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— We present an approach for overcoming computational errors at run time that originate from static hardware faults in digital processors. The approach is based on embedded machine-learning stages that learn and model the statistics of the computational outputs in the presence of errors, resulting in an error-aware model for embedded analysis. We(More)
Clinical and preclinical evidence suggests anxiolytic-like efficacy of pregabalin (PGB, Lyrica). However, its mechanism of action remains under investigation. The current study applied [(14)C]-iodoantipyrine cerebral blood flow (CBF) mapping to examine the effect of PGB on neural substrates underlying unconditioned fear in a rat model of footshock-induced(More)
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active(More)
This paper is a brief survey on the existing problems and challenges inherent in model-based control (MBC) theory, and some important issues in the analysis and design of data-driven control (DDC) methods are here reviewed and addressed. The necessity of data-driven control is discussed from the aspects of the history, the present, and the future of control(More)
In this work we study how the stimulus distribution influences the optimal coding of an individual neuron. Closed-form solutions to the optimal sigmoidal tuning curve are provided for a neuron obeying Poisson statistics under a given stimulus distribution. We consider a variety of optimality criteria, including maximizing discriminability, maximizing mutual(More)
Technological scaling and system-complexity scaling have dramatically increased the prevalence of hardware faults, to the point where traditional approaches based on design margining are becoming in-viable. The challenges are exacerbated in embedded sensing applications due to the severe energy constraints. Given the importance of classification functions(More)