Authors:
Marcel Beuler
1
;
Alexander Krum
2
;
Werner Bonath
3
and
Hartmut Hillmer
1
Affiliations:
1
University of Kassel, Germany
;
2
University of New South Wales, Australia
;
3
University of Applied Sciences, Germany
Keyword(s):
Huber-Braun, Neuronal Network, FPGA, VHDL.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The Hodgkin-Huxley model (HH) describes the initiation and propagation of action potentials in neurons closed to the biological conditions, but it is not well suited for large scale simulation of neuronal networks. In this paper, an implementation of the Huber-Braun model is presented. It is a simplified HH-type model and able to reproduce a wide variety of spiking patterns. An FPGA is selected as a reconfigurable hardware implementation platform to simulate the network functionality of the neurons. The 32-bit floating-point format and computation techniques (i.e. CORDIC) instead of LUTs are used to avoid loss of physiological information. We validated our design with a C++ program and report the synthesis result based on Xilinx Virtex 6 FPGA.