We focus on developing computational methods and tools for (a) analyzing large-scale gene expression data related to human cancer in search for gene markers and disease sub-categories, (b) identifying regulatory elements such as miRNA precursors and their targets in whole genomes of plants and mammals, (c) building theoretical models of gene regulatory networks. Our methodological approaches include (a) novel clustering and feature selection algorithms, (b) machine learning algorithms such as artificial neural networks, hidden Markov models etc.read more
Research in the CBL can be divided in two disciplines: (a) computational neuroscience, where we focus on the use of modeling techniques for characterizing the role of dendrites in learning and memory and (b) bioinformatics, where we focus on the development of methods and tools for analyzing and modeling biological data.
We are interested in understanding how dendrites and their integrative properties contribute to learning and memory functions. Towards this goal, we build abstract mathematical as well as detailed biophysical models of neural cells and circuits across multiple brain regions (hippocampus, amygdala, PFC) and abstraction levels (single neurons, microcircuits, neuronal networks). We then use the models to study how the anatomical, biophysical and plasticity properties of dendrites contribute to memory functions.