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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

September 21, 2012

Speaker: Bobby Pourziaei, Department of Math and Stats, York University

Title: Single Parameter Spatiotemporal Model For Depth Perception Of Weakly Electric Fish

Abstract: Depth perception in weakly electric fish was thought impossible until the work of Emde et al. 1998. Depth perception mechanisms suggested thus far are based on multi-parameter measurements and take into account only the spatial profile of the electric image, disregarding its temporal characteristic. The EOD of weakly electric fish are amongst the most stable known biological oscillators.  In this talk we develop a model for depth perception, based on both the spatial and temporal characteristics of the electric image. Our model is based on measurements of a single parameter, namely the width of the electric image. In contrast to previously suggested algorithms, our algorithm would only require a single narrow tuned topological map to accurately estimate distance. We use this model to study the effects of electromagnetic noise and the presence of stochastic resonance.