Hardware spiking neural network with run-time reconfigurable connectivity in an autonomous robot

By Daniel Roggen, Stephan Hofmann, Yann Thoma, Dario Floreano. 2003

90 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Mechanical Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Liam P. Maguire
2. Ammar Belatreche
3. T. McGinnity

In Electrical and Electronic Engineering

1. Liam P. Maguire
2. Ammar Belatreche
3. T. McGinnity

Citations in the corpus

• Deep learning in neural networks: An overview (2014, neutral): “Future energyefficient hardware for DL in NNs may implement aspects of such models; see (e.g., Liu et al., 2001; Roggen et al., 2003; Glackin et al., 2005; Fieres et al., 2008; Khan et al., 2008; Serrano-Gotarredona et al., 2009; Jin et al., 2010; Neil and Liu, 2014).”
• A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware (2005, background): “It is believed that this allows spiking neurons to have richer dynamics as they can exploit the temporal domain to encode or decode data in the form of spike trains [7].”