hdnet

hdnet stands for Hopfield denoising network. It is a Python package for analysis of neural population spiking data, i.e. parallel spike trains.

In particular, it provides a novel method for finding and extracting salient low-dimensional representations of the dynamics of populations of spiking neurons based on a denoising approach to spatiotemporal patterns (STP) contained in the data.

Finding STP is classical problem in data analysis of parallel spike trains, and quite a number of approaches to detect and classify recurring spatiotemporal patterns (STP) of neural population activity were proposed [1].

Yet, most published methods so far either focus solely on synchrony detection [2, 3, 4] or assume a more or less noiseless scenario, seeking to classify exactly recurring STP in neuronal activity (apart from allowing some jitter in spike timing), see e.g. [5]. Some furthermore suffer from effects of combinatorial explosion [3, 5], making their application to highly parallel neural recordings a hard computational problem.

Given the usually high variability of population responses to stimuli, the re-occurrence of such exactly repeating STP becomes more and more unlikely with increasing population size, though. Assuming that despite this variability, network activity is not random per se (under the experimentally well-supported hypothesis [6, 7] that the population has to code information about stimuli in some form of STP), a much more plausible situation is that some underlying STP appears in several "corrupted" variants, both expressing jitter in spike times and differing in a few missing or excess spikes (characterized by a low Hamming distance to a true, underlying STP).

Our method takes a different aproach. Using Hopfield networks trained with minimum probability flow (MPF), the occuring raw spatiotemporal patterns are grouped into clusters of similar patterns in an unsupervised way, assigning to each cluster a memory (the fixed point of the Hopfield dynamics in each cluster).

The proposed method is robust to this variability in the signal and able to extract the underlying recurring patterns, even for seldomly occurring STP and large population sizes.

The package ships with a tutorial and is extensively documented.

References

[1] S. Grün and S. Rotter. Analysis of parallel spike trains. Springer, 2010
[2] G. Pipa, D. W. Wheeler, W. Singer, and D. Nikolić. NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events. Journal of Computational Neuroscience, 25(1):64–88, 2008.
[3] D. Picado-Muiño, C. Borgelt, D. Berger, G. Gerstein, and S. Grün. Finding neural assemblies with frequent item set mining. Frontiers in neuroinformatics, 7(May):9, 2013.
[4] V. Lopes-dos-Santos, S. Ribeiro, and A. B. L. Tort. Detecting cell assemblies in large neuronal populations. Journal of Neuroscience Methods, 220(2):149–66, 2013.
[5] K. S. Gansel and W. Singer. Detecting multineuronal temporal patterns in parallel spike trains. Frontiers in Neuroinformatics, 6(May):18, 2012.
[6] M. Abeles and H. Bergman. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. Journal of Neurophysiology, 1993.
[7] A. Arieli and D. Shoham. Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. Journal of Neurophysiology, 1995.

Installing

Until we provide an official release, please install hdnet directly from Github using pip.

To install / update to the latest version use:

pip install git+ssh://git@github.com/team-hdnet/hdnet.git

To install a specific version, use:

pip install git+ssh://git@github.com/team-hdnet/hdnet@VERSION,

where VERSION is v0.1 for example. See releases tab above for released versions.

Documentation

View the documentation (online and PDF version available).

Citing hdnet

If you use hdnet in your work please cite it using the following BibTeX entry.

@online{hdnet,
    author      = {Hillar, Christopher  and Effenberger, Felix},
    title       = {\texttt{hdnet} -- a Python package for parallel spike train analysis},
    volume      = {abs/XXX},
    year        = {2015},
    eprinttype  = {arxiv},
    eprint      = {math/0307200v3}
    url         = {https://github.com/team-hdnet/hdnet}
    //ee        = {http://arxiv.org/abs/XXX},
}

Authors

Christopher Hillar
Redwood Center for Theoretical Neuroscience
UC Berkeley, Berkeley, CA, USA
E-mail: chillar@berkeley.edu

Felix Effenberger
Max-Planck-Institute for Mathematics in the Sciences
Leipzig, Germany
E-mail: Felix.Effenberger@mis.mpg.de

License

For non-commercial use, hdnet is licensed under the GNU GPLv3, see file LICENSE. For use in commercial projects please contact the authors for licensing options.

Contributing

We are looking for contributors! If you are interested in contributing or find a bug, please contact us personally or via a pull request!

Building the documentation

Install required modules pip install sphinx sphinx_rtd_theme sphinxcontrib-napoleon sphinxcontrib-bibtex sphinxjp.themes.basicstrap
Build documentation: HTML: make html LaTeX: make latexpdf
Built documentation can be found in doc/_build/html and doc/_build/latex

rueberger/hdnet