Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the taskdependent features of the population response in a single figure.

Prefrontal persistent activity during the delay of spatial working memory tasks is thought to maintain spatial location in memory. A 'bump attractor' computational model can account for this physiology and its relationship to behavior. However, direct experimental evidence linking parameters of prefrontal firing to the memory report in individual trials is lacking, and, to date, no demonstration exists that bump attractor dynamics underlies spatial working memory. We analyzed monkey data and found model-derived predictive relationships between the variability of prefrontal activity in the delay and the fine details of recalled spatial location, as evident in trial-to-trial imprecise oculomotor responses. Our results support a diffusing bump representation for spatial working memory instantiated in persistent prefrontal activity. These findings reinforce persistent activity as a basis for spatial working memory, provide evidence for a continuous prefrontal representation of memorized space and offer experimental support for bump attractor dynamics mediating cognitive tasks in the cortex.

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.working memory | prefrontal cortex | population coding T he neuronal basis of working memory (WM) in prefrontal cortex (PFC) has been studied for decades through singleneuron recordings from monkeys performing tasks in which a transient sensory stimulus must be held in WM across a secondslong delay to guide a future response. These studies discovered that a key neural correlate of WM in PFC is stimulus-selective persistent activity, i.e., stable elevated firing rates in a subset of neurons, that spans the delay (1). These neurophysiological findings have grounded a leading hypothesis that WM is supported by stable persistent activity patterns in PFC that bridge the gap between stimulus and response epochs. Because the timescales of WM maintenance (several seconds) are longer than typical timescales of neuronal and synaptic integration (∼10-100 ms), mechanisms at the level of neural circuits may be critical for generating WM activity in PFC (2). A leading theoretical framework proposes that PFC circuits subserve WM maintenance through dynamical attractors, i.e., stable fixed points in network activity, generated by strong recurrent connectivity (3, 4).Recent neurophysiologi...

Working memory - the ability to maintain and manipulate information over a period of seconds - is a core component of higher cognitive functions. The storage capacity of working memory is limited but can be expanded by training, and evidence of the neural mechanisms underlying this effect is accumulating. Human imaging studies and neurophysiological recordings in non-human primates, together with computational modelling studies, reveal that training increases the activity of prefrontal neurons and the strength of connectivity in the prefrontal cortex and between the prefrontal and parietal cortex. Dopaminergic transmission could have a facilitatory role. These changes more generally inform us of the plasticity of higher cognitive functions.

A conspicuous feature of cortical organization is the wide diversity of inhibitory interneurons; their differential computational functions remain unclear. Here we propose a local cortical circuit in which three major subtypes of interneurons play distinct roles. In a model designed for spatial working memory, stimulus tuning of persistent activity arises from the concerted action of widespread inhibition mediated by perisoma-targeting (parvalbumin-containing) interneurons and localized disinhibition of pyramidal cells via interneurontargeting (calretinin-containing) interneurons. Moreover, resistance against distracting stimuli (a fundamental property of working memory) is dynamically controlled by dendrite-targeting (calbindin-containing) interneurons. The experimental observation of inverted tuning curves of monkey prefrontal neurons recorded during working memory supports a key model prediction. This work suggests a framework for understanding the division of labor and cooperation among different inhibitory cell types in a recurrent cortical circuit. S ynaptic inhibition is of paramount importance to cortical recurrent dynamics, sensory processing, and memory function. The complex inhibitory operation is likely to be accomplished by coordinated action of many subtypes of GABAergic (GABA, ␥-aminobutyric acid) interneurons present in the cortex. Recent years have witnessed a dramatic accumulation of our knowledge about these inhibitory cells, their morphology, physiology, chemical markers, synaptic connections, short-term plasticity, and molecular characteristics (1-6). On the other hand, little is known about specific computations by the diverse interneuron subtypes in animal behavior.To elucidate distinct operations performed by diverse interneurons, we have investigated a cortical microcircuit model that incorporates three interneuron subpopulations. Specifically, we report here a recurrent network model for working memory in the prefrontal cortex (PFC). PFC is a brain system critical to working memory, the ability to hold information actively in the mind for a short period of time (7,8). Understanding the cellular and circuit mechanisms of stimulus-selective persistent activity associated with working memory is a subject of intense current experimental and computational research (9, 10). More generally, persistent activity is believed to be a hallmark of strong recurrency in a cortical microcircuit, therefore modeling a working memory circuit represents a testbed for our investigation of cortical organization and functions. MethodsModel Architecture. The network model represents a local circuit of dorso-lateral prefrontal cortex in monkey. There are four cell populations: pyramidal (P) neurons and three subpopulations of inhibitory cells. Perisoma-targeting, dendrite-targeting, and interneuron-targeting interneurons are assumed to express parvalbumin (PV), calbindin (CB), and calretinin (CR) calcium-binding proteins, respectively (Fig. 1). P cells are four times more numerous than interneurons, and half of...