetition for each pattern n_active_chan_per_chunk : array (size n_segments) Number of active channel for each segment

t_start : array: Starting times of the segments (s)
t_end : array: Ending times of the segments (s)
pattern_id : array: Pattern id of the segments
pattern_names : array | None: Pattern name of the segments
min_potential : float | None: If defined select only spikes whose potential is higher than this
do_plot : bool | None (default: True): If true, plot the results
fig_title : str | None: Figure’s title

Returns:

n_spikes_mean : array (size n_patterns): Mean number of spikes across repetition for each pattern
n_spikes_per_chunk : array (size n_segments): Number of spikes for each segment
n_active_chan_mean : array (size n_pattern): Mean number of active channel across repetition for each pattern
n_active_chan_per_chunk : array (size n_segments): Number of active channel for each segment

plot(bin_duration=0.002, potential_thresh=[], ax_list=[], minplot=0, tau_lif=[], pattern_id_sel=[], color=[])[source]¶

Plot the spikelist. The main central axis plots the spike list, one dot for each spike. Each pattern has a different color. The bottom axis plots the histogram of the spikes for all channels. The right axis sums up the spikes over time for each channel. The left axis plot the median ISI (Inter-Spike Interval), and if the tau_lif parameter is given, the ratio ISI_med / Tau_Lif is also plotted.

Parameters:

Parameters:	bin_duration : float (default: 0.002s) Bin duration in seconds for the time histogram, in the bottom axis. potential_thresh : float \| none (default) Potential threshold, only spike whose potential is higher than this value will be plotted. If none, plot all the spikes. ax_list : list \| none (default) Axis handles to plot on. If none, create new axes. minplot : bool (default: False) If True, plot only the central plot. tau_lif : float \| array \| none (default) Tau parameter of the LIF bank of the cochlea. If provided, the ratio ISI_med / Tau_Lif is plotted on the left axis. pattern_id_sel : int \| none (default) Pattern to select. If provided, the spikes of the selected pattern will appears on a different color in the left and right axes. color : list \| array \| none (default) Color of the patterns. If none, use default color
Returns:	ax_list : list List of the axes [central, bottom, right, left]

bin_duration : float (default: 0.002s): Bin duration in seconds for the time histogram, in the bottom axis.
potential_thresh : float | none (default): Potential threshold, only spike whose potential is higher than this value will be plotted. If none, plot all the spikes.
ax_list : list | none (default): Axis handles to plot on. If none, create new axes.
minplot : bool (default: False): If True, plot only the central plot.
tau_lif : float | array | none (default): Tau parameter of the LIF bank of the cochlea. If provided, the ratio ISI_med / Tau_Lif is plotted on the left axis.
pattern_id_sel : int | none (default): Pattern to select. If provided, the spikes of the selected pattern will appears on a different color in the left and right axes.
color : list | array | none (default): Color of the patterns. If none, use default color

Returns:

ax_list : list: List of the axes [central, bottom, right, left]

plot_channel_selectivity(title_str='')[source]¶

Plot channel selectivity. Definition ?

Parameters:	title_str : str Optional title for the figure

select_spike(time_min=[], time_max=[], channel_sel=[], potential_min=[], potential_max=[], pattern_id_sel=[])[source]¶

Select a subset of the spikelist. If multiples selection parameters are given, select the spikes meeting all conditions.

Parameters:

Parameters:	time_min : float (s) \| None Select spikes whose time is superior or equal to time_min time_max : float (s) \| None Select spikes whose time is inferior or equal to time_max channel_sel : list \| array \| None Select spikes whose channel is in channel_sel potential_min : float \| None Select spikes whose potential is superior or equal to potential_min potential_max : float \| None Select spikes whose potential is inferior or equal to potential_max pattern_id_sel : int Select spikes whose pattern_id is equal to pattern_id_sel
Returns:	sel_ind : array [bool] Boolean array representing selected spikes spikelist_sel : SpikeList Selected SpikeList n_spikes : int Number of spikes in the selected spikelist

time_min : float (s) | None: Select spikes whose time is superior or equal to time_min
time_max : float (s) | None: Select spikes whose time is inferior or equal to time_max
channel_sel : list | array | None: Select spikes whose channel is in channel_sel
potential_min : float | None: Select spikes whose potential is superior or equal to potential_min
potential_max : float | None: Select spikes whose potential is inferior or equal to potential_max
pattern_id_sel : int: Select spikes whose pattern_id is equal to pattern_id_sel

Returns:

sel_ind : array [bool]: Boolean array representing selected spikes
spikelist_sel : SpikeList: Selected SpikeList
n_spikes : int: Number of spikes in the selected spikelist

set_pattern_id_from_time_limits(t_start, t_end, pattern_id, pattern_dict)[source]¶

Given time limits given by t_start and t_end, set the pattern_id of spikes in this time interval.: Also add entries to the pattern_names dictionnary of the spikelist.

Parameters:	t_start : array Start of each time period (s) t_end : array End of each time period (s) pattern_id : array [int] Pattern ID of each time period pattern_dict : Dictionnary given the labels associated with each different pattern ID.

set_pattern_id_from_time_limits_old(t_start, t_end, pattern_id, pattern_names=[])[source]¶: Deprecated - Use set_pattern_id_from_time_limits

sort(field)[source]¶

Sort the spikelist by increasing attribute, selected by field

Parameters:	field : str Attribute to sort the spikelist. Must be in [‘time’, ‘channel’, ‘pattern_id’, ‘potential’]

to_dataframe()[source]¶

Convert the spikelist to a dataframe containing 3 fields : ‘time’, ‘channel’ and ‘pattern_id’

Returns:	df : Pandas DataFrame The spikelist as a dataframe

class simplecochlea.spikes.spikelist.SpikeList_list(spikelist_list, t_start=[], t_end=[])[source]¶

Bases: object

SpikeList_list Class. Defines a list of SpikeList instances. Can represents differents epochs of a global spikelist, spikelists corresponding to repetitions of a pattern.

