PhD Transfer Exam - Andrea D’Aquila (Lovejoy lab)
PhD Transfer Exam
Wednesday October 7th, 1:10 pm - Room 432, Ramsay Wright Building, University of Toronto
Andrea D’Aquila (Lovejoy lab)
" TCAP-Latrophilin ligand receptor interaction: Role of glucose in regulation of skeletal muscle contraction"
Abstract
Encoded in the terminal exon of the four vertebrate teneurin proteins is a bioactive peptide termed teneurin carboxyl-terminal associated peptide (TCAPs 1-4). TCAP-1 is involved in signaling pathways associated with glucose metabolism, stress, and neuroprotection. Evidence indicates that the TCAPs bind and activate latrophilin (LPHN), an adhesion class G-protein coupled receptor (GPCR) related to GPCRs whose ligands are structurally related to TCAP. The teneurin-latrophilin adhesion pair is the only integrative protein unit that has been conserved between invertebrates and vertebrates with respect to neuronal signaling. Previous studies indicate that teneurins are present in skeletal muscle, however latrophilin expression in skeletal muscle has not yet been determined. Therefore, I will investigate the roles that TCAP and latrophilin interaction play in skeletal muscle, as the mechanism by which they interact and stimulate a signal transduction is currently unknown. Skeletal muscle is essential in determining the overall metabolism of an organism, and thus it is critical to understand the roles that TCAP-1 may play in these tissues. My first aim in this research is to establish if the TCAP-latrophilin ligand receptor complex is present and functional in skeletal muscle. Secondly, I will elucidate the mechanism of TCAP-mediated glucose uptake into skeletal muscle. Lastly, I will investigate the physiological effects on vertebrate and invertebrate muscle contractions, including effects on fatigue and damage.
Ramsay Wright is a wheelchair accessible building.
PhD Transfer Exam - Jordan Guerguiev (Richards lab)
PhD Transfer Exam
Tuesday October 6th, 2:30 pm - Room 432, Ramsay Wright Building, University of Toronto
Jordan Guerguiev (Richards lab)
"Biologically Realistic Supervised Learning"
Abstract
Supervised learning refers to learning in neural networks under the guidance of an outside teaching signal. It is an important concept to consider, since whenever the goal of learning is to match some ideal behavioural target, supervised learning provides the best mechanism for achieving this goal, especially in complicated tasks. However, the most successful learning algorithms for supervised learning in neural networks invoke mechanisms that are not biologically realistic. For example, learning with backpropagation of error requires a neuron in a network to know the synaptic connectivity of all the neurons downstream of it. Thus, a biologically plausible mechanism for supervised learning has not yet been proposed. The goal of this Ph.D. project is to develop such a mechanism for supervised learning in neural networks, by combining two results from previous papers – a difference target propagation algorithm proposed by Dong-Hyun Lee et al. and a dendritic prediction algorithm described by Robert Urbanczik and Walter Senn. Using these results, we have developed a spiking neural network model which can engage in biologically realistic supervised learning. In addition to generating a biologically plausible model, the goal of this research is to generate predictions about neuronal behaviour, and to test these predictions in the living brain using two-photon microscopy paired with optogenetics.
Ramsay Wright is a wheelchair accessible building.