Publications [#289426] of Silvia Ferrari

Papers Published

1. Hu, D; Zhang, X; Xu, Z; Ferrari, S; Mazumder, P, Digital implementation of a spiking neural network (SNN) capable of spike-timing-dependent plasticity (STDP) learning, 14th Ieee International Conference on Nanotechnology, Ieee Nano 2014 (January, 2014), pp. 873-876, IEEE, ISBN 9781479956227 [doi]
   (last updated on 2021/09/05)

   Abstract:
   The neural network model of computation has been proven to be faster and more energy-efficient than Boolean CMOS computations in numerous real-world applications. As a result, neuromorphic circuits have been garnering growing interest as the integration complexity within chips has reached several billion transistors. This article presents a digital implementation of a re-scalable spiking neural network (SNN) to demonstrate how spike timing-dependent plasticity (STDP) learning can be employed to train a virtual insect to navigate through a terrain with obstacles by processing information from the environment.