Connectionists: Joint A*STAR (Singapore) and King’s College London PhD Studentship

[Yansong Chua]

We would like to encourage potential PhD candidates interested to work on
understanding the role of stochastic neuron models in neuromorphic
computing to apply for the above PhD studentship.

We are including below a non-technical introduction to the project
involved. The candidate will be jointly supervised by Prof. Osvaldo
Simeone, Professor of Information Engineering, King's College London and
Dr. Yansong Chua, Institute for Infocomm Research, A*STAR.

For further queries, we maybe reached at:
Prof Simeone:
osvaldo.simeone at kcl.ac.uk
Dr Chua:
chuays at i2r.a-star.edu.sg

More application details are also available at

https://www.kcl.ac.uk/research/funding-opportunities/doctoral-research-opportunities/homeeu-funding.aspx

https://www.kcl.ac.uk/research/funding-opportunities/doctoral-research-opportunities/current-phd-opportunities/astar-phd-studentships.aspx

For all its recent breakthroughs, modern machine learning based on deep
neural networks is becoming increasingly unaffordable in terms of computing
and energy resources needed to run training algorithms that achieve
state-of-the-art performance. This poses possibly insurmountable challenges
for the implementation of efficient learning methods on resource-limited
devices such as smart sensors or wearables. A possible solution to this
problem is the adoption of the new paradigm of neuromorphic computing,
which relies on energy-efficient sparse spike-domain processing and
communication that are inspired by the operation of the brain. Whether
Spiking Neural Networks (SNNs) can overcome the limitations of conventional
deep networks for the implementation of low-power machine learning is a
fundamental question that is currently being investigated by major
technology companies and universities. In this project, this issue will be
tackled both theoretically and through hands-on experiments by leveraging
the complementary expertise of the respective research teams at KCL and
A*STAR. Specifically, this research will seek to understand whether
conventional deterministic models for SNNs can be improved by probabilistic
models, which are typically used in neuroscience to model the brain
operation, in terms of accuracy, speed, and robustness. In the first two
years, at KCL, the project will concentrate on deriving models and learning
rules for probabilistic SNNs. In the last two years, at A*STAR, the
research will shift to aspects related to implementation, with a focus on
the comparison between deterministic and probabilistic SNNs and on the use
of nano-scale devices for the implementation of probabilistic SNNs.
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