Neural mechanisms balancing accuracy and flexibility in working memory and decision tasks

Abstract

The living system follows the principles of physics, yet distinctive features, such as adaptability, differentiate it from conventional systems. The cognitive functions of decision-making (DM) and working memory (WM) are crucial for animal adaptation, but the underlying mechanisms are still unclear. To explore the mechanism underlying DM and WM functions, here we applied a general non-equilibrium landscape and flux approach to a biophysically based model that can perform decision-making and working memory functions. Our findings reveal that DM accuracy improved with stronger resting states in the circuit architecture with selective inhibition. However, the robustness of working memory against distractors was weakened. To address this, an additional non-selective input during the delay period of decision-making tasks was proposed as a mechanism to gate distractors with minimal increase in thermodynamic cost. This temporal gating mechanism, combined with the selective-inhibition circuit architecture, supports a dynamical modulation that emphasizes the robustness or flexibility to incoming stimuli in working memory tasks according to the cognitive task demands. Our approach offers a quantitative framework to uncover mechanisms underlying cognitive functions grounded in non-equilibrium physics.

Fig. 1. Selective subnetwork of inhibitory neurons enhances the accuracy in decision-making.

a The diagram of the circuit model with non-selective inhibitory neurons. b The circuit architecture with a selective subnetwork of inhibitory neurons. The control parameter w can indicate the degree of selectivity of the inhibitory neurons. w and w₋ modulate the strength of connections between the excitatory and inhibitory populations. w increases the strength of connections between two sub-populations with the same subscript relative to cross-connections. w₋ decreases with increasing w, modeling the homeostatic depression of connections between clusters with the different subscripts (1,2). Larger w implies the inhibitory neurons more selective (see more details in the Methods section). c, d Neural activities during DM for different circuit architectures with non-selective and selective inhibitory neurons, where w = 1, 1.06, respectively. The blue curves represent the activities of the excitatory neural population 1 for correct trials, while the red curves for the error trials. The black trajectories mark the firing rate of population 1 for the trial, with median decision time. e, f The average decision times and correct rates in DM with varied architectures indicated by the parameter w. For the generality of the results, we explored the DM tasks with different difficulties (stimulus motion coherence c = 0.1, 0.2, 0.5, respectively). Each data point in Fig. 1e, f are averaged over 30,000 trials. The circuit architecture with a selective subnetwork of inhibitory neurons (larger w) results in longer decision time, with variability across trials represented by error bars (SEM). In addition, the accuracy in DM increases with w. The underlying mechanism can be reflected from the attractor landscapes showing that the selective-inhibition architecture leads to a stronger resting state, which extends the time of integration of evidences ((g–i) with w = 1.01, 1.04, 1.06, respectively and the nonzero-motion coherence c = 0.1).

Fig. 2. 3D parameter relationships and the threshold curve.

Three-dimensional visualization of decision accuracy as a function of selective inhibition w and stimulus strength c. The surface highlights parameter regimes where selective inhibition w and stimulus strength c synergistically optimize accuracy. Critical w values (red curve) at which decision accuracy reaches 100% for varying coherence c. Higher c reduces the required w, demonstrating that stronger sensory evidence compensates for weaker selective inhibition.

Fig. 3. The robustness of WM against distractors.

a The average transition time from the correct choice to the error one in the presence of distractors for different circuit architectures. The system has to go across a potential barrier to switch to another decision state. b The barrier heights (bh) inferred from the underlying landscape topography can measure the robustness of the decision state in WM. Both the average transition time and the corresponding barrier heights are reduced as the w is decreased, which implies less robustness against distractors.

Fig. 4. An increasing non-selective input can enhance the robustness of WM against distractors.

a The average transition time from the correct choice to the error one in the presence of distractors increases with increased non-selective input to both selective excitatory neural populations. The ΔI₀ here represents the magnitude of the additional non-selective input. b The transition rate in a limited duration of the distracting stimulus (1 s) is reduced as the non-selective input is increased. The mechanism of improved robustness is reflected in the larger barrier height (bh) inferred from the underlying landscape topography (c). The detailed attractor landscapes with increased non-selective input are displayed in (d–f), where the non-selective input I₀ = 0, 0.004, 0.008 nA, respectively, with the specific nonzero-motion coherence c = 0.2 and w = 1.

Fig. 5. The disadvantages of strong non-selective input in DM and WM.

Although the increased non-selective input can improve the robustness of WM against distractors. Larger thermodynamic costs are needed to support the enhanced robustness against distractors during the delay period (a). The ΔI₀ here represents the magnitude of the additional non-selective input. It leads to shorter decision time (b) while making less accurate choices (c), and the initial errors are less likely to be corrected (d). Presenting A ramping input during the delay period may serve as a cost-effective mechanism of temporal gating of distractors.