In this paper, we review several lines of recent work aimed at developing practical methods
for adaptive on-line stimulus generation for sensory neurophysiology. We consider various …

The stimulus-response relationship of many sensory neurons is nonlinear, but fully
quantifying this relationship by a complex nonlinear model may require too much data to be …

Abstract System identification is a growing approach to sensory neurophysiology that
facilitates the development of quantitative functional models of sensory processing. This …

Adaptively optimizing experiments can significantly reduce the number of trials needed to
characterize neural responses using parametric statistical models. However, the potential for …

Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the
time-varying activity of many individual neurons. A fundamental approach to understanding …

Adaptive stimulus design methods can potentially improve the efficiency of sensory
neurophysiology experiments significantly; however, designing optimal stimulus sequences …

We apply an adaptive approach to optimal experimental design in the context of estimating
the unknown parameters of a model of a neuron's response. We present an algorithm to …

Shaped by evolutionary processes, sensory systems often represent behaviorally relevant
stimuli with higher fidelity than other stimuli. The stimulus dependence of neural reliability …

The sensory-triggered activity of a neuron is typically characterized in terms of a tuning
curve, which describes the neuron's average response as a function of a parameter that …

Sensory systems extract behaviorally relevant information from a continuous stream of
complex high-dimensional input signals. Understanding the detailed dynamics and precise …