Bernklau, T. W. | Search bioRxiv

bioRxiv2026

Complementary prefrontal and thalamic representational dynamics during response planning

Advance contextual information can guide behavioral responses, but how prospective action representations unfold across prefrontal and thalamic neuronal populations of the cognitive control network remains unclear. Here, we trained mice on a contextual response-planning task in which auditory cues either predicted the upcoming instructed movement or left response identity unresolved until a later instruction. Predictive contexts improved accuracy and produced subthreshold, directionally biased movements, indicating that mice used contextual information before instruction onset. Extracellular recordings revealed prospective response-side information in prelimbic cortex (PL) and mediodorsal thalamus (MD), but with distinct dynamics. MD represented prospective response side earlier during the context epoch and showed a planning-related temporal advance after instruction onset. In contrast, PL exhibited enhanced instruction-epoch coding and stronger cross-epoch generalization, which could be localized to sparse neuronal subpopulations. These complementary representational dynamics suggest distinct but coordinated roles for prefrontal-thalamic circuits in using advance information to support flexible action planning.

neuroscience↗

bioRxiv2026

Learning stabilizes temporal activity but not neuronal selectivity in prefrontal cortex

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.

neuroscience↗