Time-invariant working memory representations in the presence of code-morphing in the lateral prefrontal cortex

• Published in: Nature communications 2019-11, Vol.10 (1), p.4995-11, Article 4995
• Main Authors: Parthasarathy, Aishwarya, Tang, Cheng, Herikstad, Roger, Cheong, Loong Fah, Yen, Shih-Cheng, Libedinsky, Camilo
• Format: Article
• Language: English
• Subjects: 631/378; 631/378/116; Algorithms; Animal cognition; Animals; Humanities and Social Sciences; Invariants; Macaca fascicularis - physiology; Maintenance; Male; Memory, Short-Term - physiology; Models, Neurological; Monkeys & apes; Morphing; multidisciplinary; Neurons - physiology; Optimization; Prefrontal cortex; Prefrontal Cortex - cytology; Prefrontal Cortex - physiology; Science; Science (multidisciplinary); Short term memory; Subspaces; Time Factors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-12841-y

Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. However, it was recently shown that distractors evoke a morphing of the prefrontal population code, even when memories are maintained throughout the delay. How can a morphing code maintain time-invariant memory information? We hypothesized that dynamic prefrontal activity contains time-invariant memory information within a subspace of neural activity. Using an optimization algorithm, we found a low-dimensional subspace that contains time-invariant memory information. This information was reduced in trials where the animals made errors in the task, and was also found in periods of the trial not used to find the subspace. A bump attractor model replicated these properties, and provided predictions that were confirmed in the neural data. Our results suggest that the high-dimensional responses of prefrontal cortex contain subspaces where different types of information can be simultaneously encoded with minimal interference. Working memory is maintained in the recurrent connectivity of prefrontal neurons; however, distractors lead to a morphing of the population code. Here, the authors show that a low dimensional subspace of activity maintains memory information even with a distractor and can be modeled as a bump attractor.