Methods

`compute_channel_with_spikes`()	Get proportion of channel with spikes in all epochs
`compute_fano_factor`()	Compute the Fano Factor (~dispersion) of the number of spikes in each spike list, for each channel.
`compute_spike_per_channel_variation`()	Compute the coefficient of variation of the number of spikes per channel for each spikelist
`compute_vanrossum_distance`([cos, tau])	Compute the van Rossum distance (see: A Novel Spike Distance - M. C. W. van Rossum).
`plot`([n_max_cols, plot_vr_dist])	Plot all the spikelist in the same figure
`plot_superpose`([plot_fano_factor, plot_vr_dist])	Plot all the spikelists of the SpikeList_list superimposed on the same axes.

compute_channel_with_spikes()[source]¶

Get proportion of channel with spikes in all epochs

Returns:	channel_with_spikes_ratio : array

compute_fano_factor()[source]¶

Compute the Fano Factor (~dispersion) of the number of spikes in each spike list, for each channel. Fano factor is defined as the variance divided by the mean of a random process The global fano factor returned is the mean across channels

Returns:	fano_factor : global_fano_factor :

compute_spike_per_channel_variation()[source]¶

Compute the coefficient of variation of the number of spikes per channel for each spikelist

Returns:	var_coeff, var_coeff_mean

compute_vanrossum_distance(cos=0.1, tau=0.001)[source]¶

Compute the van Rossum distance (see: A Novel Spike Distance - M. C. W. van Rossum). Also compute the distance normalized by sqrt((M+N)/2) where M and N are the number of spikes in the 2 spike trains. This way the normalized distance between 2 uncorrelated Poisson spike trains in 1. The result is divided by sqrt(2) due to the implementation in pymuvr to get similar results as in the original paper.

The function return the mean of these distance across each comparison of the spikelist_list for each channel.

Parameters:	cos : float ??? - Does not seem to have much influence tau : float Time constant for the exponential tail
Returns:	mean_vr_dist mean_vr_dist_norm : global_vr_dist: global_vr_dist_norm

plot(n_max_cols=5, plot_vr_dist=0)[source]¶

Plot all the spikelist in the same figure

Parameters:	n_max_cols : int Maximal number of columns. plot_vr_dist : bool (default: False)

plot_superpose(plot_fano_factor=1, plot_vr_dist=1)[source]¶

Plot all the spikelists of the SpikeList_list superimposed on the same axes.

Parameters:	plot_fano_factor : bool (default: True) If True, plot the Fano Factor plot_vr_dist : bool (default: True) If True, plot the mean Van-Rossum distance

simplecochlea.spikes.spikelist.dual_spikelist_plot(spikelist_in, spikelist_out, potential_thresh_in=0, potential_thresh_out=0, tau_lif=[], pattern_id_sel_in=[], pattern_id_sel_out=[])[source]¶

Dual Spikelist Plot.: Plot 2 spikelists in the same figure. Useful when the two spikelist share common parameters. The second spikelist can be obtained from the first one, after running a learning rule for example. First/Top spikelist is defined by spikelist_in and the second/bottom spikelist is defined by spikelist_out

Parameters:

Parameters:	spikelist_in : SpikeList First spikelist, plot on top spikelist_out : SpikeList Second spikelist, plot at the bottom potential_thresh_in : float If defined, only spikes of spikelist_in whose threshold is superior to this value will be plot. potential_thresh_out : float If defined, only spikes of spikelist_out whose threshold is superior to this value will be plot. tau_lif : array Tau value of the LIF neurons of the cochlea (used to generate these spikelists). pattern_id_sel_in : int If defined, only spikes of spikelist_in with this ID will be plot pattern_id_sel_out : int If defined, only spikes of spikelist_out with this ID will be plot
Returns:	[ax0, ax1, …, ax7] : list A list of all the axes

spikelist_in : SpikeList: First spikelist, plot on top
spikelist_out : SpikeList: Second spikelist, plot at the bottom
potential_thresh_in : float: If defined, only spikes of spikelist_in whose threshold is superior to this value will be plot.
potential_thresh_out : float: If defined, only spikes of spikelist_out whose threshold is superior to this value will be plot.
tau_lif : array: Tau value of the LIF neurons of the cochlea (used to generate these spikelists).
pattern_id_sel_in : int: If defined, only spikes of spikelist_in with this ID will be plot
pattern_id_sel_out : int: If defined, only spikes of spikelist_out with this ID will be plot

Returns:

[ax0, ax1, …, ax7] : list: A list of all the axes

simplecochlea.spikes.spikelist.import_spikelist_from_mat(import_path, n_channels)[source]¶

Import a spikelist (SpikeList instance) from a .mat file.

Parameters:	import_path : str Spikelist file path n_channels : int Number of channels in the spikelist - Now is included in the spikelist.
Returns:	spikelist : SpikeList The SpikeList instance.

simplecochlea.spikes.spikepacket module¶

class simplecochlea.spikes.spikepacket.SpikePacket(spike_list, packet_size, overlap=0)[source]¶

Bases: object

Methods

plot

plot()[source]¶

Module contents¶

Spikes Module