Stephan Pohl | Search arXiv

arXiv2024

Clarifying the conceptual dimensions of representation in neuroscience

Despite the centrality of the notion of representation in neuroscience, the field lacks a unified framework for the concepts used to characterize representation, leading to disparate use of both terminology and measures associated with it. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience. These dimensions describe relations between a neural response, features that may be represented, and downstream effects of the neural response. A neural response may be shown to be sensitive or specific to a feature, invariant to other features, or functional (it is used downstream in the brain). We use information-theoretic measures to illustrate these conceptual dimensions and explain how they relate to data analysis methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence or adaptation. We consider several canonical examples, including models of the representation of orientation, numerosity, and spatial location, which illustrate how the evidence put forth in support or criticism of these models is systematized by our framework. By offering a unified conceptual framework to characterize representation in neuroscience, we hope to aid the comparison and integration of results across studies and research groups and to help determine when evidence for a neural representation is strong.

q-bio.NC↗

arXiv2022

Studying the neural representations of uncertainty

The study of the brain's representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer's beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between "code-driven" and "correlational" approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance, functionality. Our analysis reveals that the two approaches lead to different, but complementary findings, shaping new research questions and guiding future experiments.

q-bio.NC↗