Evaluating the Accuracy of an Equivalent Linear Model in Predicting Peak Displacement of Seismic Isolation Systems using Single Friction Pendulum Bearings

This study evaluates the accuracy of an equivalent linear model in predicting peak nonlinear time-history displacement of seismic isolation systems with single friction pendulum bearings. To perform this evaluation, dynamic response of numerical models of 120 isolation systems subjected to 390 strong earthquake ground motions, including motions with pulse and motions without pulse, was analyzed and statistically processed. The results show that the equivalent linear model can partly predict the peak displacement of its counterpart nonlinear model. However, the equivalent model can also underestimate or overestimate the peak displacement. On average sense, the equivalent linear model underestimates small peak displacement and overestimates large peak displacement. It is also observed that the relationship between linear and nonlinear peak displacements depends on ground motion types. Based on the analysis data, equations representing relationship between linear and nonlinear peak displacements at different reliable levels for different ground motion types were proposed. These equations can be used in practice.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Equivalent Linear Model for Existing Soil-Structure Systems

The concept of equivalent linearization is extended for the soil-structure systems, in which the strength ratio (defined as the ratio of the yielding strength to the elastic strength demand) is known rather than the ductility ratio. The nonlinear soil-structure system is replaced by a linear single-degree-of-freedom (SDOF) system, which can capture the response of the actual system with sufficient accuracy. The dynamic characteristics of the equivalent linear SDOF system are determined through a statistical approach. The super-structure is modeled by an inelastic SDOF system with bilinear behavior, and the homogeneous half space beneath the structure by a discrete model, following the Cone Model. To cover a wide range of soil-structure systems, a comprehensive parametric study is conducted using a set of nondimensional parameters for the soil-structure system. The accuracy of the equivalent linear parameters is then assessed. The results confirm that the proposed equivalent linear model can capture the simultaneous effects of soil-structure interaction (SSI) and nonlinearity in the super-structure concerning the maximum inelastic response of the soil-structure system.

Dendrites enable a robust mechanism for neuronal stimulus selectivity

Hearing, vision, touch-underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of synapses or dendrites loss than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.