We describe a dynamic data-driven methodology that is capable of simultaneously determining both the parameters of a constitutive model associated with the response of a composite material, and the optimum experimental design that leads to the corresponding material characterization. The optimum design of experiments may contain two parts. One involving the identification of the parameters that...

Sequential optimal design methods hold great promise for improving the efficiency of neurophysiology experiments. However, previous methods for optimal experimental design have incorporated only weak prior information about the underlying neural system (e.g., the sparseness or smoothness of the receptive field). Here we describe how to use stronger prior information, in the form of parametric m...

The goal of this paper is on the one hand to give a tutorial on the main ideas of a recently introduced paradigm for optimal experiment design whose objective is to design the least costly identification experiment while guaranteeing a sufficiently accurate model for e.g. control. On the other hand, the second goal is to illustrate with well chosen examples the advantages of designing optimally...

In this paper we present a framework for the design of experiments for sensor calibration. The theoretical ideas, which are based on the mathematical theory of optimal design of experiments, are demonstrated using a new software program, I-OPT, which was developed by Crary, Snow, and Hoo.' This program has been used to find I-optimal designs for both a silicon capacitive absolute-pressure senso...

the binary and ternary codes spanned by the rows of the point by block incidence matrices of some 2-designs and their complementary and orthogonal designs are studied. a new method is also introduced to study optimal codes.

We consider the problem of how to assign treatment in a randomized experiment, when the correlation among the outcomes is informed by a network available preintervention. Working within the potential outcome causal framework, we develop a class of models that posit such a correlation structure among the outcomes, and a strategy for allocating treatment optimally, for the goal of minimizing the ...

The optimum design of experiments for nonlinear models requires parameter sensitivities, that is the derivatives of the response with respect to the parameters. If the differential equations forming the kinetic model do not have an analytical solution, numerical derivatives have to be used. We describe the “direct” method for calculating the sensitivities and apply it to the design of experimen...

Neuroscience experiments often require training animals to perform tasks designed to elicit various sensory, cognitive, and motor behaviors. Training typically involves a series of gradual adjustments of stimulus conditions and rewards in order to bring about learning. However, training protocols are usually hand-designed, relying on a combination of intuition, guesswork, and trial-and-error, a...

With the advent of modern stimulation techniques in neuroscience, the opportunity arises to map neuron to neuron connectivity. In this work, we develop a method for efficiently inferring posterior distributions over synaptic strengths in neural microcircuits. The input to our algorithm is data from experiments in which action potentials from putative presynaptic neurons can be evoked while a su...