Prefrontal cortex exhibits multi-dimensional dynamic encoding during decision-making

2019

41 citations recorded by the source corpus.

Recent work has suggested that the prefrontal cortex (PFC) plays a key role in context-dependent perceptual decision-making. In this study, we addressed that role using a new method for identifying task-relevant dimensions of neural population activity. Specifically, we show that the PFC has a multidimensional code for context, decisions and both relevant and irrelevant sensory information. Moreover, these representations evolve in time, with an early linear accumulation phase followed by a phase with rotational dynamics. We identify the dimensions of neural activity associated with these phases and show that they do not arise from distinct populations but from a single population with broad tuning characteristics. Finally, we use model-based decoding to show that the transition from linear to rotational dynamics coincides with a plateau in decoding accuracy, revealing that rotational dynamics in the PFC preserve sensory choice information for the duration of the stimulus integration period. Aoi et al. used a new dimensionality-reduction method to disentangle the contributions of different task variables to neural population activity, which revealed rotational dynamics in monkey PFC during context-dependent decision-making.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Molecular Biology

Citations in the corpus

• Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, positive): “We assessed the embedding dimensionality of the patterns observed in monkey FEF using the same truncated SVD approach described above for the human fMRI data.”