Edward W. Kairiss

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1ranked-venue papers

0as first author

0since 2021 · last 1994

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing 1 paper		Bioinformatics and computational biology · 100%
Artificial intelligence 1 paper		Deep learning architectures and training · 100%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Integrated circuit design · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Deep learning architectures and training spiking neural network	1	1994	Model of a Biological Neuron as a Temporal Neural Network · NIPS 1994
Bioinformatics and computational biology computational neuroscience	1	1994	Model of a Biological Neuron as a Temporal Neural Network · NIPS 1994
Bioinformatics and computational biology › computational neuroscience neuron model	1	1994	Model of a Biological Neuron as a Temporal Neural Network · NIPS 1994
Integrated circuit design VLSI design	1	1994	Model of a Biological Neuron as a Temporal Neural Network · NIPS 1994

Methods — techniques the papers use, named apart from their topics

compartmental model · 0.0

Year	Publication	Venue	Position
1994	Model of a Biological Neuron as a Temporal Neural Network abstract A biological neuron can be viewed as a device that maps a multidimen(cid:173) sional temporal event signal (dendritic postsynaptic activations) into a unidimensional temporal event signal (action potentials). We have designed a network, the Spatio-Temporal Event Mapping (STEM) architecture, which can learn to perform this mapping for arbitrary bio(cid:173) physical models of neurons. Such a network appropriately trained, called a STEM cell, can be used in place of a conventional compartmen(cid:173) tal model in simulations where only the transfer function is important, such as network simulations. The STEM cell offers advantages over compartmental models in terms of computational efficiency, analytical tractabili1ty, and as a framework for VLSI implementations of biologi(cid:173) cal neurons. Sean D. Murphy, Edward W. Kairiss	NIPS	2