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<p class="MsoNormal"><b><span style="font-size:18.0pt">Advancing the State of the Art and Understanding of Spiking Neural Networks<o:p></o:p></span></b></p>
<p class="MsoNormal"><b><o:p> </o:p></b></p>
<p class="MsoNormal"><span style="font-family:Roboto">Trevor Bihl <a href="mailto:trevor.bihl.2@us.af.mil">
trevor.bihl.2@us.af.mil</a> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-family:Roboto"><o:p> </o:p></span></p>
<p class="xmsonormal" style="margin-bottom:8.0pt;line-height:11.75pt"><span lang="EN-GB" style="font-family:Roboto">Davide Manna
<a href="mailto:davide.manna@strath.ac.uk">davide.manna@strath.ac.uk</a></span><o:p></o:p></p>
<p class="xmsonormal" style="margin-bottom:8.0pt;line-height:11.75pt"><span style="font-family:Roboto">Alex Vicente Sola
<a href="mailto:alex.vicente-sola@strath.ac.uk">alex.vicente-sola@strath.ac.uk</a></span><o:p></o:p></p>
<p class="xmsonormal" style="margin-bottom:8.0pt;mso-line-height-alt:11.75pt"><span lang="EN-GB" style="font-size:16.0pt">Spiking Neural Networks (SNNs) are largely inspired by the human brain and by the theories that govern its components and its learning.
Among these, spiking neuron models are used in SNNs to process events coming from Neuromorphic (NM) inputs or propagated within. Neuroscience literature provides a plethora of different spiking neuron models. This talk will consider the neuron models in the
literature and present a comparative study aimed at understanding whether using models with different complexities increases the performance of an SNN trained. The relationship between the increased complexity and the richness of the spatio-temporal features
of the data is also explored by using two different datasets. Beyond fundamental questions, improving learning algorithms and neural architectures for a more accurate feature extraction are current priorities in SNN research. This talk will further present
our recent work on a study on the key components of modern spiking architectures with the developing a spiking version of the successful residual network (ResNet) architecture. Our results further outperforms prior SNN architectures in benchmark datasets
with less parameters than the prior state of the art.</span><span style="font-size:16.0pt"><o:p></o:p></span></p>
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