Mechanism for top-down control of working memory capacity

Abstract

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding top-down control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.

Fig. 1.

Computational model of multiitem working memory has limited capacity. (A) (Left) Network with E cells and I cells. Nearby cells are strongly connected (strength indicated by thickness of connections). (Right) E→E cell (light) and E→I or I→E cell (dark) connection strength is a function of the distance in preferred angle between pre- and postsynaptic cells. (B) (Left) Simulation of task with four stimuli. Each dot represents an E cell action potential. Cue presentation (thick horizontal bar): 0–0.5 s. (Right) Delay-phase firing rate (black). (C) Mean network rates for three networks with capacity 1 (●), 2 (▲), and 3 (▼). (D) Firing rate of memory-storing E cells and net current entering nonstoring cells. This current is a measure of the influence from distant bumps. Horizontal line indicates approximate level of current below which memories become unstable. Because memory load is an integer number, the curves do not end at exactly the same level, and the threshold cannot be determined numerically. (E) Number of encoded objects (■) and delay-phase fMRI activity calculated from spikes (▶) and synaptic activity (◀) from a simulated population of 8 virtual test participants. There were 10 simulations per data point in C and D and per data point of each virtual participant in E.

Fig. 2.

Mechanism behind vsWM capacity. (A) Solution of Eq. 1 for p items. Output activity r feeds back through recurrent connections and produces synaptic input I(r), which leads to new output f(I). Iterating from different starting points (r₁ or r₂) stabilizes activity at either of two fixed points (upper dot indicates successful retention; lower dot, memory loss), provided I(r) and f(I) overlap in three positions (dots). The effective connection strength, ΔI/Δr, is determined by local excitation (G⁺) and lateral inhibition (G⁻) from the p − 1 adjacent memories. (B) Potential energy landscape representation for p = 1. Memory populations (balls) seek minimum-energy states, but fluctuations can push activity uphill. (C) Memory stability changes with G⁺, I_X, or G⁻. Dashed lines, ΔI_X = ±0.9. (D) Solution of capacity equation. As p increases, ΔI/Δr decreases. When I(r) and f(I) cease to overlap, capacity is reached. (E) Energy landscape visualization of D. Memory rate (wedges) and stability decrease with load. Above capacity, the memory well disappears and reforms only if sufficient items are forgotten. (F) For p well above capacity, the energy slope is so steep that many items disappear before the landscape restabilizes in a memory state with fewer memories (p = 2) than capacity (p = 3). Supracapacity storage thus decreases with load.

Fig. 3.

Boosting of capacity through dlPFC top-down signals. dlPFC has nonspecific, excitatory connections to IPS. (A) If dlPFC has low activity, only two items are stored. (B) When dlPFC activity is high, all four items are remembered. (C and D) Capacity increases with increasing dlPFC input because of increased connection strength or dlPFC activity. (E) Relationship between vsWM load and stored items for two different values of I_X. (C–E) Ten simulations per data point.

Fig. 4.

fMRI experiment confirms model predictions. (A) vsWM task with 3 or 5 stimuli. After an instruction to perform the task, stimuli were presented for 1 s, and a delay of 2, 3, or 4 s followed. After the delay, the participants had to indicate whether a probe was in the position of a stimulus or not. (B) The frontal brain areas that activated significantly either in the M5–C5 or M3–C3 contrast conjunction (Table S1) were dlPFC (a), SFG (b), IFG (c), and mSFG (d). Based on a priori knowledge, we identified clusters in the visual cortex (e) and the load-dependent IPS area (f). (C) Correlation between the M5–M3 activity contrast in dlPFC and IPS (n = 25). (D) Correlation between M5–M3 performance drop and dlPFC activity (n = 21).