A Trainable Sequence Learner that Learns and Recognizes Two-Input Sequence Patterns

October 21, 2022 ยท Declared Dead ยท ๐Ÿ› IEEE Region 10 Conference

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Authors Jan Hohenheim, Zhaoyu Devon Liu, Tommaso Stecconi, Pietro Palopoli arXiv ID 2210.12193 Category cs.NE: Neural & Evolutionary Citations 0 Venue IEEE Region 10 Conference Last Checked 4 months ago
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.
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