1,184 research outputs found
Synthesizing realistic neural population activity patterns using generative adversarial networks
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
A new class of indicators for the model selection of scaling laws in nuclear fusion
The development of computationally efficient model selection strategies
represents an important problem facing the analysis of Nuclear Fusion
experimental data, in particular in the field of scaling laws for the
extrapolation to future machines, and image processing. In this paper, a new
model selection indicator, named Model Falsification Criterion (MFC), will be
presented and applied to the problem of choosing the most generalizable scaling
laws for the power threshold to access the H-mode of confinement in Tokamaks.
The proposed indicator is based on the properties of the model residuals, their
entropy and an implementation of the data falsification principle. The model
selection ability of the proposed criterion will be demonstrated in comparison
with the most widely used frequentist (Akaike Information Criterion) and
bayesian (Bayesian Information Criterion) indicators.Comment: 4 pages, 2 figure
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