The activity of neurons in the primate lateral prefrontal cortex (LPFC) is strongly modulated by visual attention. Such a modulation has mostly been documented by averaging the activity of independently recorded neurons over repeated experimental trials. However, in realistic settings, ensembles of simultaneously active LPFC neurons must generate attentional signals on a single-trial basis, despite the individual and correlated variability of neuronal responses. Whether, under these circumstances, the LPFC can reliably generate attentional signals is unclear. Here, we show that the simultaneous activity of neuronal ensembles in the primate LPFC can be reliably decoded to predict the allocation of attention on a single-trial basis. Decoding was sensitive to the noise correlation structure of the ensembles. Additionally, it was resilient to distractors, predictive of behavior, and stable over weeks. Thus, LPFC neuronal ensemble activity can reliably encode attention within behavioral time frames, despite the noisy and correlated nature of neuronal activity.

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 (r sc ) in LPFC neuronal ensembles during a spatial WM task. We found a pattern of pairwise r sc 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 r sc structure on information coding in the neuronal ensembles. We found that the r sc 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 WMoptimized subensembles were more signal correlation (r signal )-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 r sc structure. We observed a pattern of r sc between LPFC neurons indicative of recurrent dynamics as a mechanism for WM-related activity and that the r sc 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, ensemblelevel phenomena.working memory | prefrontal cortex | noise correlations | macaque | decoding T o interact with a complex, dynamic environment, organisms must be capable of maintaining and manipulating information that is no longer available to their sensory systems. This capability, when applied transiently (i.e., for milliseconds to seconds), is referred to as working memory (WM) (1)-a hallmark of intelligence and a crucial component of goal-directed behavior (2). In 1949, Hebb postulated that sustained neuronal activity in the absence of stimulus input could serve as the neural substrate for WM (3). Fuster and Alexander later discovered neurons in the lateral prefrontal cortex (LPFC) of monkeys that exhibited sustained firing during WM tasks (4). Subsequent neurophysiological studies have corroborated that neuronal activity in the LPFC and other regions can represent WM for visual-mnemonic space (5-7), as well as nonspatial visual features (8-10).Electrophysiological studies of spatial WM have traditionally relied on recording from one neutron or a few neurons simultaneously (10). However, the neuronal computations that underlie sophisticated behaviors such as WM require the coordinated activity of many neurons within and across brain networks (11). We currently lack a clear understanding of how single neuron coding properties scale to neuronal ensembles. Can the proper...

Ketamine is a dissociative anesthetic drug, which has more recently emerged as a rapid-acting antidepressant. When acutely administered at subanesthetic doses, ketamine causes cognitive deficits like those observed in patients with schizophrenia, including impaired working memory. Although these effects have been linked to ketamine’s action as an N-methyl-D-aspartate receptor antagonist, it is unclear how synaptic alterations translate into changes in brain microcircuit function that ultimately influence cognition. Here, we administered ketamine to rhesus monkeys during a spatial working memory task set in a naturalistic virtual environment. Ketamine induced transient working memory deficits while sparing perceptual and motor skills. Working memory deficits were accompanied by decreased responses of fast spiking inhibitory interneurons and increased responses of broad spiking excitatory neurons in the lateral prefrontal cortex. This translated into a decrease in neuronal tuning and information encoded by neuronal populations about remembered locations. Our results demonstrate that ketamine differentially affects neuronal types in the neocortex; thus, it perturbs the excitation inhibition balance within prefrontal microcircuits and ultimately leads to selective working memory deficits.