THE ROLE OF THE SUPERIOR COLLICULUS IN PREDATORY HUNTING

Combining the results of behavioral, neuronal immediate early gene activation, lesion and neuroanatomical experiments, we have presently investigated the role of the superior colliculus (SC) in predatory hunting. First, we have shown that insect hunting is associated with a characteristic large increase in Fos expression in the lateral part of the intermediate gray layer of the SC (Wig). Next, we have shown that animals with bilateral NMDA lesions of the lateral parts of the SC presented a significant delay in starting to chase the prey and longer periods engaged in other activities than predatory hunting. They also showed a clear deficit to orient themselves toward the moving prey and lost the stereotyped sequence of actions seen for capturing, holding and killing the prey. Our Phaseolus vulgaris-leucoagglutinin analysis revealed that the lateral SCig, besides providing the well-documented descending crossed pathway to premotor sites in brainstem and spinal cord, projects to a number of midbrain and diencephalic sites likely to influence key functions in the context of the predatory behavior, such as general levels of arousal, motivational level to hunt or forage, behavioral planning, appropriate selection of the basal ganglia motor plan to hunt, and motor output of the primary motor cortex. In contrast to the lateral SC lesions, medial SC lesions produced a small deficit in predatory hunting, and compared to what we have seen for the lateral SCig, the medial SCig has a very limited set of projections to thalamic sites related to the control of motor planning or motor output, and provides conspicuous inputs to brainstem sites involved in organizing a wide range of anti-predatory defensive responses. Overall, the present results served to clarify how the different functional domains in the SC may mediate the decision to pursue and hunt a prey or escape from a predator. (C) 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.

An algorithm for automatic checking of exercises in a dynamic geometry system: iGeom

One of the key issues in e-learning environments is the possibility of creating and evaluating exercises. However, the lack of tools supporting the authoring and automatic checking of exercises for specifics topics (e.g., geometry) drastically reduces advantages in the use of e-learning environments on a larger scale, as usually happens in Brazil. This paper describes an algorithm, and a tool based on it, designed for the authoring and automatic checking of geometry exercises. The algorithm dynamically compares the distances between the geometric objects of the student`s solution and the template`s solution, provided by the author of the exercise. Each solution is a geometric construction which is considered a function receiving geometric objects (input) and returning other geometric objects (output). Thus, for a given problem, if we know one function (construction) that solves the problem, we can compare it to any other function to check whether they are equivalent or not. Two functions are equivalent if, and only if, they have the same output when the same input is applied. If the student`s solution is equivalent to the template`s solution, then we consider the student`s solution as a correct solution. Our software utility provides both authoring and checking tools to work directly on the Internet, together with learning management systems. These tools are implemented using the dynamic geometry software, iGeom, which has been used in a geometry course since 2004 and has a successful track record in the classroom. Empowered with these new features, iGeom simplifies teachers` tasks, solves non-trivial problems in student solutions and helps to increase student motivation by providing feedback in real time. (c) 2008 Elsevier Ltd. All rights reserved.