Search Results for author:
Found 7 papers, 5 papers with code
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
In this paper, we overcome this data bottleneck and advance the detection of MDs acute episode vs stable state from wearables data on the back of recent advances in self-supervised learning (SSL).
V1T: large-scale mouse V1 response prediction using a Vision Transformer
Accurate predictive models of the visual cortex neural response to natural visual stimuli remain a challenge in computational neuroscience.
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.
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.
Synthesising Realistic Calcium Traces of Neuronal Populations Using GAN
Here, we propose a Generative Adversarial Network (GAN) model to generate realistic calcium signals as seen in neuronal somata with calcium imaging.
Unsupervised Cipher Cracking Using Discrete GANs
This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.
