Purpose: Neuromorphic Computing is an integrative area of study that benefits from collaboration between Neuroscience and Computer Engineering. Our journal club aims to facilitate this collaboration by providing a semi-formal environment to share research and ideas with peers. Subscribe to our email list and take a look at our upcoming meeting topics below:

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Brains, Algorithms, and Engineering

Upcoming Topics

  • Prof. Jennifer Hasler: Physical Neuromorphic Computing
    • Date: July 27th @ 5-6:30 pm
    • Abstract: Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are meeting hard physical limits. These silicon systems mimic extremely energy-efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Towards this end, we provide a glimpse at what the technology evolution roadmap looks like for these systems so that Neuromorphic engineers may gain the same benefit of anticipation and foresight that IC designers gained from Moore’s law many years ago. Scaling of energy efficiency, performance, and size will be discussed as well as how the implementation and application space of Neuromorphic systems are expected to evolve over time.  These approaches are fueled by recent advances in programmable and configurable large-scale analog circuits and systems enabling a typical factor of 1000 improvement in computational power (Energy) efficiency over their digital counterparts. 


Our goal is to utilize this Journal Club to investigate and formulate answers to central questions in neuromorphics:

  • How does the human brain compute using limited resources (time, energy, space)?
  • What are possible data encodings possible in spiking neural networks, and what are their advantages and tradeoffs?
  • How can we build systems which learn in an unsupervised or semi-supervised environment?

If you are interested in giving a guest presentation for our group, contact lzelsrouji@ucdavis.edu.