Jan Gosmann, Aaron R. Voelker, and Chris Eliasmith. A spiking independent accumulator model for winner-take-all computation. In Proceedings of the 39th Annual Conference of the Cognitive Science Society. London, UK, 2017. Cognitive Science Society. URL: https://mindmodeling.org/cogsci2017/papers/0405/index.html.
@inproceedings{gosmann2017a,
title = {A Spiking Independent Accumulator Model for Winner-Take-All Computation},
author = {Jan Gosmann and Aaron R. Voelker and Chris Eliasmith},
booktitle = {Proceedings of the 39th Annual Conference of the Cognitive Science Society},
publisher = {Cognitive Science Society},
address = {London, UK},
url = {https://mindmodeling.org/cogsci2017/papers/0405/index.html},
pdf = {/files/publications/gosmann.2017a.pdf},
poster = {/files/publications/gosmann.2017a.poster.pdf},
abstract = {Winner-take-all (WTA) mechanisms are an important component
of many cognitive models. For example, they are often
used to decide between multiple choices or to selectively direct
attention. Here we compare two biologically plausible,
spiking neural WTA mechanisms. We first provide a novel
spiking implementation of the well-known leaky, competing
accumulator (LCA) model, by mapping the dynamics onto a
population-level representation. We then propose a two-layer
spiking independent accumulator (IA) model, and compare its
performance against the LCA network on a variety of WTA
benchmarks. Our findings suggest that while the LCA network
can rapidly adapt to new winners, the IA network is better
suited for stable decision making in the presence of noise.},
year = {2017},
}