A neurocomputational theory of the dopaminergic modulation of working memory functions

Abstract

The dopaminergic modulation of neural activity in the prefrontal cortex (PFC) is essential for working memory. Delay-activity in the PFC in working memory tasks persists even if interfering stimuli intervene between the presentation of the sample and the target stimulus. Here, the hypothesis is put forward that the functional role of dopamine in working memory processing is to stabilize active neural representations in the PFC network and thereby to protect goal-related delay-activity against interfering stimuli. To test this hypothesis, we examined the reported dopamine-induced changes in several biophysical properties of PFC neurons to determine whether they could fulfill this function. An attractor network model consisting of model neurons was devised in which the empirically observed effects of dopamine on synaptic and voltage-gated membrane conductances could be represented in a biophysically realistic manner. In the model, the dopamine-induced enhancement of the persistent Na+ and reduction of the slowly inactivating K+ current increased firing of the delay-active neurons, thereby increasing inhibitory feedback and thus reducing activity of the "background" neurons. Furthermore, the dopamine-induced reduction of EPSP sizes and a dendritic Ca2+ current diminished the impact of intervening stimuli on current network activity. In this manner, dopaminergic effects indeed acted to stabilize current delay-activity. Working memory deficits observed after supranormal D1-receptor stimulation could also be explained within this framework. Thus, the model offers a mechanistic explanation for the behavioral deficits observed after blockade or after supranormal stimulation of dopamine receptors in the PFC and, in addition, makes some specific empirical predictions.

Fig. 1.

Structure of the PFC network model. Within the PFC network, excitatory neurons (representing deep-layer pyramidal cells) are interconnected by excitatory synapses on their proximal compartments (prox) while receiving afferent input from other cortices on their distal compartments (dis). An inhibitory unit (INH) provides feedback inhibition. A “striatal” motor unit (MOTOR) receives excitatory input from all PFC “pyramidal cells” and inhibits the “mesencephalic” DA unit, which modulates parameters of PFC neurons and synapses in an activity-dependent manner.

Fig. 2.

Activity of the PFC network under conditions of normal (+DA) and reduced (−DA) DA unit output during two successive trials of a DMS task with intervening stimuli. A, Mean proximal membrane potential (top parts) of all PFC net units belonging to the pattern “4,” which is the TP in the first trial (TP1), and pattern “0,” which is the TP in the second trial (TP2).Gray bars indicate the time intervals during which stimuli were presented. Three intervening stimuli (IS1–IS3 andIS4–IS6, respectively) were presented between the first (sample) and the second (matching) presentation of a TP. Note thatIS1 = IS2 = TP2 andIS4 = IS5 = TP1. Bottom parts give the gray level-coded membrane potential of all 100 PFC network units at discrete time points during presentation of the stimuli in the first trial. Lighter gray levels indicate higher activity. With normal DA output, TPs stay stable during a whole trial (+DA), whereas intervening patterns override currently active patterns under conditions of reduced DA unit output (−DA). I_aff = 0.55.B, Membrane potential of the motor unit under the+DA and the −DA conditions. C, Mean firing frenquency of the inhibitory unit under the +DA and the −DA condition.

Fig. 3.

Effects of DA-induced variations inI_NaP parameters on the stability of TPs. Stability is measured in terms of the afferent input needed to disrupt current neural representations (I_aff,crit).Dashed vertical lines indicate the range within which the respective parameters or variables during full network simulations varied. All other network parameters had the baseline values given in Table 1. OVL, Overlap; CON, connectivity.A, Reduction of the I_NaP activation threshold (α_NaP) increases the stability of the active neural representation at different overlaps (CON= 25%). B, Reduction of the I_NaPactivation threshold (α_NaP) increases the stability of the active neural representation at different connectivities (OVL = 15%). C, Dependence of the stability of an active representation on the amplitude of a constant (i.e., non-voltage-dependent) Na⁺ current at different connectivities (OVL = 15%). D, Dependence of the stability of an active representation on the effective I_NaP amplitude (I_NaP,eff), compared for the constantI_NaP (labeled I_NaP,cons), for I_NaP,max variation of the voltage-dependentI_NaP (labeled I_NaP,max), and for α_NaP variation of the voltage-dependentI_NaP (labeled α_NaP).OVL = 15%; CON = 25%.

