Quantization of Map-Based Neuronal Model for Embedded Simulations of Neurobiological Networks in Real-Time
- 1 University of California, United States
- 2 Ariel Systems, United States
- 3 California State University San Marcos, United States
- 4 National Research University Higher School of Economics, Russia
Copyright: © 2020 Nikolai F. Rulkov, Ariel Mark Hunt, Peter N. Rulkov and Andrey G. Maksimov. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The discreet-time (map-based) approach to modeling nonlinear dynamics of spiking and spiking-bursting activity of neurons has demonstrated its very high efficiency in simulations of neuro-biologically realistic behavior both in large-scale network models for brain activity studies and in real-time operation of Central Pattern Generator network models for biomimetic robotics. This paper studies the next step in improving the model computational efficiency that includes quantization of model variables and makes the network models suitable for embedded solutions. We modify a map-based neuron model to enable simulations using only integer arithmetic and demonstrate a significant reduction of computation time in an embedded system using readily available, inexpensive ARM Cortex L4 microprocessors.
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- Map-Based Neuron Models
- Spiking-Bursting Activity
- Embedded Solutions
- Biomimetic Robotics
- Neurobiological Networks