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Songbirds are a great model for studying how the brain solves the challenges of vocal imitation, since, like human infants, young songbirds learn to produce complex vocal sequences that are exact copies of those of adult conspecifics. This feat is thought to be accomplished by matching the bird's motor performance to a memorized sensory template. To study this process experimentally, we use a computer interface that presents birds with specific vocal imitation tasks and records their entire vocal output during the process.
Applying this methodology to vocal combinatorial learning, we trained juvenile zebra finches to swap syllable order in their song, or insert a new syllable into a string. Surprisingly, solving these tasks required a prolonged stage of learning new transitions between syllables one by one, indicating that the ability to rearrange vocal sounds is not the starting point of vocal learning, but a laboriously achieved endpoint. Analysis of babbling development data of human infants revealed that infants face a similar challenge in acquiring new transitions between syllables, suggesting that birds and humans share a common developmental stage of gradually learning to combine sounds into sequences.
In a current set of experiments, I am testing hypotheses about the computations involved in sensori-motor vocal learning. For example, is the motor output matched to the sensory template as a single unit, or is the match computed independently for different levels of the song hierarchy? Preliminary results suggest that matching vocal performance to the template occurs independently on at least two levels: the level of individual syllables, and the level of syllable sequences, suggesting that learning on these levels is carried out by distinct neural mechanisms.

Understanding how cells differentiate during embryonic development is invaluable for the derivation of functional cells from stem cells in vitro and for the development of regenerative medicine applications. Mapping the human embryome is an overwhelming challenge including characterization of all the cell types that make up both the developing and mature human body, including embryonic progenitor cell types in between states.
The LifeMap DiscoveryTM database (http://discovery.lifemapsc.com) is taking the lead role towards this effort, providing the research community with viable, scalable, and easy to use data portal describing embryonic development. The data is manually curated from literature, high throughput experiments and large scale datasets. In addition to embryonic development information, the database provides substantial information about stem and progenitor cells, their differentiation protocols and cell therapy applications.
The database has been modeled to integrate data from the in vivo and the in vitro, including gene expression and signaling information which, in developing cells, is essential for stem cells identification and classification. Furthermore, this information can be used to match stem cells and their derived cells to similar in vivo cells, and to enable the development of novel differentiation protocols and therapeutic products.
GeneAnalyticsTM, the gene analysis tool, supports analysis of multiple genes and applies a novel algorithm to match gene sets to tissues and cells within the database.
The value provided by LifeMap Discovery originates from the combined power of this data, which enables identifying, predicting and indicating possible differentiation paths and future regenerative medicine applications. LifeMap Discovery integrates with the more elemental database GeneCards where rich information is available at the gene level, and MalaCards, that provides human disease information.