Biomedical subjects

E Pirozzi

Publications and source records attributed to E Pirozzi.

4 recordsLinked to original sources

PubMed2005

On Myosin II dynamics in the presence of external loads.

We address the controversial hot question concerning the validity of the loose coupling versus the lever-arm theories in the actomyosin dynamics by re-interpreting and extending the phenomenological washboard potential model proposed by some of us in a previous paper. In this new model a Brownian motion harnessing thermal energy is assumed to co-exist with the deterministic swing of the lever-arm, to yield an excellent fit of the set of data obtained by some of us on the sliding of Myosin II heads on immobilized actin filaments under various load conditions. Our theoretical arguments are complemented by accurate numerical simulations, and the robustness of the model is tested via different choices of parameters and potential profiles.

Actins↗

PubMedDate not supplied

On the dynamics of a pair of coupled neurons subject to alternating input rates.

We present a statistical analysis of the firing activity of two coupled neuronal units that interact according to a 'sending-receiving' model. The membrane potential's behavior of both units is described by the Stein equations under the additional assumption that the spikes released by the sending neuron constitute an extra excitation for the receiving one. We also assume the presence of an alternating behavior for the rates of inputs to the sending neuron. By means of ad hoc simulations, we obtain, and then discuss, some statistical results concerning the spike production times of the units within the subintervals of the alternating inputs, as well as the reaction times of the receiving neuron.

Action Potentials↗

PubMedDate not supplied

On some computational results for single neurons' activity modeling.

The classical Ornstein-Uhlenbeck diffusion neuronal model is generalized by inclusion of a time-dependent input whose strength exponentially decreases in time. The behavior of the membrane potential is consequently seen to be modeled by a process whose mean and covariance classify, it as Gaussian-Markov. The effect of the input on the neuron's firing characteristics is investigated by comparing the firing probability densities and distributions for such a process with the corresponding ones of the Ornstein-Uhlenbeck model. All numerical results are obtained by implementation of a recently developed computational method.

Computational Biology↗

PubMedDate not supplied

On a non-Markov neuronal model and its approximations.

Single neuron's activity modeling is considered with reference to some earlier contributions in which a non-Markov Gaussian process is assumed to describe the time course of the neuron's membrane potential. After re-formulating the problem in a rigorous framework and pinpointing the limits of validity of such a model, the available results on the firing probability density are compared with those obtained by us by means of an ad hoc numerical algorithm implemented for the leaky integrator diffusion firing model and with some data constructed by a simulation procedure of non-Markov Gaussian processes with pre-assigned covariances. Throughout this paper, the notion of 'correlation time' plays a fundamental role for the neuronal coding process modeling.

Markov Chains↗