Neuromorphic Computing in Autoassociative Memory with a Regular Spiking Neuron Model
- 10.2991/jrnal.k.200512.013How to use a DOI?
- Spiking neural network; associative memory; DSSN model; spike frequency adaptation
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.
- © 2020 The Authors. Published by Atlantis Press SARL.
- 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/).
Cite this article
TY - JOUR AU - Naruaki Takano AU - Takashi Kohno PY - 2020 DA - 2020/05/18 TI - Neuromorphic Computing in Autoassociative Memory with a Regular Spiking Neuron Model JO - Journal of Robotics, Networking and Artificial Life SP - 63 EP - 67 VL - 7 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.200512.013 DO - 10.2991/jrnal.k.200512.013 ID - Takano2020 ER -