Topic: representational similarity

Neural systems form high-dimensional representations that underpin their computational capabilities. Consequently, methods for quantifying representational similarity in neural representations have become a popular tool for identifying computational principles that are potentially shared across neural systems. These methods generally assume that neural responses are deterministic and static. However, responses of biological systems are noisy and dynamically unfold over time and these characteristics can have substantial influence on a system’s computational capabilities. We first show that exi

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

According to current theory, memories are initially encoded through hippocampal (HC) and neocortical (NC) interactions, and subsequently consolidated during slow-wave-sleep (SWS) into hippocampus-independent neocortical schemas. In this sleep phase, specific neural oscillations, Sharp-Wave-Ripples (SWR) and K-complexes (KC), dominate, respectively, HC and NC.Complementary Learning Systems Theory (CLST)1 proposes that new memories can gradually be integrated into the NC, without catastrophically interfering2 with existing knowledge. However, it assumes a massive replay of previous knowledge int