Back

Population-level temporal decoding and dynamical structure in frontostriatal circuits during decision making

Goldring, A. B.; De, A.; Stevenson, T.; Stewart, K.; Akhmetzhanova, A.; Chaudhuri, R.; Hanks, T. D.

2025-11-09 neuroscience
10.1101/2025.11.07.687310 bioRxiv
Show abstract

Perceptual decisions unfold over time, requiring neural circuits to evaluate sensory evidence, track elapsed time, and commit to an action. To investigate how these computations are distributed across corticostriatal circuits, we recorded neural population activity using Neuropixels probes in the rat frontal orienting field (FOF) and anterior dorsal striatum (ADS) during a free-response auditory change detection task. Both regions exhibited interesting dynamical properties during task performance. Using single-trial population decoding, we found that both FOF and ADS robustly encoded retrospective time from stimulus onset and prospective time preceding the decision report. Using a new approach to analyze temporal encoding to identify the dynamical structure supporting decoding, we found that time encoding could be decomposed into two primary dynamical motifs: monotonic ramp-like trajectories and transient bump-like trajectories. While these modes were similarly expressed during early evidence evaluation, FOF exhibited more pronounced decision-aligned changes near the time of the decision report, possibly reflecting a state transition at commitment. Population geometry analyses further revealed stable low-dimensional subspaces during evidence evaluation that transitioned at decision commitment, with significantly larger subspace changes in FOF than ADS. Together, these results demonstrate that FOF and ADS share common dynamical features during evidence evaluation but diverge near the time of decision commitment, with FOF exhibiting stronger state-transition dynamics.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.