CNS*2026 Halifax

Introduction
Understanding how cortical oscillations coordinate spatial memory and motor planning is a central challenge in systems neuroscience. We tested whether phase–amplitude dynamics in cortical local field potentials (LFPs) encode distributed versus region-specific signals for spatial memory and planning under varying visuospatial conditions.


Methods
We developed a Complex-Valued Neural Network (CVNN) model [1, 2] to decode landmark-dependent spatial states from LFPs recorded in the posterior ventrolateral prefrontal cortex (pVLPFC, 128 channels) and intraparietal sulcus (IPS, 32 channels) of a female rhesus monkey performing memory-guided reaching tasks in which visual landmarks were stable, shifted 8° in one of eight directions, or absent [3, 4]. Preprocessed LFPs were transformed into complex-valued time series using the Hilbert transform to preserve phase and amplitude information [5].


Results
We trained separate CVNN models on IPS or pVLPFC signals which classified the three landmark conditions with >90% training accuracy and more than 51% overall validation accuracy, significantly above chance (33%). However, validation performance revealed inter-regional specialization: the IPS model performed best for no-landmark trials (88.35% ± 6.99), whereas the pVLPFC model showed superior performance for shifted-landmark trials (71.73% ± 8.59). We then trained dual-stream models combining pVLPFC and IPS recordings. The single-region results were confirmed via region occlusion analysis after training: removing pVLPFC improved no-landmark classification, while removing IPS improved shifted-landmark classification.


Discussion
These findings suggest that IPS specializes in maintaining spatial representations for reach plans in egocentric coordinates, whereas pVLPFC shows enhanced encoding in the presence of visual landmarks, especially in the dynamic landmark-shift conditions, indicating complementary computational roles in maintaining and updating spatial representations for reach.


References

1. N. Benvenuto and F. Piazza, "On the complex backpropagation algorithm," in IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 967-969, April 1992, doi: 10.1109/78.127967.
   
2. G. M. Georgiou and C. Koutsougeras, "Complex domain backpropagation," in IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 39, no. 5, pp. 330-334, May 1992, doi: 10.1109/82.142037.
3. Lin, J., Wang, H., Sun, S., Yan, X., & Crawford, J. D. (2023). Influence of a visual landmark shift on memory-guided reaching in monkeys. Journal of Vision, 23(9), 4828–482 https://doi.org/10.1167/jov.23.9.4828.
4. Lin, J. Y. X. (2024). Influence of a visual landmark shift on memory-guided reaching in the monkey.
5. Freeman, W. J. (2007). Hilbert transform for brain waves. Scholarpedia, 2(1), 1338.

Acknowledgement
This research was funded by the Connected Minds Program, supported by the Canada First Research Excellence Fund.