Topic: population size

Understanding how neural representations align between biological and artificial systems has emerged as a central challenge in computational neuroscience. While deep neural networks now reliably predict neural responses across multiple brain areas, their utility for understanding biological computation remains limited by our ability to accurately measure representational similarities. This limitation becomes particularly acute when working with sparse neural recordings, where traditional similarity metrics may fail to capture true representational relationships. Recent spectral analyses of sim

We investigate the ability of the pairwise maximum entropy model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability of multiple simultaneously active neurons. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while vary