RESUMO
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. 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. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at www.sensorium-competition.net.
RESUMO
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
RESUMO
Lipin functions in mammalian phospholipid biosynthesis through its phosphatidate phosphohydrolase 1 (PAP(1)) activity. Here, we studied cardiac PAP(1) activity and lipin expression ex vivo in 8-month-old Zucker diabetic fatty (ZDF) rats and humans with type 2 diabetes mellitus undergoing open heart surgery for coronary bypass grafting. Compared to non-diabetic littermates (ZDF-fa/+), left ventricular PAP(1) activity was 29% lower in diabetic ZDF-fa/fa rats. Left ventricular PAP(1) activities were 2.1-fold (ZDF-fa/fa) and 3.6-fold (ZDF-fa/+) higher than the respective atrial activities, indicating marked differences in cardiac distribution of PAP(1). PAP(1) activity was highly related with cardiac lipin-1 and lipin-3 mRNA expression in ZDF rats (r=0.99 and 0.96). Consistent with the findings in experimental animals, human atrial tissue displayed PAP(1) activity that was 33% lower in those having diabetes than in non-diabetic controls. Accordingly, atrial lipin-1 and lipin-3 mRNA expression in diabetic patients was 50% and 59% lower as in non-diabetic patients, respectively. Insulin therapy increased both PAP(1) activity and lipin mRNA expression in diabetic patients. We conclude that suppression of cardiac PAP(1) activity/lipin expression may contribute to metabolic dysfunction of the diabetic heart.
