ePPR: a new strategy for the characterization of sensory cells from input/output data.

A central goal of systems neuroscience is to characterize the transformation of sensory input to spiking output in single neurons. This problem is complicated by the large dimensionality of the inputs. To cope with this problem, previous methods have estimated simplified versions of a generic linear-nonlinear (LN) model and required, in most cases, stimuli with constrained statistics.

On the stability of interval type-2 TSK fuzzy logic control systems.

Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties. This paper proposes a novel inference mechanism for an interval type-2 Takagi-Sugeno-Kang fuzzy logic control system (IT2 TSK FLCS) when antecedents are type-2 fuzzy sets and consequents are crisp numbers (A2-C0). The proposed inference mechanism has a closed form which makes it more feasible to analyze the stability of this FLCS.

Estimating nonlinear receptive fields from natural images.

The response of visual cells is a nonlinear function of their stimuli. In addition, an increasing amount of evidence shows that visual cells are optimized to process natural images. Hence, finding good nonlinear models to characterize visual cells using natural stimuli is important.