Linster et al [8] were successful in replicating the various different response patterns observed experimentally in Manduca sexta projection neurons (PNs) by varying the number of excitatory and inhibitory local neurons (LNs) in an abstract model of the male moth Macroglomerular Complex (MGC) where all connections between pheromone-sensitive ORNs, LNs and PNs are made.
After exposure, 3 animals were euthanized at each 24, 48 and 72 h and blood taken for various analyses, including gene response patterns.
Our method offers flexible accommodation of different response patterns across various brain regions, controls for potential confounding factors, and accounts for subject variability in brain function.
The approach taken is to compute the forced response patterns of various idealised systems, and from these to calculate the parameters of Statistical Energy Analysis model for the systems using the matrix inversion approach [1].
In order to evaluate the dose-dependent response patterns of various oxidative stress parameters, the trout hepatocytes were exposed to three different doses of selenite [50, 100 and 200 μM (corresponding to approximately 10%, 20%and35%5% of 24 h LD50)] in addition to control (0 μM of selenite) for 24 h.
Regression models were constructed to identify the determinants of the various non- and partial response patterns.
Within rounds, the number of tags can be estimated from various statistical metrics extracted from the observed tags' response patterns.
Burattini et al. [ 44] reviewed the response patterns of cells under the influence of various physical agents.
Such mechanisms can underpin adaptive response patterns that depend on prior exposure to various stimuli [ 11, 12].
The halfwidth of the covariance functions reflects various aspects of the response relationship between neurons including synchrony, intrinsic temporal response patterns, such as bursting or oscillatory events, and the influence of potential multiple inputs common to both neurons.
