CNS*2026 Halifax

Introduction
Forecasting behavioral actions from neuronal circuit activity requires selecting an appropriate prediction horizon and history window for the model. This choice depends on the signal timescale, circuitry, target behaviors, and task structure. In dopaminoceptive striatal circuits, D1R signals may carry both fast action-related and slower reward-expectation components [1]. Modeling based on inhibitory D2R signals is equally important but more challenging, because suppression predictors relate to events less directly, interact with state-dependent effects, and may therefore yield weaker or less interpretable forecasts. In this study, we present an extension of a previously reported D1R forecasting hyperparameter search to D2R signal analysis.


Methods
Data came from 9 D1-Cre and 11 A2A-Cre mice with fiber-photometry recordings of GCaMP6 signal from the ventrolateral striatum during head-fixed anticipatory licking protocol [2]. Ca2+, isosbestic channels and licking were sampled at 20 Hz. Signals were detrended for photobleaching, low-pass filtered, artifact-corrected by robust regression of the control channel, and normalized [3]. Licks and lick bursts were extracted as prediction targets. Predictors used fixed-basis representations of signal history [4], with grid search over preset history windows (L) and forecast horizons (H). With binomial logistic GLM, leave-one-mouse-out testing compared dopamine-channel and isosbestic-only models using Precision@1%.


Results
Across leave-one-mouse-out testing, D1R signaling improved high-confidence forecasting beyond the isosbestic control in a timescale-dependent manner: for single licks, the strongest gain occurred at short windows (best at L = 0.5 s, H = 0.25 s; ΔP@1% = 0.148), whereas burst-start prediction peaked at an intermediate forecast horizon (H = 1 s), with history peaks at L = 1 s (ΔP@1% = 0.091) and L = 8 s (ΔP@1% = 0.098), consistent with both rapid event-related and slower schedule-aligned dynamics. D2R signaling showed its strongest advantage for burst events at longer history and wider forecast windows (L = 3 s, H = 3 s; ΔP@1% = 0.204) and for single licks at the shortest windows (L = 0.25 s, H = 0.25 s; ΔP@1% = 0.111).

Discussion
We extended a temporal hyperparameter search developed for D1R fiber-photometry signals to D2R signals to compare the optimal forecast horizon and history length for excitatory and inhibitory predictors. For single-lick events, both pathways showed similar dynamics: behavior was best predicted over short horizons from recent signal history. For state-dependent events such as licking bursts, the models diverged. D1R signals peaked at a 1 s horizon with 1 s of history, consistent with consummatory or anticipatory state changes. D2R signals were most informative at a 3 s horizon with a 3 s history window, reflecting distinct underlying mechanisms. These findings further indicate the importance of temporal parameter selection for analysis.


References
1. Kim, H. R., et al. (2020). A unified framework for dopamine signals across timescales. Cell, 183(6), 1600–1616. https://doi.org/10.1016/j.cell.2020.11.013
2. Toda, K., et al. (2017). Nigrotectal stimulation stops interval timing in mice. Current Biology, 27(24), 3763–3770. https://doi.org/10.1016/j.cub.2017.11.003
3. Keevers, L. J., & Jean-Richard-dit-Bressel, P. (2025). Obtaining artifact-corrected signals in fiber photometry via isosbestic signals, robust regression, and dF/F calculations. Neurophotonics, 12(2), 025003–025003. https://doi.org/10.1117/1.NPh.12.2.025003
4. Pillow, J. W., et al. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. Journal of Neuroscience, 25(47), 11003–11013. https://doi.org/10.1523/JNEUROSCI.3305-05.2005

Acknowledgement
The authors have no additional acknowledgments to report