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<p class="MsoNormal"><span style="font-size:11.0pt">Hello all,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">The inauguration of the BIC Postdocs and Students seminar will take place at 12 noon on Monday, October 21.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Venue: The Grand-pre auditorium at the Neuro.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Pizza and soft drinks will be provided at 11:55 AM, courtesy of the BIC director (strictly for attendees of the seminar).<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Please RSVP here:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><a href="https://www.surveymonkey.com/r/ZNY3YVG">https://www.surveymonkey.com/r/ZNY3YVG</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal">Title: The hippocampus at the center of the universe: Evolution, AI, and HippoMaps<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Presenter: Jordan DeKraker, postdoc, supervisors: Dr. Boris Bernhardt and Dr. Alan Evans<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Summary: Many researchers study the hippocampus as a center for memory, navigation or as a crux in disease. Here, I consider the role of the hippocampus as a driver, or cost function, for deep learning throughout brain evolution and in
modern AI. Modern AI takes millions of training samples and is told the corresponding interpretations (outputs) for each. Animals learn fast and through mostly unstructured exploration. I argue that the ability to predict, and especially to remember predictions
over long timescales, is a key component of animal learning and an avenue towards more data-efficient and general AI. In part 2, I address the issue that current AI models don't make good long-term predictions and present a way forward through HippoMaps: a
tool for quantitative mapping of hippocampal macro-, micro-circuitry, and functional recordings across domains and spatial scales. Spatial comparison of these maps can help us better understand the contrast of the acquisition methods and inter-individual differences,
but also the mechanisms through which circuitry gives rise to function. Altogether, this talk leverages computational methods for making bridgeheads between theory-driven and data-driven understanding of learning and intelligence. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">See you all,<o:p></o:p></p>
<p class="MsoNormal">Amir.<o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
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