Spiking Neural Networks and Energy Efficient AI

Research at our chair

In Progress

General References

• Spiking neuron models as biologically inspired computational models
  Spiking neuron models: single neurons, populations, plasticity https://lcnwww.epfl.ch/gerstner/PUBLICATIONS/SpikingNeuronM-extracts.pdf
  Networks of spiking neurons: The third generation of neural network models https://igi-web.tugraz.at/PDF/85a.pdf
• A survey on the training spiking neural networks
  Training Spiking Neural Networks Using Lessons From Deep Learning https://arxiv.org/abs/2109.12894
• Expressivity
  Expressivity of Spiking Neural Networks https://arxiv.org/abs/2308.08218
• Energy efficiency
  To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration https://arxiv.org/pdf/2306.15749
• Neuromorphic computing
  Memory and information processing in neuromorphic systems.https://arxiv.org/pdf/1506.03264
• Applications: SNNs for Vision
  Unsupervised learning of digit recognition using spike-timing-dependent plasticity. https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2015.00099/full

Contact

Do you have questions about our research in this area?

Please do not hesitate to contact us directly. Feel free to write an e-mail to Ernesto Araya Valdivia, one of our PhD students in the field of SNNs and ernergy efficient AI.

Inquiries from students are very welcome!