Analysis of Sequences of Synchronous EvenTs (ASSET)

ASSET is a statistical method [as1] for the detection of repeating sequences of synchronous spiking events in parallel spike trains.

ASSET analysis class object of finding patterns

ASSET(spiketrains_i[, spiketrains_j, …])

Analysis of Sequences of Synchronous EvenTs class.

Patterns post-exploration

synchronous_events_intersection(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of positions (iK, jK) of matrix entries and associated synchronous events SK, finds the intersection among them.
synchronous_events_difference(sse1, sse2[, …])	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), computes the difference between sse1 and sse2.
synchronous_events_identical(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), determines whether sse1 is strictly contained in sse2.
synchronous_events_no_overlap(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), determines whether sse1 and sse2 are disjoint.
synchronous_events_contained_in(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), determines whether sse1 is strictly contained in sse2.
synchronous_events_contains_all(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), determines whether sse1 strictly contains sse2.
synchronous_events_overlap(sse1, sse2)	Given two sequences of synchronous events (SSEs) sse1 and sse2, each consisting of a pool of pixel positions and associated synchronous events (see below), determines whether the two SSEs overlap.

Tutorial

View tutorial

Run tutorial interactively:

Examples

0. Create ASSET class object that holds spike trains.

   ASSET requires at least one argument - a list of spike trains. If spiketrains_y is not provided, the same spike trains are used to build an intersection matrix with.

   >>> import neo
   >>> import numpy as np
   >>> import quantities as pq
   >>> from elephant import asset
   

   >>> spiketrains = [
   ...      neo.SpikeTrain([start, start + 6] * (3 * pq.ms) + 10 * pq.ms,
   ...                     t_stop=60 * pq.ms)
   ...      for _ in range(3)
   ...      for start in range(3)
   ... ]
   >>> asset_obj = asset.ASSET(spiketrains, bin_size=3*pq.ms, verbose=False)
   

1. Build the intersection matrix imat:

   >>> imat = asset_obj.intersection_matrix()
   

2. Estimate the probability matrix pmat, using the analytical method:

   >>> pmat = asset_obj.probability_matrix_analytical(imat,
   ...                                                kernel_width=9*pq.ms)
   

3. Compute the joint probability matrix jmat, using a suitable filter:

   >>> jmat = asset_obj.joint_probability_matrix(pmat, filter_shape=(5, 1),
   ...                                           n_largest=3)
   

4. Create the masked version of the intersection matrix, mmat, from pmat and jmat:

   >>> mmat = asset_obj.mask_matrices([pmat, jmat], thresholds=.9)
   

5. Cluster significant elements of imat into diagonal structures:

   >>> cmat = asset_obj.cluster_matrix_entries(mmat, max_distance=3,
   ...                                         min_neighbors=3, stretch=5)
   

6. Extract sequences of synchronous events:

   >>> sses = asset_obj.extract_synchronous_events(cmat)
   

The ASSET found 2 sequences of synchronous events:

>>> from pprint import pprint
>>> pprint(sses)
{1: {(9, 3): {0, 3, 6}, (10, 4): {1, 4, 7}, (11, 5): {8, 2, 5}}}

References

[as1]

Emiliano Torre, Carlos Canova, Michael Denker, George Gerstein, Moritz Helias, and Sonja Grün. Asset: analysis of sequences of synchronous events in massively parallel spike trains. PLoS computational biology, 12(7):e1004939, 2016. doi:10.1371/journal.pcbi.1004939.