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Author: Zachary S. Hutchinson

Affiliation: School of Computing and Information Science, University of Maine, Orono, Maine, U.S.A.

Keyword(s): Dimensionality Reduction, Feature Clustering, Spiking Neural Networks, Artificial Dendrites.

Abstract: In this paper, we present an algorithm capable of spatially encoding the relationships between elements of a feature vector. Spike-time dependent feature clustering positions a set of points within a spherical, non- Euclidean space using the timing of spiking neurons. The algorithm uses an Hebbian process to move feature points. Each point is representative of an individual element of the feature vector. Relative angular distances encode relationships within the feature vector of a particular data set. We demonstrate that trained points can inform a feature reduction process. It is capable of clustering features whose relationships extend through time (e.g., spike trains). In this paper, we describe the algorithm and demonstrate it on several real and artificial data sets. This work is the first stage of a larger effort to construct and train artificial dendritic neurons.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hutchinson, Z. (2022). Spike-time Dependent Feature Clustering. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 188-194. DOI: 10.5220/0010799100003116

@conference{icaart22,
author={Zachary S. Hutchinson.},
title={Spike-time Dependent Feature Clustering},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={188-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010799100003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Spike-time Dependent Feature Clustering
SN - 978-989-758-547-0
IS - 2184-433X
AU - Hutchinson, Z.
PY - 2022
SP - 188
EP - 194
DO - 10.5220/0010799100003116
PB - SciTePress