Fig. 4.

Effects of DA-induced variations inI_KS,max on the stability of TPs (as measured byI_aff,crit). Dashed vertical linesindicate the parameter range within whichI_KS,max during full network simulations varied. All other network parameters had the baseline values given in Table 1.OVL, Overlap; CON, connectivity. A, Reduction of I_KS,max leads to higher stability for different overlaps, more pronounced at low overlaps (CON = 25%). B, Reduction ofI_KS,max leads to higher stability for different connectivities, and this trend is more pronounced at low connectivities (OVL = 15%).

Fig. 5.

Effects of DA-induced variations in the general excitatory synaptic efficiency (η_exc) on the stability of TPs (as measured by I_aff,crit).Dashed vertical lines indicate the parameter range within which η_exc during full network simulations varied.OVL, Overlap; CON, connectivity. A, With all other network parameters being in a low (baseline) DA configuration, reduction of η_exc has only a slightly stabilizing effect at different overlaps (CON = 25%).B, With all other network parameters in a high DA configuration, reduction of η_exc has a >10-fold greater stabilizing effect (CON = 25%). C, In a high DA configuration, reduction of η_exc has a stabilizing effect at all except very high connectivities (OVL = 15%). D, The effect of a variation in η_exc is more pronounced at low than at high, except very low I_NaP activation thresholds (α_NaP) (OVL = 15%;CON = 25%). E, The effect of a variation in η_exc is more pronounced at low values ofI_KS,max (OVL = 15%;CON = 25%). F, In the high DA configuration, a reduction of η_exc only for PFCinternal excitatory synapses but not for afferent synapses has a slightly stabilizing effect for high but not for low degrees of overlap (CON = 25%).

Fig. 6.

Effects of DA-induced variations in a dendritic “HVA Ca²⁺ channel.” All other network parameters had the baseline values given in Table 1. OVL, Overlap;CON, connectivity. A, Decreasing the coupling strength (λ_pd) between the proximal and the distal “pyramidal cell” compartment diminishes the impact of intervening patterns on current network activity (as measured byI_aff,crit). This tendency is much more pronounced for short stimulus presentation times (Δt_stim). OVL = 15%;CON = 25%. Dashed vertical lines indicate the parameter range within which λ_pd during full network simulations varied. B, Reduction of the amplitude (I_HVA,max) of an explicitly modeled dendritic HVA Ca²⁺ channel has a stabilizing effect within a reasonable parameter range (I_HVA,max < 0.17) at different overlaps (CON = 25%). C, Reduction of the amplitude (I_HVA,max) of an explicitly modeled dendritic HVA Ca²⁺ channel has a stabilizing effect within a reasonable parameter range at different connectivities (OVL = 15%).

Fig. 7.

Effects of DA-induced variations in the inhibitory synaptic efficiency (η_inh) on the stability of TPs (as measured by I_aff,crit). Dashed vertical lines indicate the parameter range within which η_inh during full network simulations varied. All other network parameters had the baseline values given in Table 1.OVL, Overlap; CON, connectivity. A, Within the parameter range used during full network simulations, increase of η_inh has a mildly destabilizing effect for different overlaps (CON = 25%). B, Same asA for different connectivities (OVL = 15%).C, Simulation of a DMS task with the full network shows too high pyramidal cell activity peaks eventually followed by a collapse of the TP when η_inh (1.11) is kept constant.

Fig. 8.

Activity of the PFC network under conditions of supranormal DA unit output (++DA) during two successive trials of a DMS task with intervening stimuli. A, Mean proximal membrane potential of all PFC net units belonging to the pattern “4” (=TP1; see Fig. 2) and pattern “0” (=TP2; see Fig. 2). Gray bars indicate the time intervals during which stimuli were presented. Three intervening stimuli were presented during each trial as described in Figure 2. The TPs are disrupted by intervening stimuli. I_aff = 0.55. B, Membrane potential of the motor unit, showing many premature motor responses.

Fig. 9.

The crossings of the logarithmic function with the straight lines give the stable membrane potential of the TP units for conditions of lowI_NaP,cons (=0.04), highI_NaP,cons (=0.09), and high connectivity (I_NaP,cons = 0.04) where the non-TP units become active above threshold.