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

Abstract

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...

Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory

“…Experiments in brain slices and in vivo provide supporting evidence for models of dynamical neural activities, in which certain combinations of firing rates nevertheless remain constant (Li et al 2016, Murray et al 2016, Zylberberg et al 2016b). Of particular note is the recent work by Murray et al (2016), who compared neural recordings from monkey PFC during the delay period of both a spatial WM task and a parametric WM task to the predictions of several different computational models.…”

“…This is the same tuning requirement displayed by the original line attractor networks (Seung 1996). ] Similar models have been studied elsewhere in the context of WM and neural integrators (Boerlin et al 2013, Murray et al 2016, Schwemmer et al 2015). …”

“…Of particular note is the recent work by Murray et al (2016), who compared neural recordings from monkey PFC during the delay period of both a spatial WM task and a parametric WM task to the predictions of several different computational models. Of the models considered, the constant combination of firing rates model (Druckmann & Chklovksii 2012) was found to be most consistent with the electrophysiology observations.…”

The Prefrontal Cortex: A Basic Embryological, Histological, Anatomical, and Functional Guideline

“…Another function of PFC is the elaboration of thoughts, that is, developing depth and abstractness to the information [3,4,28].…”

“…PFC has key roles in defining personality and behavior. It also has many functions including maintaining attention, planning complex movements, controlling emotions, discriminating between good and bad, speech, memory, temporal perception and working memory [1][2][3][4][5][6][7][8][9].…”