Developed as part of the COSMOS project:
cSPIKE is an easy to use spike train analysis software. It runs on Matlab command line and uses MEX files with C++ backends for speed. cSPIKE implements functions such as ISI-distance , SPIKE-distance , SPIKE-synchronization  and their adaptive variants  as well as the directional SPIKE-order . It also includes basic functions for plotting spike trains and profiles. The ISI-distance, the SPIKE-distance, and SPIKE-synchronization are time-scale adaptive measures of spike train similarity that can be used for assessing similarity between two or more point processes. In neuroscience the point processes are often taken as action potentials either from simultaneously recorded neuronal populations or from the same neuron recorded during different time intervals.
http://www.fi.isc.cnr.it/users/thomas.kreuz/Source-Code/cSPIKE.htmlReferences common for cSPIKE, SPIKY (see below) and PySpike (see below):
 Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A:
Measuring spike train synchrony.
J Neurosci Methods 165, 151 (2007) [PDF]. Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F:
Monitoring spike train synchrony.
J Neurophysiol 109, 1457 (2013) [PDF]. Kreuz T, Mulansky M, Bozanic N:
SPIKY: A graphical user interface for monitoring spike train synchrony.
JNeurophysiol 113, 3432 (2015) [PDF] Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T:
Leaders and followers: Quantifying consistency in spatio-temporal propagation patterns.
New J. Phys., 19, 043028 [PDF] and arXiv [PDF] (2017) cSPIKE was developed by COSMOS ESR Eero Satuvuori (Project 9).
We provide the Matlab source code to calculate the measure L first introduced in  with the improvements introduced in . The nonlinear interdependence measure L allows for nonlinear coupling detection between two point processes. In particular, the nonlinear interdependence measure L is suited to be applied to spike trains from neurons. The approach is modular, for example different spike train distances  can be used to assess similarity. This modularity allows one to test for different manifestations of the coupling such as signatures in the event rate or event timing
For a detailed description of the source code see the document MalvestioPRE2017Readme.pdf available on the download page. We also provide simulated data from Hindmarsh-Rose dynamics to allow testing the code.
 Andrzejak RG, Kreuz T: “Characterizing unidirectional couplings between point processes and flows.” EPL, 96 (2011) 50012.
 Malvestio I, Kreuz T, Andrzejak RG: “Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains.” Physical Review E, 96 (2017) 022203.
 We use source code from cSPIKE (see above).
Two libraries, both written in Rust. The first is for solving ODEs, the
second is for performing Gram-Schmidt Orthonormalization:
(also available on PyPI as ‘oscillator_snap’)
OscillatorSnap is a Python package for modeling oscillators with artificial neural networks. It uses a Tensor Flow and a Keras and provides straight-forward and non-technical high-level functions meant to appeal to non-
experts of neural nets. It helps you train a recurrent neural network on
oscillatory signals. And then from the trained network forecast the future state or probe the network for dynamical responses, e.g. estimate the
phase response curve and maximal Lyapunov exponent. Alongside that it also provides functions for analyzing oscillators from their dynamical
 R. Cestnik and M. Abel, Inferring the dynamics of oscillatory systems using recurrent neural networks, Chaos (submitted)
Short description: A C-code performing the Explosive Immunization algorithm for fast network dismantling. Based on the concept of explosive percolation, the algorithm properly identifies the nodes whose removal minimizes the size of the largest component of a given network, thus preventing the spreading of epidemic processes. The method uses two different scores to quantify the blocking ability of each node. The first is used whenever the number of removed nodes is large enough to prevent the existence of a giant component, whereas the second is used when a giant component is unavoidable. For more information check the original paper .
 Clusella P., Grassberger P., Perez-Reche, F. J. and Politi A. “Immunization and targeted destruction of networks using explosive percolation”, Phys. Rev. Lett. 117, 208301 (2016)
AMCOS_booklet (developed on the occasion of the AMCOS conference organised by the ESRs in Barcelona on March 19-23, 2018) is a LaTeX template for conference booklets, sometimes called book of abstracts. It includes an additional python script to automatise the management and inclusion of abstracts. The template also has an option to compile a short and a long version of the booklet, for print and online use, for example. The template is ready to use as is, but also easily customisable for willing users.
– slick look, easily customisable in terms of layout, colours, and content
– templates for various typical sections of a booklet
– LaTeX environments to display the timetable, and a (long and short version of) list of abstracts, of posters, and of participants
– automated management of abstracts via additional python script.
– automated creation of a short or long version of the booklet via a one-word option in the template. Creation of both via an additional bash script.
Developed by COSMOS groups related to the project:
SPIKY is a Matlab graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to simulated and real data. It contains the standard Peri-Stimulus Time Histogram (PSTH), the ISI-distance, the SPIKE-distance, SPIKE synchronization, and the directional SPIKE-order.
For a given dataset SPIKY calculates the measures of choice and allows the user to switch between different visualizations such as dissimilarity profiles, pairwise dissimilarity matrices, or hierarchical cluster trees. SPIKY also includes a spike train generator, an event detector (for continuous data) and complementary programs for the analysis of a large number of datasets and for evaluating the statistical significance of results.
Kreuz T, Mulansky M, Bozanic N: SPIKY: A graphical user interface for monitoring spike train synchrony. JNeurophysiol 113, 3432 (2015) [PDF]
SPIKY was developed by Nebojsa Bozanic and COSMOS PI Thomas Kreuz
PySpike is a Python library for the numerical analysis of spike train similarity. Its core functionality is the implementation of the ISI-distance  and SPIKE-distance  as well as SPIKE-Synchronization. It provides functions to compute multivariate profiles, distance matrices, as well as averaging and general spike train processing. All computation intensive parts are implemented in C via cython to reach a competitive performance (factor 100-200 over plain Python). PySpike provides the same fundamental functionality as the SPIKY framework for Matlab.
Documentation for PySpike can be found here:
Mulansky M, Kreuz T:PySpike – A Python library for analyzing spike train synchrony Software X 5, 183 and arXiv [PDF] (2016
PySpike was developed by Mario Mulansky and COSMOS PI Thomas Kreuz
The Data Analysis with Models Of Coupled Oscillators (DAMOCO) toolbox is a collection of Matlab functions for multivariate data analysis, based on the coupled oscillators approach. It provides functions for computation of protophases (initial phase estimates) from time series via the Hilbert Transform, marker events, or via length of the trajectory in the state space. It allows a researcher to transform protophases into true phases and infer equations of phase dynamics of coupled oscillators. With DAMOCO you can easily compute synchronization and directionality indices as well as reveal a coupling structure of a network by means of pairwise or triplet analysis.
Detrended Fluctuation Analysis (DFA) is a classical approach to analyze long-range temporal and spatial correlations. This Matlab implementation provides an algorithm not only to quantify power-law scaling but first to test it against alternatives via (Bayesian) model comparison.
Like in conventional DFA, after removing (linear or nonlinear) trends of a signal, mean squared fluctuations in consecutive intervals are determined. In contrast to DFA all values per interval are used to approximate the distribution of these mean squared fluctuations. This allows for estimating the corresponding log-likelihood as a function of interval size without presuming the fluctuations to be normally distributed.
Ton R, Daffertshofer A: Model selection for identifying power-law scaling.
Neuroimage 136:215-26, 2016, doi:10.1016/j.neuroimage.2016.01.008
‘Fluctuation Analysis’ was developed by COSMOS PI Andreas Daffertshofer.
Other source codes from the Nonlinear Time Series Analysis Group at the Universitat Pompeu Fabra:
These source codes were developed by COSMOS PI Ralph Andrzejak.