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 models 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.

The softmax function is ubiquitous in machine learning and optimization applications.

In this work, we study the relationship between these objectives.

Automatically generating novel and interesting games is a complex task.

Sampling from probability distributions is a crucial task in both statistics and machine learning. However, when the target distribution does not permit exact sampling, researchers often rely on Markov chain Monte Carlo (MCMC) methods.

However, sampling is not the only source of uncertainty. In practice, distributions change between locations and across time.

Humans have a powerful and mysterious capacity to reason.

By analyzing the loss landscape of a single Transformer layer using Softmax and Gaussian attention kernels, our work provides concrete answers to these questions.

As such, it is often desired to select a small number of informative features to consider in subsequent analyses.