A Trainable Sequence Learner that Learns and Recognizes Two-Input Sequence Patterns
Neural and Evolutionary Computing
2023-01-03 v1
Abstract
We present two designs for an analog circuit that can learn to detect a temporal sequence of two inputs. The training phase is done by feeding the circuit with the desired sequence and, after the training is completed, each time the trained sequence is encountered again the circuit will emit a signal of correct recognition. Sequences are in the order of tens of nanoseconds. The first design can reset the trained sequence on runtime but assumes very strict timing of the inputs. The second design can only be trained once but is lenient in the input's timing.
Cite
@article{arxiv.2210.12193,
title = {A Trainable Sequence Learner that Learns and Recognizes Two-Input Sequence Patterns},
author = {Jan Hohenheim and Zhaoyu Devon Liu and Tommaso Stecconi and Pietro Palopoli},
journal= {arXiv preprint arXiv:2210.12193},
year = {2023}
}
Comments
Submitted to IEEE TENCON 2022