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Proc Natl Acad Sci U S A. 2017 Jan 10;114(2):394-399. doi: 10.1073/pnas.1619449114. Epub 2016 Dec 27.

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex.

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Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510.
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC 27157.
Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico D.F., Mexico;
El Colegio Nacional, 06020 Mexico D.F., Mexico.
Center for Neural Science, New York University, New York, NY 10012;
New York University-East China Normal University Institute of Brain and Cognitive Science, NYU-Shanghai, Shanghai 200122, China.


Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.


population coding; prefrontal cortex; working memory

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