Mixed-Mode Neural Circuit for Solving Linear Equations

Part of the Studies in Computational Intelligence book series (SCI, volume 508)


A ‘mixed’-mode neural network is one in which the neuronal states are represented by voltages and the synaptic signals are conveyed by currents. This results in a lower complexity circuit since the synaptic resistances are not required. The present chapter discusses a mixed-mode variant of the voltage-mode linear equation solver presented in the previous chapter. The Differential Voltage Current Conveyor (DVCC) has been used as the analog building block to realize a voltage comparator with current outputs. Further, a digitally programmable version of the mixed-mode circuit is also presented and a mechanism to adjust the weights corresponding to the coefficients in the set of linear equations is also discussed. Effect of deviations from ideal device behaviour, like offsets in the DVCC and opamps, is also explored.


Energy Function Current Output Operational Amplifier Simultaneous Linear Equation Voltage Comparator 


  1. 1.
    Elwan, H.O., Soliman, A.M.: Novel CMOS differential voltage current conveyor and its applications. IEE Proc. Circ. Dev. Syst. 144(3), 195–200 (1997)CrossRefGoogle Scholar
  2. 2.
    Maheshwari, S.: A canonical voltage-controlled VM-APS with a grounded capacitor. Circ. Syst. Sig. Proces. 27(1), 123–132 (2008)CrossRefGoogle Scholar
  3. 3.
    Soliman, A.M.: Generation and classification of Kerwin-Huelsman-Newcomb circuits using the DVCC. Int. J. Circ. Theory Appl. 37, 835–855 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hassan, T.M., Mahmoud, S.A.: New CMOS DVCC realization and applications to instrumentation amplifier and active-RC filters. Int. J. Electron. Commun. 64, 47–55 (2010)CrossRefGoogle Scholar
  5. 5.
    Khateba, F., Khatiba, N., Koton, J.: Novel low-voltage ultra-low-power DVCC based on floating-gate folded cascode OTA. Microelectron. J. 42(8), 1010–1017 (2011)CrossRefGoogle Scholar
  6. 6.
    Rahman, S.A., Jayadeva, Dutta Roy, S.C.: Neural network approach to graph colouring. Electron. Lett. 35(14), 1173–1175 (1999)CrossRefGoogle Scholar
  7. 7.
    Rahman, S.A.: A nonlinear synapse neural network and its applications. PhD thesis, Department of Electrical Engineering, Indian Institute of Technology, Delhi, India (2007)Google Scholar
  8. 8.
    Tank, D., Hopfield, J.: Simple ‘neural’ optimization networks: an A/D converter, signal decision circuit, and a linear programming circuit. IEEE Trans. Circ. Syst. 33(5), 533–541 (1986)CrossRefGoogle Scholar
  9. 9.
    LMC7101A. National semiconductor inc. http://www.national.com/assets/en/tools/spice/LMC7101A.MOD. Last Accessed on 25 Oct 2012
  10. 10.
    Hassan, T.M., Mahmoud, S.A.: Fully programmable universal filter with independent gain-\({\omega }_o-{\rm Q}\) control based on new digitally programmable CMOS CCII. J. Circ. Syst. Comput. 18(5), 875–897 (2009)CrossRefGoogle Scholar
  11. 11.
    Plummer, J.D., Deal, M.D., Griffin, P.B.: Silicon VLSI Technology: Fundamentals, Practice and Modeling. Prentice Hall, Upper Saddle River (2000)Google Scholar
  12. 12.
    Hu, J., Xu, T., Zhang, W., Xia, Y.: A CMOS rail-to-rail differential voltage current conveyor and its applications. In: Communications, Circuits and Systems, 2005. Proceedings. 2005 International Conference on, vol. 2. IEEE (2005)Google Scholar
  13. 13.
    Mahmoud, S.A.: Low voltage wide range CMOS differential voltage current conveyor and its applications. Contemp. Eng. Sci. 1(3), 105–126 (2008)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Electronics EngineeringAligarh Muslim UniversityAligarhIndia

Personalised recommendations