Nonlinear cellular dynamics of the idealized detonation model: Regular cells

High-resolution numerical simulations of cellular detonations are performed using a parallelized adaptive grid solver, in the case where the channel width is very wide. In particular, the nonlinear response of a weakly unstable ZND detonation to two-dimensional perturbations is studied in the context of the idealized one-step chemistry model. For random perturbations, cells appear with a characteristic size in good agreement with that corresponding to the maximum growth rate from a linear stability analysis. However, the cells then grow and equilibrate at a larger size. It is also shown that the linear analysis predicts well the ratio of cell lengths to cell widths of the fully developed cells. The evolutionary dynamics of the growth are nonetheless quite slow, in that the detonation needs to run of the order of 1000 reaction lengths before the final size and equilibrium state is reached. For sinusoidal perturbations, it is found that there is a large band of wavelengths/cell sizes which can propagate over very long distances (~1000 reaction lengths). By perturbing the fully developed cells of each wavelength, it is found that smaller cells in this range are unstable to symmetry breaking, which again results in cellular growth to a larger final size. However, a range of larger cell sizes appear to be nonlinearly stable. As a result it is found that the final cell size of the model is non-unique, even for such a weakly unstable, regular cell case. Indeed, in the case studied, the equilibrium cell size varies by 100% with different initial conditions. Numerical dependencies of the cellular dynamics are also examined

Spatial Representation and Navigation in a Bio-inspired Robot

A biologically inspired computational model of rodent repre-sentation?based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neuro-physiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks

Induction of Fear Extinction with Hippocampal-Infralimbic BDNF

The extinction of conditioned fear memories requires plasticity in the infralimbic medial prefrontal cortex (IL mPFC), but little is known about the molecular mechanisms involved. Brain-derived neurotrophic factor (BDNF) is a key mediator of synaptic plasticity in multiple brain areas. In rats subjected to auditory fear conditioning, BDNF infused into the IL mPFC reduced conditioned fear for up to 48 hours, even in the absence of extinction training, which suggests that BDNF substituted for extinction. Similar to extinction, BDNF-induced reduction in fear required N-methyl-D-aspartate receptors and did not erase the original fear memory. Rats failing to learn extinction showed reduced BDNF in hippocampal inputs to the IL mPFC, and augmenting BDNF in this pathway prevented extinction failure. Hence, boosting BDNF activity in hippocampal-infralimbic circuits may ameliorate disorders of learned fear