Nuovo modello animale per lo studio della working memory: validazione farmacologica e applicazioni

The present study was performed to validate a spatial working memory task using pharmacological manipulations. The water escape T-maze, which combines the advantages of the Morris water maze and the T-maze while minimizes the disadvantages, was used. Scopolamine, a drug that affects cognitive function in spatial working memory tasks, significantly decreased the rat performance in the present delayed alternation task. Since glutamate neurotransmission plays an important role in the maintaining of working memory, we evaluated the effect of ionotropic and metabotropic glutamatergic receptors antagonists, administered alone or in combination, on rat behaviour. As the acquisition and performance of memory tasks has been linked to the expression of the immediately early gene cFos, a marker of neuronal activation, we also investigated the neurochemical correlates of the water escape T-maze after pharmacological treatment with glutamatergic antagonists, in various brain areas. Moreover, we focused our attention on the involvement of perirhinal cortex glutamatergic neurotransmission in the acquisition and/or consolidation of this particular task. The perirhinal cortex has strong and reciprocal connections with both specific cortical sensory areas and some memory-related structures, including the hippocampal formation and amygdala. For its peculiar position, perirhinal cortex has been recently regarded as a key region in working memory processes, in particular in providing temporary maintenance of information. The effect of perirhinal cortex lesions with ibotenic acid on the acquisition and consolidation of the water escape T-maze task was evaluated. In conclusion, our data suggest that the water escape T-maze could be considered a valid, simple and quite fast method to assess spatial working memory, sensible to pharmacological manipulations. Following execution of the task, we observed cFos expression in several brain regions. Furthermore, in accordance to literature, our results suggest that glutamatergic neurotransmission plays an important role in the acquisition and consolidation of working memory processes.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.