Published February 23, 2023
| Version v1
Journal article
Open
Low-dimensional encoding of decisions in parietal cortex reflects long-term training history
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.
Files
Low-dimensional-encoding-of-decisions-in-parietal-cortex-reflects-long-term-training-history.pdf
Files
(21.3 MB)
| Name | Size | Download all |
|---|---|---|
|
Supplementary information md5:3c80642582b4b34e9333d83d6df2eb6d |
6.2 MB | Preview Download |
|
Peer review file md5:0d399d41380c7a2132fc38554bf32164 |
518.2 kB | Preview Download |
|
Reporting summary md5:ffd0957d1f4de76962ab596ff5136652 |
86.9 kB | Preview Download |
|
Source data md5:90814077bc5d352687645e330aaafef4 |
11.6 MB | Download |
|
Article md5:930ed1aebc96b923f14dcc9556b8a1ed |
2.9 MB | Preview Download |
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