Abstract
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.
Highlights
Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections
We used an optimization algorithm that minimized the distance between Delay 1 and Delay 2 responses when projected into a reference subspace, while simultaneously maintaining memory information
In order to quantify the stability of the code in the subspace, we calculated the difference in decoding performance between decoders trained and tested in Delay 1 (LP11), and decoders trained in Delay 1 and tested in Delay 2 (LP12)
Summary
Maintenance of working memory is thought to involve the activity of prefrontal neuronal populations with strong recurrent connections. The stability extended to the distractor presentation period, which was not used in the optimization, and the stability was absent in error trials These results show that the LPFC retains behaviorally relevant time-invariant memory information despite exhibiting code-morphing. Network models with strong recurrent connection between neurons with similar tuning have been shown to replicate several properties of LPFC activity, including code stability[16,17,18,19]. It is not known whether these models can exhibit codemorphing while retaining a subspace with time-invariant memory information. These results suggest that nonmemory inputs to the LPFC may be a critical component of codemorphing
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