Recurrent activity within microcircuits of macaque dorsolateral prefrontal cortex tracks cognitive flexibility

Human and non-human primate data clearly implicate the dorsolateral prefrontal cortex (dlPFC) as critical for advanced cognitive functions[1][1],[2][2]. It is thought that intracortical synaptic architectures within dlPFC are the integral neurobiological substrate that gives rise to these processes, including working memory, inferential reasoning, and decision-making[3][3]–[7][4]. In the prevailing model, each cortical column makes up one fundamental processing unit composed of dense intrinsic connectivity, conceptualized as the ‘canonical’ cortical microcircuit[3][3],[8][5]. Each cortical microcircuit receives sensory and cognitive information from a variety of sources which are represented by sustained activity within the microcircuit, referred to as persistent or recurrent activity[4][6],[9][7]. Via recurrent connections within the microcircuit, activity can propagate for a variable length of time, thereby allowing temporary storage and computations to occur locally before ultimately passing a transformed representation to a downstream output[4][6],[5][8],[10][9]. Competing theories regarding how microcircuit activity is coordinated have proven difficult to reconcile in vivo where intercortical and intracortical computations cannot be fully dissociated[5][8],[9][7],[11][10],[12][11]. Here, we interrogated the intrinsic features of isolated microcircuit networks using high-density calcium imaging of macaque dlPFC ex vivo . We found that spontaneous activity is intrinsically maintained by microcircuit architecture, persisting at a high rate in the absence of extrinsic connections. Further, using perisulcal stimulation to evoke persistent activity in deep layers, we found that activity propagates through stochastically assembled intracortical networks, creating predictable population-level events from largely non-overlapping ensembles. Microcircuit excitability covaried with individual cognitive performance, thus anchoring heuristic models of abstract cortical functions within quantifiable constraints imposed by the underlying synaptic architecture. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-7 [5]: #ref-8 [6]: #ref-4 [7]: #ref-9 [8]: #ref-5 [9]: #ref-10 [10]: #ref-11 [11]: #ref-12