Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2015
Abstract
Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon as a slow leaky integrator, which captures almost all-known neural behaviors. The model controls the switching of axonal firing dynamics between passive conduction mode and persistent firing mode. The interplay between the axonal integrated potential and its multiple thresholds in axon precisely determines the persistent firing dynamics of neurons. We also present a persistent firing polychronous spiking network which exhibits asynchronous dynamics indicating that this computationally efficient model is not only bio-plausible, but also suitable for large scale spiking network simulations. The implications of this network and the analog circuit design for exploring the relationship between working memory and persistent firing enable developing a spiking networkbased memory and bio-inspired computer systems.
Keywords
Neuron Model, Neuromorphic, Persistent Firing, Slow Integration, Spiking Network, Working Memory
Discipline
Databases and Information Systems | OS and Networks
Research Areas
Data Science and Engineering
Publication
International Journal of Intelligence Science
Volume
5
Issue
2
First Page
89
Last Page
101
ISSN
2163-0283
Identifier
10.4236/ijis.2015.52009
Publisher
Scientific Research Publishing
Citation
NING, Ning; LI, Guoqi; HE, Wei; HUANG, Kejie; PAN, Li; RAMANATHAN, Kiruthika; ZHAO, Rong; and SHI, Luping.
Modeling neuromorphic persistent firing networks. (2015). International Journal of Intelligence Science. 5, (2), 89-101.
Available at: https://ink.library.smu.edu.sg/sis_research/7389
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.4236/ijis.2015.52009