Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles.

Abstract

Neurons in the primate lateral prefrontal cortex (LPFC) encode working memory (WM) representations via sustained firing, a phenomenon hypothesized to arise from recurrent dynamics within ensembles of interconnected neurons. Here, we tested this hypothesis by using microelectrode arrays to examine spike count correlations (<i>r</i>_<i>sc</i> ) in LPFC neuronal ensembles during a spatial WM task. We found a pattern of pairwise <i>r</i>_<i>sc</i> during WM maintenance indicative of stronger coupling between similarly tuned neurons and increased inhibition between dissimilarly tuned neurons. We then used a linear decoder to quantify the effects of the high-dimensional <i>r</i>_<i>sc</i> structure on information coding in the neuronal ensembles. We found that the <i>r</i>_<i>sc</i> structure could facilitate or impair coding, depending on the size of the ensemble and tuning properties of its constituent neurons. A simple optimization procedure demonstrated that near-maximum decoding performance could be achieved using a relatively small number of neurons. These WM-optimized subensembles were more signal correlation (<i>r</i>_{<i>signal</i>} )-diverse and anatomically dispersed than predicted by the statistics of the full recorded population of neurons, and they often contained neurons that were poorly WM-selective, yet enhanced coding fidelity by shaping the ensemble's <i>r</i>_<i>sc</i> structure. We observed a pattern of <i>r</i>_<i>sc</i> between LPFC neurons indicative of recurrent dynamics as a mechanism for WM-related activity and that the <i>r</i>_<i>sc</i> structure can increase the fidelity of WM representations. Thus, WM coding in LPFC neuronal ensembles arises from a complex synergy between single neuron coding properties and multidimensional, ensemble-level phenomena.