Topic: Centered kernel alignment

Comparing high-dimensional neural response patterns across different animals or artificial deep networks is a fundamental problem in computational neuroscience. There are now many methods for quantifying representational dissimilarity including canonical correlations analysis (CCA), centered kernel alignment (CKA), representational similarity analysis (RSA), shape metrics, and so on. Intuitively, these measures quantify similarity in the geometry of neural responses while removing expected forms of invariance across networks (e.g. permutations over arbitrary neuron indices). However, these met

Neural networks are now widely used for modeling neural activity in the brain. They have been particularly successful in modeling the visual system, using mostly feedforward networks and leveraging community-wide efforts centered around benchmarks to both improve model architectures and evaluate model fits to data. Now that recurrent neural networks (RNNs) are also used to model a larger variety of brain functions, there is a similar need for developing appropriate metrics to compare these models. Towards this goal, we have built a high-throughput pipeline for training different RNN models on

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

Constructing well-motivated methodologies for quantifying similarity between high-dimensional neural representations is an active research in computational neuroscience. Such similarity measures have implications for understanding variability in neural computations across individuals and species [1], as well as for comparisons between biological systems and computational models [2]. One common approach to understand these systems is to build regression models or “decoders” that reconstruct features of the stimulus from neural responses. Here, we leverage this idea to quantify the similarity of