Neuromorphic Encoding / Decoding of Data-Event Streams Based on the Poisson Point Process Model

Viacheslav Antsiperov

2024

Abstract

The work is devoted to a new approach to neuromorphic encoding of streaming data. An essential starting point of the proposed approach is a special (sampling) representation of input data in the form of a stream of discrete events (counts), modeling the firing events of biological neurons. Considering the specifics of the sampling representation, we have formed a generative model for the primary processing of the count stream. That model was also motivated by known neurophysiological facts about the structure of receptive fields of sensory systems of living organisms that implement universal mechanisms (including central-circumferential inhibition) of biological neural networks, particularly the brain. To list the main ideas and consolidate the notations used, the article provides a brief overview of the features and most essential provisions of the proposed approach. The new results obtained within the framework of the approach, related to the analysis of neuromorphic encoding (with distortions) of streaming data, are discussed. The issues of possible decoding/restoration of the original data are discussed in the context of what Marr called the primary sketch. The results of computer modelling of the developed encoding/decoding procedures are presented, approximate numerical characteristics of their quality are given.

Paper Citation

in Harvard Style

Antsiperov V. (2024). Neuromorphic Encoding / Decoding of Data-Event Streams Based on the Poisson Point Process Model. In Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS; ISBN 978-989-758-720-7, SciTePress, pages 139-146. DOI: 10.5220/0013015500003886

in Bibtex Style

@conference{explains24,
author={Viacheslav Antsiperov},
title={Neuromorphic Encoding / Decoding of Data-Event Streams Based on the Poisson Point Process Model},
booktitle={Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS},
year={2024},
pages={139-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013015500003886},
isbn={978-989-758-720-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods - Volume 1: EXPLAINS
TI - Neuromorphic Encoding / Decoding of Data-Event Streams Based on the Poisson Point Process Model
SN - 978-989-758-720-7
AU - Antsiperov V.
PY - 2024
SP - 139
EP - 146
DO - 10.5220/0013015500003886
PB - SciTePress