Spike train regularity of the noisy neural auditory system model under the influence of two sinusoidal signals with different frequencies is investigated. For the increasing ratio m/n of the input signal frequencies (m, n are natural numbers) the linear growth of the regularity is found at the fixed difference (m - n). It is shown that the spike train regularity in the model is high for harmoni...

Neurons integrate inputs from thousands of afferents. Similarly, some experimental techniques record the pooled activity of large populations of cells. When cells in these populations are correlated, the correlation coefficient between the collective activity of two subpopulations is typically much larger than the correlation coefficient between individual cells: The act of pooling individual c...

This paper applies the cognitive hierarchy model of Camerer, Ho and Chong (2004) to the action commitment game of Hamilton and Slutsky (1990). The model generates the heterogeneity of behavior reported in Huck, Müeller and Normann (2002). The model predicts the spike in the leadership quantity in the first period as well as the spike in the Cournot quantity in the second period. The model predi...

Recognition of nonlinearities in the neuronal encoding of repetitive spike trains has generated a number of models to explain this behavior. Here we develop the mathematics and a set of tests for two such models: the leaky integrator and the variable-gamma model. Both of these are nearly sufficient to explain the dynamic behavior of a number of repetitively firing, sensory neurons. Model parame...

We examine the effect of the phase-resetting curve on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy superthreshold stimulation. We use linear-response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem and contrast the results for type I vs type II models and across ...

We present a kinetic theory for all-to-all coupled networks of identical, linear, integrate-and-fire, excitatory point neurons in which a fast and a slow excitatory conductance are driven by the same spike train in the presence of synaptic failure. The maximal-entropy principle guides us in deriving a set of three (1+1) -dimensional kinetic moment equations from a Boltzmann-like equation descri...

The spike detection problem is cast into a delay estimation. Using elementary operational calculus, we obtain an explicit characterization of the spike locations, in terms of short time window iterated integrals of the noisy signal. From this characterization, we derive a joint spike detection and localization system where the decision function is implemented as the output of a digital Volterra...

We develop a method from semiparametric statistics (Cox, 1972) for the purpose of tracking links and connection strengths over time in a neuronal network from spike train data. We consider application of the method as implemented in Masud and Borisyuk (2011), and evaluate its use on data generated independently of the Cox model hypothesis, in particular from a spiking model of Izhikevich in fou...

Covariations in neuronal latency or excitability can lead to peaks in spike train covariograms that may be very similar to those caused by spike timing synchronization (see companion article). Two quantitative methods are described here. The first is a method to estimate the excitability component of a covariogram, based on trial-by-trial estimates of excitability. Once estimated, this componen...

In this paper we analyze the response of the electric power grid to a hypothetical nuclear attack inside a major city in the US. We assume that the protective devices of the power system located within a given distance of the attack will be affected by the resulting radiation and hence will not work. A probabilistic examination of the resulting power surges indicates that, if left unchecked, th...