637 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
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.Comment: Published as a conference paper at ICLR 2018 V2: minor changes in
supp. materia
CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of Neuronal Populations
Calcium imaging has become a powerful and popular technique to monitor the
activity of large populations of neurons in vivo. However, for ethical
considerations and despite recent technical developments, recordings are still
constrained to a limited number of trials and animals. This limits the amount
of data available from individual experiments and hinders the development of
analysis techniques and models for more realistic size of neuronal populations.
The ability to artificially synthesize realistic neuronal calcium signals could
greatly alleviate this problem by scaling up the number of trials. Here we
propose a Generative Adversarial Network (GAN) model to generate realistic
calcium signals as seen in neuronal somata with calcium imaging. To this end,
we adapt the WaveGAN architecture and train it with the Wasserstein distance.
We test the model on artificial data with known ground-truth and show that the
distribution of the generated signals closely resembles the underlying data
distribution. Then, we train the model on real calcium signals recorded from
the primary visual cortex of behaving mice and confirm that the deconvolved
spike trains match the statistics of the recorded data. Together, these results
demonstrate that our model can successfully generate realistic calcium imaging
data, thereby providing the means to augment existing datasets of neuronal
activity for enhanced data exploration and modeling
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Common measures of brain functional connectivity (FC) including covariance
and correlation matrices are semi-positive definite (SPD) matrices residing on
a cone-shape Riemannian manifold. Despite its remarkable success for
Euclidean-valued data generation, use of standard generative adversarial
networks (GANs) to generate manifold-valued FC data neglects its inherent SPD
structure and hence the inter-relatedness of edges in real FC. We propose a
novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN)
for FC data generation on the SPD manifold that can preserve the global FC
structure. Specifically, we optimize a generalized Wasserstein distance between
the real and generated SPD data under an adversarial training, conditioned on
the class labels. The resulting generator can synthesize new SPD-valued FC
matrices associated with different classes of brain networks, e.g., brain
disorder or healthy control. Furthermore, we introduce additional population
graph-based regularization terms on both the SPD manifold and its tangent space
to encourage the generator to respect the inter-subject similarity of FC
patterns in the real data. This also helps in avoiding mode collapse and
produces more stable GAN training. Evaluated on resting-state functional
magnetic resonance imaging (fMRI) data of major depressive disorder (MDD),
qualitative and quantitative results show that the proposed GR-SPD-GAN clearly
outperforms several state-of-the-art GANs in generating more realistic
fMRI-based FC samples. When applied to FC data augmentation for MDD
identification, classification models trained on augmented data generated by
our approach achieved the largest margin of improvement in classification
accuracy among the competing GANs over baselines without data augmentation.Comment: 10 pages, 4 figure
A Deep Generative Model for Feasible and Diverse Population Synthesis
An ideal synthetic population, a key input to activity-based models, mimics
the distribution of the individual- and household-level attributes in the
actual population. Since the entire population's attributes are generally
unavailable, household travel survey (HTS) samples are used for population
synthesis. Synthesizing population by directly sampling from HTS ignores the
attribute combinations that are unobserved in the HTS samples but exist in the
population, called 'sampling zeros'. A deep generative model (DGM) can
potentially synthesize the sampling zeros but at the expense of generating
'structural zeros' (i.e., the infeasible attribute combinations that do not
exist in the population). This study proposes a novel method to minimize
structural zeros while preserving sampling zeros. Two regularizations are
devised to customize the training of the DGM and applied to a generative
adversarial network (GAN) and a variational autoencoder (VAE). The adopted
metrics for feasibility and diversity of the synthetic population indicate the
capability of generating sampling and structural zeros -- lower structural
zeros and lower sampling zeros indicate the higher feasibility and the lower
diversity, respectively. Results show that the proposed regularizations achieve
considerable performance improvement in feasibility and diversity of the
synthesized population over traditional models. The proposed VAE additionally
generated 23.5% of the population ignored by the sample with 79.2% precision
(i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of
the ignored population with 89.0% precision. The proposed improvement in DGM
generates a more feasible and diverse synthetic population, which is critical
for the accuracy of an activity-based model
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