Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Peter Blouw"'
Publikováno v:
Frontiers in Computational Neuroscience, Vol 14 (2020)
Our understanding of the neurofunctional mechanisms of speech production and their pathologies is still incomplete. In this paper, a comprehensive model of speech production based on the Neural Engineering Framework (NEF) is presented. This model is
Externí odkaz:
https://doaj.org/article/360bd5e854f04ea0bcd30c56c203ec1b
Publikováno v:
Frontiers in Psychology, Vol 11 (2020)
BackgroundTo produce and understand words, humans access the mental lexicon. From a functional perspective, the long-term memory component of the mental lexicon is comprised of three levels: the concept level, the lemma level, and the phonological le
Externí odkaz:
https://doaj.org/article/184476d7c38c4995ad1a15642db100a2
Publikováno v:
Frontiers in Neurorobotics, Vol 13 (2019)
Predicting future behavior and positions of other traffic participants from observations is a key problem that needs to be solved by human drivers and automated vehicles alike to safely navigate their environment and to reach their desired goal. In t
Externí odkaz:
https://doaj.org/article/5ca2feff614d446f8159c76239cea357
Publikováno v:
Frontiers in Robotics and AI, Vol 6 (2019)
Many medical screenings used for the diagnosis of neurological, psychological or language and speech disorders access the language and speech processing system. Specifically, patients are asked to fulfill a task (perception) and then requested to giv
Externí odkaz:
https://doaj.org/article/195d503f7d044cceb346dfa72521125c
Autor:
Peter Blouw, Chris Eliasmith
Publikováno v:
Frontiers in Psychology, Vol 8 (2018)
Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be
Externí odkaz:
https://doaj.org/article/621bb1b777cc45c5b6e5ff8b7c0b0b1e
Autor:
Peter Blouw, Aaron R. Voelker, Xuan Choo, Terrence C. Stewart, Nicole Sandra-Yaffa Dumont, Chris Eliasmith
While neural networks are highly effective at learning task-relevant representations from data, they typically do not learn representations with the kind of symbolic structure that is hypothesized to support high-level cognitive processes, nor do the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f56aa69988704d82e73a0eb4f2761ca5
https://nrc-publications.canada.ca/eng/view/object/?id=2c04ad45-d0c3-4065-bcf2-3327803c2fef
https://nrc-publications.canada.ca/eng/view/object/?id=2c04ad45-d0c3-4065-bcf2-3327803c2fef
Autor:
Chris Eliasmith, Peter Blouw
Publikováno v:
ICASSP
Neuromorphic hardware has long promised to provide power advantages by leveraging the kind of event-driven, temporally sparse computation observed in biological neural systems. Only recently, however, has this hardware been developed to a point that
Publikováno v:
Frontiers in Robotics and AI
Frontiers in robotics and AI 6, 62 (2019). doi:10.3389/frobt.2019.00062
Frontiers in Robotics and AI, Vol 6 (2019)
Frontiers in robotics and AI 6, 62 (2019). doi:10.3389/frobt.2019.00062
Frontiers in Robotics and AI, Vol 6 (2019)
Frontiers in robotics and AI 6, 62 (2019). doi:10.3389/frobt.2019.00062
Published by Lausanne
Published by Lausanne
Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::455aab504bed085f7d2322434e2cb19f
http://arxiv.org/abs/1812.01739
http://arxiv.org/abs/1812.01739
Publikováno v:
SSRN Electronic Journal.
The term "Gettier Case" is a technical term frequently applied to a wide array of thought experiments in contemporary epistemology. What do these cases have in common? It is said that they all involve a justified true belief which, intuitively, is no