1217051
doi
10.5281/zenodo.1217051
oai:zenodo.org:1217051
user-eu
Onken, Arno
Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy; University of Edinburgh, Edinburgh EH8 9AB, UK
Piasini, Eugenio
Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy; University of Pennsylvania, Philadelphia, PA 19104
Panzeri, Stefano
Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy
Synthesizing realistic neural population activity patterns using Generative Adversarial Networks
Molano-Mazon, Manuel
Laboratory of Neural Computation, Istituto Italiano di Tecnologia, 38068 Rovereto (TN), Italy
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
GANs
spike train analysis
neuroscience
<p>The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain. We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics. We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches. Finally, we show how to exploit a trained Spike-GAN to construct 'importance maps' to detect the most relevant statistical structures present in a spike train. Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience.</p>
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 699829.
Zenodo
2018-05-02
info:eu-repo/semantics/conferencePaper
1217050
user-eu
award_title=Encoding and Transmission of Information in the Mouse Somatosensory Cortex; award_number=699829; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/699829; funder_id=00k4n6c32; funder_name=European Commission;
1579540247.793388
2612252
md5:6d550c8592cfd600ade042d17b827744
https://zenodo.org/records/1217051/files/Synthesizing realistic neural population activity patterns using semi-convolutional GANs.pdf
public
10.5281/zenodo.1217050
isVersionOf
doi