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Hello Everyone,</div>
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Just a reminder of the CTN colloquium next <b>Tuesday, March 12 at 3:30 p.m. in</b></div>
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<b>E7-7363</b>. Note that this is not our usual room. It is on the 7<sup>th</sup> floor of the E7 building.</div>
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<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">The speaker is Memming Park from the Champalimaud Centre. </span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><a href="https://www.fchampalimaud.org/research/groups/memming-park">https://www.fchampalimaud.org/research/groups/memming-park</a></span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 14.6667px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);">Prof. Park will be visiting Waterloo March
11-13 and is very interested to meet with people associated with the CTN. If you would like to meet with Prof. Park, please contact me at sacampbell@uwaterloo.ca. He has broad interests in modelling cognitive dynamics, statistical analysis of neural time series
and applications of machine learning in neuroscience.</span></div>
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<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><b>Title:
</b>Persistent learning signals and working memory without continuous attractors</span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><b>Abstract: </b></span></div>
<div class="elementToProof"><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Neural dynamical systems with stable attractor structures, such as point attractors
and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful learning signals necessary to adapt to changes in the temporal structure of the environment. We
show that in addition to the continuous attractors that are widely implicated, periodic and quasi-periodic attractors can also support learning arbitrarily long temporal relationships. Unlike the continuous attractors that suffer from the fine-tuning problem,
the less explored quasi-periodic attractors are uniquely qualified for learning to produce temporally structured behavior. Our theory has broad implications for the design of artificial learning systems and makes predictions about observable signatures of
biological neural dynamics that can support temporal dependence learning and working memory. Based on our theory, we developed a new initialization scheme for artificial recurrent neural networks that outperforms standard methods for tasks that require learning
temporal dynamics. Moreover, we propose a robust recurrent memory mechanism for integrating and maintaining head direction without a ring attractor.</span></div>
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<p style="margin-top: 0px; margin-bottom: 0px;">_______________________________________________</p>
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<p style="margin-top: 0px; margin-bottom: 0px;">Dr. Sue Ann Campbell (she/her)</p>
<p style="margin-top: 0px; margin-bottom: 0px;">Professor and University Research Chair</p>
<p style="margin-top: 0px; margin-bottom: 0px;">Department of Applied Mathematics</p>
<p style="margin-top: 0px; margin-bottom: 0px;"><a href="https://uwaterloo.ca/centre-for-theoretical-neuroscience/" id="OWAf9851970-405f-bd4a-84fc-af73fe737692" class="OWAAutoLink" title="https://uwaterloo.ca/centre-for-theoretical-neuroscience/" data-loopstyle="linkonly" style="margin-top: 0px; margin-bottom: 0px;">Centre
for Theoretical Neuroscience</a></p>
<p style="margin-top: 0px; margin-bottom: 0px;">Associate Dean, Research</p>
<p style="margin-top: 0px; margin-bottom: 0px;">Faculty of Mathematics</p>
<p style="margin-top: 0px; margin-bottom: 0px;">University of Waterloo</p>
<p style="margin-top: 0px; margin-bottom: 0px;">Waterloo ON N2L 3G1</p>
<p style="margin-top: 0px; margin-bottom: 0px;"><a href="https://www.math.uwaterloo.ca/~sacampbe/" id="OWA0f0b6484-8fdf-f1f7-95ab-c5c0c7fe018c" class="OWAAutoLink" data-loopstyle="linkonly" style="margin-top: 0px; margin-bottom: 0px;">https://www.math.uwaterloo.ca/~sacampbe/</a></p>
<p style="margin-top: 0px; margin-bottom: 0px;">President, <span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt;">
<a href="https://caims.ca/" id="OWA1cdd2380-f6a7-e952-7172-52ede3c73cf7" class="OWAAutoLink" title="https://caims.ca/" data-loopstyle="linkonly" style="margin-top: 0px; margin-bottom: 0px;">Canadian Applied and Industrial Mathematics Society</a></span></p>
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<p style="margin-top: 0px; margin-bottom: 0px;"><i>I acknowledge that I live and work on the traditional territory of the Chonnonton, Anishinaabeg and Haudenosaunee peoples. The University of Waterloo main campus is located on the Haldimand tract, the land
granted to the Six Nations that includes six miles on each side of the Grand River.</i></p>
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