VLSI Implementation of a Bio-Inspired Olfactory Spiking Neural Network

  title={VLSI Implementation of a Bio-Inspired Olfactory Spiking Neural Network},
  author={Hung-Yi Hsieh and Kea-Tiong Tang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the… CONTINUE READING
Highly Cited
This paper has 65 citations. REVIEW CITATIONS
25 Citations
28 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 25 extracted citations

66 Citations

Citations per Year
Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 28 references

Studies on metal-oxide semiconductor ZnO as a hydrogen gas sensor

  • C. Prajapati, P. Sahay
  • J. Nano-Electro. Phys., vol. 3, no. 1, pp. 714…
  • 2011
1 Excerpt

Similar Papers

Loading similar papers…