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On-line Hebbian learning for spiking neurons: Architecture of the weight-unit of NESPINN

  • Part VIII: Implementations
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

We present the implementation of on-line Hebbian learning for NESPINN, the Neurocomputer for the simulation of spiking neurons. In order to support various forms of Hebbian learning we developed a programmable weight unit for the NESPINN-system. On-line weight modifications are performed event-controlled in parallel to the computation of basic neuron functions. According to our VHDL-simulations, the system will offer a performance of up to 50 MCUPS.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Roth, U., Jahnke, A., Klar, H. (1997). On-line Hebbian learning for spiking neurons: Architecture of the weight-unit of NESPINN. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020317

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  • DOI: https://doi.org/10.1007/BFb0020317

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

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