Xiao-Jing Wang on working memory and prefrontal cortex

Why is the ability to hold something in mind, even briefly, the gateway to flexible cognition? Xiao-Jing Wang explains how attractor dynamics and slow synaptic reverberation in prefrontal cortex give rise to both working memory and decision-making. Subscribe for more from the Convergent Science Network podcast series. Xiao-Jing Wang begins with a deceptively simple argument: without the capacity to maintain information in the absence of direct sensory input, an organism is enslaved to its environment, reduced to reflexive responses. Working memory , sustained neural activity that bridges the gap between stimulus and action , is therefore the foundation of cognitive flexibility. Drawing on decades of lesion studies, single-neuron recordings, and computational modeling, Wang makes the case that prefrontal cortex is uniquely equipped for this role, thanks to its dense recurrent excitatory connections and distinctive neuromodulatory environment. The interview dives deep into the mechanics of attractor networks, which Wang uses as the theoretical framework for understanding prefrontal dynamics. He is careful to demystify the concept: attractor states are simply relatively stable states, not rigid black holes. What makes prefrontal cortex special is not persistence per se, even oculomotor circuits show persistent activity, but the capacity to maintain multiple stable states simultaneously and switch between them with brief inputs. This multiplicity is what a working memory system requires, and it emerges naturally from the nonlinear dynamics of strongly recurrent circuits. A key surprise from Wang's modeling work is that the reverberation sustaining working memory must be slow, mediated primarily by NMDA receptors rather than fast AMPA transmission. This was not a design choice but a computational necessity: fast positive feedback makes the network explosively unstable, while slow reverberation provides both stable memory states and the gradual ramping activity observed during decision-making. The same circuit architecture that holds items in working memory also integrates evidence over time, producing the reaction-time signatures seen in prefrontal recordings during perceptual decision tasks. Wang also addresses the frontier challenges: extending local circuit models to large-scale brain systems, understanding how mixed selectivity in prefrontal neurons supports combinatorial coding of sensory, rule, and motor information, and reconciling the role of neural oscillations and correlations with the stochastic firing of individual neurons. His vision is one of building blocks , elementary computational mechanisms that can be composed into increasingly realistic models of cognition.