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Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis

Overview of attention for article published in Frontiers in Neuroscience, January 2015
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Title
Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis
Published in
Frontiers in Neuroscience, January 2015
DOI 10.3389/fnins.2014.00428
Pubmed ID
Authors

Konstantinos I. Papadimitriou, Shih-Chii Liu, Giacomo Indiveri, Emmanuel M. Drakakis

Abstract

The field of neuromorphic silicon synapse circuits is revisited and a parsimonious mathematical framework able to describe the dynamics of this class of log-domain circuits in the aggregate and in a systematic manner is proposed. Starting from the Bernoulli Cell Formalism (BCF), originally formulated for the modular synthesis and analysis of externally linear, time-invariant logarithmic filters, and by means of the identification of new types of Bernoulli Cell (BC) operators presented here, a generalized formalism (GBCF) is established. The expanded formalism covers two new possible and practical combinations of a MOS transistor (MOST) and a linear capacitor. The corresponding mathematical relations codifying each case are presented and discussed through the tutorial treatment of three well-known transistor-level examples of log-domain neuromorphic silicon synapses. The proposed mathematical tool unifies past analysis approaches of the same circuits under a common theoretical framework. The speed advantage of the proposed mathematical framework as an analysis tool is also demonstrated by a compelling comparative circuit analysis example of high order, where the GBCF and another well-known log-domain circuit analysis method are used for the determination of the input-output transfer function of the high (4(th)) order topology.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 8%
Switzerland 1 4%
Unknown 22 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 24%
Professor 3 12%
Researcher 3 12%
Student > Ph. D. Student 3 12%
Student > Doctoral Student 2 8%
Other 3 12%
Unknown 5 20%
Readers by discipline Count As %
Engineering 13 52%
Neuroscience 2 8%
Agricultural and Biological Sciences 1 4%
Computer Science 1 4%
Nursing and Health Professions 1 4%
Other 4 16%
Unknown 3 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 February 2015.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from Frontiers in Neuroscience
#10,137
of 11,542 outputs
Outputs of similar age
#308,011
of 359,949 outputs
Outputs of similar age from Frontiers in Neuroscience
#113
of 125 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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