Published February 23, 2023 | Version v1
Journal article Open

Low-dimensional encoding of decisions in parietal cortex reflects long-term training history

  • 1. University of Chicago

Description

Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach—generalized multilinear models—to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks.

Data availability

The datasets analyzed during the current study are available from the corresponding authors of the original studies (refs. 13 and 22) on reasonable request. Source Data are provided with this paper for all figures.

All GLM and GMLM analyses were performed using custom software for MATLAB (MathWorks) and CUDA (Nvidia). The GMLM tools are available publicly and can be found at https://github.com/latimerk/GMLM_dmc for both MATLAB and Python. Higher-order singular value decompositions for visualizing the subspaces were performed with Tensor Toolbox for MATLAB.

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Additional details

Identifiers

DOI
10.1038/s41467-023-36554-5
Other
oai:uchicago.tind.io:5556

Funding

University of Chicago
Biological Sciences Divison Chicago Fellows Fellowship
National Institutes of Health
R01 EY019041
National Institutes of Health
NIH R01 NS107609
DOD
VBFF

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Neurobiology