Spike-time Dependent Feature Clustering

Zachary Hutchinson

2022

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.

Paper Citation

in Harvard Style

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, pages 188-194. DOI: 10.5220/0010799100003116

in Bibtex Style

@conference{icaart22,
author={Zachary 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},
}

in EndNote Style

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
AU - Hutchinson Z.
PY - 2022
SP - 188
EP - 194
DO - 10.5220/0010799100003116