Artificial Life and Robotics

, Volume 19, Issue 3, pp 215–219 | Cite as

Silicon neuron: digital hardware implementation of the quartic model

  • F. Grassia
  • T. Levi
  • T. Kohno
  • S. Saïghi
Original Article


This paper presents an FPGA implementation of the quartic neuron model. This approach uses digital computation to emulate individual neuron behavior. We implemented the neuron model using fixed-point arithmetic operation. The neuron model’s computations are performed in arithmetic pipelines. It was designed in VHDL language and simulated prior to mapping in the FPGA. We show that the proposed FPGA implementation of the quartic neuron model can emulate the electrophysiological activities in various types of cortical neurons and is capable of producing a variety of different behaviors, with diversity similar to that of neuronal cells. The neuron family of this digital neuron can be modified by appropriately adjusting the neuron model’s parameters.


Silicon neuron Neuromorphic engineering FPGA Cortical neuron Quartic model 



This work was supported by the European Union’s Seventh Framework Programme (ICT-FET FP7/2007-2013, FET Young Explorers scheme) under Grant agreement no 284772 BRAIN BOW (


  1. 1.
    Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its applications to conduction and excitation in nerve. J Physiol 117:500–544Google Scholar
  2. 2.
    Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572CrossRefGoogle Scholar
  3. 3.
    Naud R, Marcille N, Clopath C, Gerstner W (2008) Firing patterns in the adaptive exponential integrate-and-fire model. Biol Cybern 99:335–347CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Touboul J (2008) Bifurcation analysis of a general class of nonlinear integrate- and -fire neurons. SIAM J Appl Math 68(4):1045–1079CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Cassidy A, Andreou AG (2008) Dynamical digital silicon neurons. IEEE biomedical circuits and systems conference, Nov 2008, pp 289–292Google Scholar
  6. 6.
    Cassidy A, Georgiou J, Andreou AG (2013) Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization. J Neural Netw 45:4–26CrossRefGoogle Scholar
  7. 7.
    Llinás R (1988) The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242(4886):1654–1664CrossRefGoogle Scholar

Copyright information

© ISAROB 2014

Authors and Affiliations

  1. 1.IMS Lab.University of BordeauxBordeauxFrance
  2. 2.LIMMS/CNRS-IIS, Institute of Industrial ScienceThe University of TokyoTokyoJapan

Personalised recommendations