Accelerating Ray Shooting Through Aggressive 5D Visibility Pre-processing

We present a new approach to accelerating general ray shooting. Our technique uses a five-dimensional ray space partition and is based on the classic ray-classication algorithm. Where the original algorithmevaluates intersection candidates at run-time, our solution evaluates them as a preprocess. The offline nature of our solution allows for an adaptive subdivision of ray space. The advantage being, that it allows for the placement of a user set upper bound on the number of primitives intersected. The candidate sets produced account for occlusion, thereby reducing memory requirements and accelerating the ray shooting process. A novel algorithm which exploits graphics hardware is used to evaluate the candidate sets. It is the treatment of occlusion that allows for the practical precomputation of the ray space partition. This algorithm is called aggressive since it is optimal (no invisible primitives are included), but may result in false exclusion of visible primitives. Error is minimised through the adaptive sampling

The neurogenic potential of astrocytes is regulated by inflammatory signals

Although the adult brain contains neural stem cells (NSCs) that generate new neurons throughout life, these astrocyte-like populations are restricted to two discrete niches. Despite their terminally differentiated phenotype, adult parenchymal astrocytes can re-acquire NSC-like characteristics following injury, and as such, these 'reactive' astrocytes offer an alternative source of cells for central nervous system (CNS) repair following injury or disease. At present, the mechanisms that regulate the potential of different types of astrocytes are poorly understood. We used in vitro and ex vivo astrocytes to identify candidate pathways important for regulation of astrocyte potential. Using in vitro neural progenitor cell (NPC)-derived astrocytes, we found that exposure of more lineage-restricted astrocytes to either tumor necrosis factor alpha (TNF-α) (via nuclear factor-κB (NFκB)) or the bone morphogenetic protein (BMP) inhibitor, noggin, led to re-acquisition of NPC properties accompanied by transcriptomic and epigenetic changes consistent with a more neurogenic, NPC-like state. Comparative analyses of microarray data from in vitro-derived and ex vivo postnatal parenchymal astrocytes identified several common pathways and upstream regulators associated with inflammation (including transforming growth factor (TGF)-β1 and peroxisome proliferator-activated receptor gamma (PPARγ)) and cell cycle control (including TP53) as candidate regulators of astrocyte phenotype and potential. We propose that inflammatory signalling may control the normal, progressive restriction in potential of differentiating astrocytes as well as under reactive conditions and represent future targets for therapies to harness the latent neurogenic capacity of parenchymal astrocytes

Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex

The orbitofrontal cortex has been hypothesized to carry information regarding the value of expected rewards. Such information is essential for associative learning, which relies on comparisons between expected and obtained reward for generating instructive error signals. These error signals are thought to be conveyed by dopamine neurons. To test whether orbitofrontal cortex contributes to these error signals, we recorded from dopamine neurons in orbitofrontal-lesioned rats performing a reward learning task. Lesions caused marked changes in dopaminergic error signaling. However, the effect of lesions was not consistent with a simple loss of information regarding expected value. Instead, without orbitofrontal input, dopaminergic error signals failed to reflect internal information about the impending response that distinguished externally similar states leading to differently valued future rewards. These results are consistent with current conceptualizations of orbitofrontal cortex as supporting model-based behavior and suggest an unexpected role for this information in dopaminergic error signaling

Choice, uncertainty and value in prefrontal and cingulate cortex.

Reinforcement learning models that focus on the striatum and dopamine can predict the choices of animals and people. Representations of reward expectation and of reward prediction errors that are pertinent to decision making, however, are not confined to these regions but are also found in prefrontal and cingulate cortex. Moreover, decisions are not guided solely by the magnitude of the reward that is expected. Uncertainty in the estimate of the reward expectation, the value of information that might be gained by taking a course of action and the cost of an action all influence the manner in which decisions are made through prefrontal and cingulate cortex