Authors
Naruaki Takano1, *, Takashi Kohno2
1Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
2Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Naruaki Takano
Received 6 November 2019, Accepted 16 March 2020, Available Online 18 May 2020.
DOI
https://doi.org/10.2991/jrnal.k.200512.013How to use a DOI?
Keywords
Spiking neural network; associative memory; DSSN model; spike frequency adaptation
Abstract
Digital Spiking Silicon Neuron (DSSN) model is a qualitative neuron model specifically designed for efficient digital circuit implementation which exhibits high biological plausibility. In this study we analyzed the behavior of an autoassociative memory composed of 3-variable DSSN model which has a slow negative feedback variable that models the effect of slow ionic currents responsible for Spike Frequency Adaptation (SFA). We observed the network dynamics by altering the strength of SFA which is known to be dependent on Acetylcholine volume, together with the magnitude of neuronal interaction. By altering these parameters, we obtained various pattern retrieval dynamics, such as chaotic transitions within stored patterns or stable and high retrieval performance. In the end, we discuss potential applications of the obtained results for neuromorphic computing.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).