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Found 12 papers, 4 papers with code
Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks
We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models.
Building population models for large-scale neural recordings: opportunities and pitfalls
Modern recording technologies now enable simultaneous recording from large numbers of neurons.
Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN
Calcium imaging has become a powerful and popular technique to monitor the activity of large populations of neurons in vivo.
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.
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships
When the exact density estimation with a parametric model is not possible, our Copula-GP model is still able to provide reasonable information estimates, close to the ground truth and comparable to those obtained with a neural network estimator.
Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification
After correction, our best CNN beats the SVM by 6. 12%, achieving a classification accuracy of 96. 32%.
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell.
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.
Baseline-corrected space-by-time non-negative matrix factorization for decoding single trial population spike trains
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions.
Mixed vine copulas as joint models of spike counts and local field potentials
Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts.
Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.
