no code implementations • NeurIPS 2008 • Arno Onken, Steffen Grünewälder, Matthias Munk, Klaus Obermayer
Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.
no code implementations • NeurIPS 2009 • Arno Onken, Steffen Grünewälder, Klaus Obermayer
The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts.
no code implementations • NeurIPS 2016 • Arno Onken, Stefano Panzeri
Our methods hold the promise to considerably improve statistical analysis of neural data recorded simultaneously at different scales.
no code implementations • ICLR 2018 • Arezoo Alizadeh, Marion Mutter, Thomas Münch, Arno Onken, Stefano Panzeri
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions.
1 code implementation • ICLR 2018 • Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, Stefano Panzeri
The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing.
no code implementations • 12 Aug 2018 • Shanshan Jia, Zhaofei Yu, Arno Onken, Yonghong Tian, Tiejun Huang, Jian. K. Liu
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.
1 code implementation • ICLR 2020 • Bennet Breier, Arno Onken
After correction, our best CNN beats the SVM by 6. 12%, achieving a classification accuracy of 96. 32%.
no code implementations • 3 Aug 2020 • Nina Kudryashova, Theoklitos Amvrosiadis, Nathalie Dupuy, Nathalie Rochefort, Arno Onken
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.
1 code implementation • 6 Sep 2020 • Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken
Here, we propose a Generative Adversarial Network (GAN) model to generate realistic calcium signals as seen in neuronal somata with calcium imaging.
1 code implementation • 1 Jan 2021 • Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken
Calcium imaging has become a powerful and popular technique to monitor the activity of large populations of neurons in vivo.
no code implementations • 3 Feb 2021 • Cole Hurwitz, Nina Kudryashova, Arno Onken, Matthias H. Hennig
Modern recording technologies now enable simultaneous recording from large numbers of neurons.
no code implementations • 25 Nov 2021 • Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken
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.
no code implementations • 11 Jul 2022 • Lazaros Mitskopoulos, Theoklitos Amvrosiadis, Arno Onken
Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces.
1 code implementation • 6 Feb 2023 • Bryan M. Li, Isabel M. Cornacchia, Nathalie L. Rochefort, Arno Onken
Accurate predictive models of the visual cortex neural response to natural visual stimuli remain a challenge in computational neuroscience.