Optimal control of neuronal activity

We investigate the optimal control of neuronal spiking activity for neurons receiving a class of random synaptic inputs, characterized by a positive parameter alpha. Optimal control signals and optimal variances are found exactly for the diffusion process approximating an integrate and fire model. When synaptic inputs are "sub-Poisson" (alpha<0.5), we find that the optimal synaptic input is a delta function (corresponding to bang-bang control) and the optimal signal is not unique. Poisson synaptic input is the critical case: The control signal is unique, but the control signal is still a delta function. For "supra-Poisson" (alpha>0.5) inputs, the optimal control is smooth and unique. The optimal variance obtained in the current paper sets the lowest possible bound in controlling the stochasticity of neuronal activity. We also discuss how to implement the optimal control signal for certain model neurons.     

Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean–Vlasov–Fokker–Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh–Nagumo model neurons, which are often used to approximate Hodgkin–Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.     

Surface Aggregation of Candida albicans on Glass in the Absence and Presence of Adhering Streptococcus gordonii in a Parallel-Plate Flow Chamber: A Surface Thermodynamical Analysis Based on Acid-Base Interactions

Adhesive interactions between yeasts and bacteria are important in the maintenance of infectious mixed biofilms on natural and biomaterial surfaces in the human body. In this study, the extended DLVO (Derjaguin-Landau-Verwey-Overbeek) approach has been applied to explain adhesive interactions between C. albicans ATCC 10261 and S. gordonii NCTC 7869 adhering on glass. Contact angles with different liquids and the zeta potentials of both the yeasts and bacteria were determined and their adhesive interactions were measured in a parallel-plate flow chamber.Streptococci were first allowed to adhere to the bottom glass plate of the flow chamber to different seeding densities, and subsequently deposition of yeasts was monitored with an image analysis system, yielding the degree of initial surface aggregation of the adhering yeasts and their spatial arrangement in a stationary end point. Irrespective of growth temperature, the yeast cells appeared uncharged in TNMC buffer, but yeasts grown at 37 degrees C were intrinsically more hydrophilic and had an increased electron-donating character than cells grown at 30 degrees C. All yeasts showed surface aggregation due to attractive Lifshitz-van der Waals forces. In addition, acid-base interactions between yeasts, yeasts and the glass substratum, and yeasts and the streptococci were attractive for yeasts grown at 30 degrees C, but yeasts grown at 37 degrees C only had favorable acid-base interactions with the bacteria, explaining the positive relationship between the surface coverage of the glass by streptococci and the surface aggregation of the yeasts. Copyright 1999 Academic Press.     

High-spin structure of yrast-band in 78Kr

Lifetime of levels up to 22⁺, have been measured in ⁷⁸Kr and an oblate shape is assigned to the ground state using the CSM and the configuration dependent shell correction calculations. Calculations further show that ⁷⁸Kr is highly γ-soft nucleus. The experimental Q _t values coupled with theoretical calculations indicate an oblate shape for ⁷⁸Kr at low spins and triaxial shape at higher spins     

Structure of 72,74Se at high spin

Lifetimes of high spin states up to { }=22⁺ in the yrast positive parity bands have been measured to investigate the shape evolution with increasing spin in ^{72, 74}Se. The Q _t values derived from these measurements indicate that prolate shape stabilizes for ⁷²Se, while a triaxial shape develops for ⁷⁴Se at higher spins. Comparison of the observed trend in Q _t with spin for ^{72, 74}Se with that of the corresponding kryptones isotones emphasizes the stability provided by N=38 prolate shell gap even at high rotational frequency.     

Studies on the valence electronic structure of Fe and Ni in Fe x Ni1−x alloys

K _β-to-K _α X-ray intensity ratios of Fe and Ni in pure metals and in Fe _x Ni_1−x alloys (x=0.20, 0.50, 0.58) exhibiting similar crystalline structure have been measured following excitation by 59.54 keV γ-rays from a ²⁴¹Am point source, to understand as to why the properties of permalloy Fe_0.2Ni_0.8 is distinct from other alloy compositions. It is observed that the valence electronic structure of Fe_0.2Ni_0.8 alloy is totally different from other alloys which may be attributed to its special magnetic properties.     

IMPROVEMENT OF FLUIDIZABILITY OF FINE POWDERS ——A COMPUTER STUDY

Simulations of the gas fluidization of a cohesive powder were performed using the Stokesian Dynamics method and an agglomeration-deagglomeration model to investigate methods of improving the fluidizability of fine powders. Three techniques (a) high gas velocity (b) vibration-assisted fluidization and (c) tapered fluidizer were used in the simulations which provided detailed information on the bed microscopy such as the motion of 1 O0 particles in a fluidizing vessel along with the formation and destruction of cohesive bonds dudng collisions. While all three techniques were found to effectively improve the fluidizability of a strongly cohesive powder, we suggest a combination of high velocity fluidization assisted by extemal vibration of the fluidized bed to minimize entrainment of particles.     

Analysis of inverse stochastic resonance and the long-term firing of Hodgkin–Huxley neurons with Gaussian white noise

In order to explain the occurrence of a minimum in firing rate which occurs for certain mean input levels μ$\mu$ as noise level σ$\sigma$ increases (inverse stochastic resonance, ISR) in Hodgkin–Huxley (HH) systems, we analyze the underlying transitions from a stable equilibrium point to limit cycle and vice-versa. For a value of μ$\mu$ at which ISR is pronounced, properties of the corresponding stable equilibrium point are found. A linearized approximation around this point has oscillatory solutions from whose maxima spikes tend to occur. A one dimensional diffusion is also constructed for small noise. Properties of the basin of attraction of the limit cycle (spike) are investigated heuristically. Long term trials of duration 500000 ms are carried out for values of σ$\sigma$ from 0 to 2.0. The graph of mean spike count versus σ$\sigma$ is divided into 4 regions _R1,…,_R4$R_1,\dots ,R_4$, where _R3$R_3$ contains the minimum associated with ISR. In _R1$R_1$ transitions to the basin of attraction of the rest point are not observed until a small critical value of σ=_σc1$\sigma ={\sigma }_{c_1}$ is reached, at the beginning of _R2$R_2$. The sudden decline in firing rate when σ$\sigma$ is just greater than _σc1${\sigma }_{c_1}$ implies that there is only a small range of noise levels 0<σ<_σc1$0<\sigma <{\sigma }_{c_1}$ where repetitive spiking is safe from annihilation by noise. The firing rate remains small throughout _R3$R_3$. At a larger critical value σ=_σc2$\sigma ={\sigma }_{c_2}$ which signals the beginning of _R4$R_4$, the probability of transitions from the basin of attraction of the equilibrium point to that of the limit cycle apparently becomes greater than zero and the spike rate thereafter increases with increasing σ$\sigma$. The quantitative scheme underlying the ISR curve is outlined in terms of the properties of exit time random variables. In the final subsection, several statistical properties of the main random variables associated with long term spiking activity are given, including distributions of exit times from the two relevant basins of attraction and the interspike interval.