ICANN 98 pp 475-480 | Cite as

Analog VLSI implementation of a spike driven stochastic dynamical synapse

  • Mario Annunziato
  • Davide Badoni
  • Stefano Fusi
  • Andrea Salamon
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


We have undertaken to implement in analog electronics a neural network device which autonomously learns from its experience in real time. Implementing a large neural network that has this capability, implies analog VLSI technology and on-chip learning. This means designing a plastic synaptic connection that 1. is simple (low number of transistors and reduced silicon area), 2. has low power consumption and 3. preserves memory on long time scales and, at the same time, can be modified in short time intervals during stimulation.


Current Mirror Synaptic Efficacy Analog VLSI Presynaptic Spike Spike Duration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Mario Annunziato
    • 1
  • Davide Badoni
    • 2
  • Stefano Fusi
    • 3
  • Andrea Salamon
    • 4
  1. 1.Physics Dept.University of PisaItaly
  2. 2.INFN Sezione RM2RomeItaly
  3. 3.INFN Sezione RM1RomeItaly
  4. 4.Physics Dept.University of Rome “La Sapienza”RomeItaly

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