On the Estimation of Population-Specific Synaptic Currents from Laminar Multielectrode Recordings

Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP), is assumed to reflect synaptic activity and can be used to extract the laminar current source density (CSD) profile. However, physiological interpretation of the CSD profile is uncertain because it does not disambiguate synaptic inputs from passive return currents and does not identify population-specific contributions to the signal. These limitations prevent interpretation of the CSD in terms of synaptic functional connectivity in the columnar microcircuit. Here we present a novel anatomically informed model for decomposing the LFP signal into population-specific contributions and for estimating the corresponding activated synaptic projections. This involves a linear forward model, which predicts the population-specific laminar LFP in response to synaptic inputs applied at different positions along each population and a linear inverse model, which reconstructs laminar profiles of synaptic inputs from laminar LFP data based on the forward model. Assuming spatially smooth synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions and estimates the corresponding laminar profiles of synaptic input as a function of time. It should be noted that constant synaptic currents at all positions along a neuronal population cannot be reconstructed, as this does not result in a change in extracellular potential. However, constraining the solution using a priori knowledge of the spatial distribution of synaptic connectivity provides the further advantage of estimating the strength of active synaptic projections from the columnar LFP profile thus fully specifying synaptic inputs

Modeling Io'S Sublimation-Driven Atmosphere: Gas Dynamics And Radiation Emission

Io's sublimation-driven atmosphere is modeled using the direct simulation Monte Carlo method. These rarefied gas dynamics simulations improve upon earlier models by using a three-dimensional domain encompassing the entire planet computed in parallel. The effects of plasma impact heating, planetary rotation, and inhomogeneous surface frost are investigated. Circumplanetary flow is predicted to develop from the warm subsolar region toward the colder night-side. The non-equilibrium thermal structure of the atmosphere, including vibrational and rotational temperatures, is also presented. Io's rotation leads to an asymmetric surface temperature distribution which is found to strengthen circumplanetary flow near the dusk terminator. Plasma heating is found to significantly inflate the atmosphere on both day- and night-sides. The plasma energy flux also causes high temperatures at high altitudes but permits relatively cooler temperatures at low altitudes near the dense subsolar point due to plasma energy depletion. To validate the atmospheric model, a radiative transfer model was developed utilizing the backward Monte Carlo method. The model allows the calculation of the atmospheric radiation from emitting/absorbing and scattering gas using an arbitrary scattering law and art arbitrary surface reflectivity. The model calculates the spectra in the v, vibrational band of SO(2) which are then compared to the observational data.Aerospace Engineerin

BioNet: A Python interface to NEURON for modeling large-scale networks.

There is a significant interest in the neuroscience community in the development of large-scale network models that would integrate diverse sets of experimental data to help elucidate mechanisms underlying neuronal activity and computations. Although powerful numerical simulators (e.g., NEURON, NEST) exist, data-driven large-scale modeling remains challenging due to difficulties involved in setting up and running network simulations. We developed a high-level application programming interface (API) in Python that facilitates building large-scale biophysically detailed networks and simulating them with NEURON on parallel computer architecture. This tool, termed "BioNet", is designed to support a modular workflow whereby the description of a constructed model is saved as files that could be subsequently loaded for further refinement and/or simulation. The API supports both NEURON's built-in as well as user-defined models of cells and synapses. It is capable of simulating a variety of observables directly supported by NEURON (e.g., spikes, membrane voltage, intracellular [Ca++]), as well as plugging in modules for computing additional observables (e.g. extracellular potential). The high-level API platform obviates the time-consuming development of custom code for implementing individual models, and enables easy model sharing via standardized files. This tool will help refocus neuroscientists on addressing outstanding scientific questions rather than developing narrow-purpose modeling code

In vivo Stimulus-Induced Vasodilation Occurs without IP3 Receptor Activation and May Precede Astrocytic Calcium Increase

publishedVersio