Learning stabilizes temporal activity but not neuronal selectivity in prefrontal cortex
Huang, Y.-Y.; Mehrke, L. S.; Bernklau, T. W.; Busse, L.; Jacob, S. N.
Show abstract
Intelligent behavior requires neural representations to change with new demands while preserving learned structure. The prefrontal cortex is central to this ability, but the mechanisms are unclear. Here, we tracked medial prefrontal neurons for months as mice learned an association task with successive rule switches. Learning progressively stabilized when individual neurons were active during a trial, but not what task variables they responded to. Neurons repeatedly gained, lost, or changed selectivity even after their activity profile had stabilized. We developed Sparse Tensor Component Analysis to show that, rather than reflecting random drift, dynamic single-neuron selectivity arose through rule-dependent recombination of a fixed set of task representations. Thus, learning established a stable temporal scaffold in the prefrontal cortex within which neurons participated flexibly in different representations.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A neural mechanism for conserved value computations integrating information and rewards 98%
- Interplay between persistent activity and activity-silent dynamics in prefrontal cortex during working memory 97%
- Correlations enhance the behavioral readout of neural population activity in association cortex 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.