Neurons and Cognition (q-bio.NC)

Abstract: Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in the context of scenarios involving continuous sensory streams, such as speech and music listening. Over the past 10 years or so, novel analytic frameworks for analysing the neural processing of continuous sensory streams combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. As a result, numerous data formats and analysis toolkits are available, with limited or no compatibility between studies. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. The framework has been designed to interface easily with existing toolboxes (e.g., EelBrain, NapLib, MNE, mTRF-Toolbox). We present guidelines by taking both the user view (how to load and rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share). Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. In doing so, we aim to facilitate data sharing and promote transparent research practices, while also making the process as straightforward and accessible as possible for all users.

Abstract: When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together.