In this paper, a neural mass perceptual decision making model introduced by Piskała et al. is analyzed. The model describes activity of two neuron populations influenced by each other and external inputs. The groups' activities correspond to the process of making a perceptual binary decision. Existing results are generalized by investigating the impact of both a delay in self-inhibition and a generic Hill coefficient on solutions to the system of differential equations. Several versions of the model with various assumptions are compared using analytical and numerical methods.
Sensitivity analysis plays a crucial role in multiobjective linear programming (MOLP), where understanding the impact of parameter changes on efficient solutions is essential. This work builds upon and extends previous investigations. In this paper, we introduce a novel approach to sensitivity analysis in MOLP, designed to be computationally feasible for decision-makers studying the behavior of efficient solutions under perturbations of objective function coefficients in a two-dimensional variable space. This approach classifies all MOLP problems in S ⊂ℝ^2 by defining an equivalence relation that partitions the space of linear maps-comprising all sequences of linear forms on ℝ^2 of length K ≥ 2-into a finite number of equivalence classes. Each equivalence class is associated with a unique subset of the boundary of S. For any MOLP with K objective functions belonging to the same equivalence class, its set of efficient solutions corresponds to the associated subset of the boundary of S. This approach is detailed and illustrated with a numerical example.
