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Journal of applied research and technology

versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423

J. appl. res. technol vol.7 no.1 Ciudad de México abr. 2009

 

Design, at transistor level, of a neuron with axonic delay

 

E. Mateos Santillán*1 , J. L. Pérez Silva2

 

1,2 Centro de Ciencias Aplicadas y Desarrollo Tecnológico Universidad Nacional Autónoma de México. *E–mail: pepito@aleph.cinstrum.unam.mx

 

ABSTRACT

An electronic neuron designed with only transistors, with the idea of being able to develop to future a VLSI integrated microcircuit is presented. The neuron is of leaky integrator type, with a ramp function with saturation type response and axonic delay. In this work we will present the mathematical model of our neuron, and its electronics main characteristics, as fundamental part of our simulation system, the neural analog computer.

Keywords: Biological neuron models, artificial neuron models, electronic neuron models.

 

RESUMEN

Se presenta una neurona electrónica diseñada con puros transistores con la idea de poder desarrollar a futuro un microcircuito integrado VLSI. La neurona es del tipo integradora, con respuesta tipo rampa con saturación y retardo axónico. En este trabajo presentamos el modelo matemático de nuestra neurona y sus características electrónicas principales, como parte fundamental de un sistema de simulación, la computadora neuronal analógica.

 

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References

[1] A.L. Hodgkin and A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, vol. 117, pp. 500–544. (1952).         [ Links ]

[2] B. Softkey. C. Koch. Single cell models. in M. Arbib. editor. The handbook of brain theory and neural networks. pp. 879–884. MIT Press. Boston. MA. (1995).         [ Links ]

[3] C. Koch and I. Segev, Editors. Methods in neuronal modeling: from synapses to networks. MIT Press. Cambridge. MA. (1989).         [ Links ]

[4] M. Mahowald, R.J. Douglas. A silicon neuron. Nature. vol. 354. pp. 515–518. (1991).         [ Links ]

[5] R.J. Douglas, M. Mahowald. A construction set for silicon neurons. in S.F. Zornetzer and al. editors. Neural and Electronics Networks. pp. 277–296. Academic Press. Arlington. (1995).         [ Links ]

[6] Pérez, J.L., Miranda, A.I., Bañuelos, M.A., Castillo, J., Quintana, S., Garces, A.M., Herrera, A.A., (2003) Neuronal Synapses of Voltage to Frequency, And Frequency to Voltage Conversion. Advances in Artificial Intelligence and Engineering Cybernetics. Vol. IX. pp. 17–21.         [ Links ]

[7] Perez, J.L., Miranda, A.I., Garces, A.M. (2003). A Neuron Model with Parabolic Burst Response. En Advances in Artificial Intelligence and Engineering Cybernetics Vol X. pp. 6–10.         [ Links ]

[8] J.L. Pérez–Silva, A. Garcés–Madrigal, F. Lara–Rosano, F. Gamboa–Rodríguez, A. Miranda–Vitela, Evocation Process in an Artificial Tectal Column. En Transactions on Systems Research and Cybernetics Vol 8 No. 1. pp 40–44. ISSN 1609–8625.         [ Links ]

[9] Mc Cullock, W. Pitts. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133. (1943).         [ Links ]

[10] D. Dupeyron, S. Le Masson, Y. Deval, G. Le Masson and J.P. Dom, A BiCMOS implementation of the Hodgkin–Huxley formalism. Proc. of MicroNeuro'96. Lausanne. IEEE Computer Society Press. pp. 311–316. (1996).         [ Links ]

[11] Laflaquière, S. Le Masson, G. Le Masson and J.P. Dom, Accurate analog VLSI model of Calcium–dependent bursting neurons. International Conference on Neural Networks (ICNN'97. Houston. Texas). (1997).         [ Links ]

[12] Carver Mead, Analog VLSI and Neural Systems. Addison–Wesley Publishing Company. (1989).         [ Links ]

[13] Sarpeshar R., Watts L., and Mead C. Refractory neuron circuits. CNS Technical Report CNS–TR–98–08. California Institute of Technology.         [ Links ]

[14] Van Schaik. Building blocks for electronic spiking neural networks. Neural networks 14 2001. 617–628.         [ Links ]

[15] Rasche, C. and Douglas. R.J., 2000. An improved silicon neuron. Analog Integrated Circuits and Signal Processing Volume 23. Number 3. 227–236. (2000).         [ Links ]

[16] Texas Instruments. TTL Logic. Standard TTL. Schottky. Lpw–Power Schottky. Data Book. U.S.A., (2001).         [ Links ]

[17] B.C. Cragg and H.N.V. Temperley. Brain 78, 304–316. (1955).         [ Links ]

[18] E. Guigon and Y. Burnod. The handbook of brain theory and neural networks. "Short–term memory". M A. Arbib. (The MIT Press. Cambridge. Mass.). 4 – 11. (1995).         [ Links ]

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