[Eecs_msee] School of EECS Seminar Series Invited Talk-Advancing the State of the Art and Understanding of Spiking Neural Networks

    Hunter, Tiffany huntert1 at ohio.edu
    Fri Apr 8 09:31:08 EDT 2022 
    Please remember this is on Microsoft Teams today at 11:45 am.

-----Original Appointment-----
From: Hunter, Tiffany
Sent: Monday, March 28, 2022 10:50 AM
To: BIHL, TREVOR J DR-03 USAF AFMC AFRL/RYAR; davide.manna at strath.ac.uk 
; alex.vicente-sola at strath.ac.uk 
; ' eecs_bscs at listserv.ohio.edu 
'; ' eecs_bsee at listserv.ohio.edu 
'; ' eecs_mscs at listserv.ohio.edu 
'; ' eecs_msee at listserv.ohio.edu 
'; ' eecs_phd at listserv.ohio.edu 
'; Abukamail, Nasseef; Chelberg, David; Juedes, David; Kelsey, Ralph; Liu, Chang; Liu, Jundong; Mourning, Chad; Ostermann, Shawn; Stewart, Gordon; Welch, Lonnie; Bartone, Chris; Braasch, Michael; Chenji, Harshavardhan; Dill, Jeffrey; Irwin, Dennis; Jadwisienczak, Wojciech; Kaya, Savas; Lawrence, Douglas; Rahman, Faiz; Ugazio, Sabrina; Vasiliadis, Konstantinos; Zhu, Jim; Allwine, Daniel; Goble, James; Schlicher, Jared; Hawes, Carl; Karanth, Avinash
Subject: School of EECS Seminar Series Invited Talk-Advancing the State of the Art and Understanding of Spiking Neural Networks
When: Friday, April 8, 2022 11:45 AM-12:45 PM (UTC-05:00) Eastern Time (US & Canada).
Where: Microsoft Teams Meeting

Advancing the State of the Art and Understanding of Spiking Neural Networks

Trevor Bihl trevor.bihl.2 at us.af.mil 
<mailto: trevor.bihl.2 at us.af.mil 
>


Davide Manna davide.manna at strath.ac.uk 
<mailto: davide.manna at strath.ac.uk 
>

Alex Vicente Sola alex.vicente-sola at strath.ac.uk 
<mailto: alex.vicente-sola at strath.ac.uk 
>

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.

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