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How do neurons code information? Recent work emphasizes the geometry and information content of population codes, using measures that are blind to the native tunings (or ‘axes’) of …

Comparing internal representations is a central goal in neuroscience and machine learning, but standard linear alignment metrics (Representational Similarity Analysis, Centered Kernel Alignment, and linear regression) are frequently …

Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains poorly understood. In this work, we characterize …

Internal neural representations can systematically deviate from externally measured sensory and behavioral variables, yet neuroscientists lack a principled statistical framework to quantify these mismatches. Here we introduce a …

Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predictive of alignment with brain representation. A …

Representational similarity metrics typically force all units to be matched, making them susceptible to noise and outliers common in neural representations. We extend the soft-matching distance to …

This paper introduces quasi-Monte Carlo latent variable models (QLVMs): a class of deep generative models that are specialized for finding extremely low-dimensional and interpretable embeddings of …

The Poisson Generalized Linear Model (GLM) is a foundational tool for analyzing neural spike train data. However, standard implementations rely on discretizing spike times into binned count data …

Neural activity is usually interpreted by imposing external labels (e.g., stimuli or position during locomotion) and decoding within that space (e.g. replay). While powerful, such supervision …

Color discrimination thresholds—the smallest detectable color differences—provide a benchmark for models of color vision, enable quantitative evaluation of eye diseases, and inform the design of display …

Neural population activity exhibits complex, nonlinear dynamics, varying in time, over trials, and across experimental conditions. Here, we develop Conditionally Linear Dynamical System (CLDS) models as a general …

Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global structure can have strikingly different local geometries. Here …

Methods for tracking lab animal movements in unconstrained environments have become increasingly common and powerful tools for neuroscience. The prevailing hypothesis is that animal behavior in these environments …

Biological and artificial neural systems form high-dimensional neural representations that underpin their computational capabilities. Methods for quantifying geometric similarity in neural representations have become a popular tool …

Quantifying differences across species and individuals is fundamental to many fields of biology. However, it remains challenging to draw detailed functional comparisons between large populations of interacting neurons …

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards and actions, in which the extent of tuning depends on ongoing task demands1 …

Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning …

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with …

Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or “decoders” that …

Centered kernel alignment (CKA) and representational similarity analysis (RSA) of dissimilarity matrices are two popular methods for comparing neural systems in terms of representational geometry. Although they follow …

We propose a framework for comparing a set of image representations (artificial or biological) in terms of their sensitivities to local distortions. We quantify the local geometry of …

Understanding the behavioral and neural dynamics of social interactions is a goal of contemporary neuroscience. Many machine learning methods have emerged in recent years to make sense of …

Representation of the environment by hippocampal populations is known to drift even within a familiar environment, which could reflect gradual changes in single-cell activity or result from …

The canonical polyadic (CP) tensor decomposition decomposes a multidimensional data array into a sum of outer products of finite-dimensional vectors. Instead, we can replace some or all …

How do neurons code information? Recent work emphasizes properties of population codes, such as their geometry and decodable information, using measures that are blind to the native tunings …

Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning. Although many methods have been proposed, only a few …

Uncovering and comparing the dynamical mechanisms that support neural processing remains a key challenge in the analysis of biological and artificial neural systems. However, measures of representational (dis …

Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging …