Synaptic rewiring in neuromorphic VLSI for topographic map formation (PhD)

I’m interested in how neural networks rewire themselves during development, so that for example brain cells which are connected to each other when you’re a baby might end up connected to different cells later on. I’m using as a prime example how the connections from light-sensitive cells on the back of the eye organise themselves in the same order as they project to different areas of the brain.

I want to create silicon chips with circuitry which behaves a bit like brain cells, and I want to use these artificial neurons to demonstrate this rewiring behaviour. Silicon chips designed in this way might ultimately lead to better designs of computers, and I’m interested in how models systems like the ones I’m creating could be scaled up into useable systems.

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Related Publications and Presentations

  • Simeon Bamford, “Synaptic Rewiring in Neuromorphic VLSI for Topographic Map Formation”, Electronic and Computer Engineering Dept., Hong Kong University of Science and Technology, 2008.
  • Simeon Bamford, “Synaptic Rewiring in Neuromorphic VLSI for Topographic Map Formation”, Robotics, Brain and Cognitive Sciences Dept., Italian Institute of Technology, 2008.
  • Simeon Bamford, Alan Murray, and David Willshaw, “Synaptic Rewiring for Topographic Map Formation and Receptive Field Development”, Neural Networks, 2010, 23, 517-527.
  • Simeon Bamford, Alan Murray, and David Willshaw, “Synaptic Rewiring for Topographic Map Formation”, International Conference on Artificial Neural Networks (ICANN), 2008.
  • Simeon Bamford, “Synaptic Rewiring for in Neuromorphic VLSI for Topographic Map Formation”, Istituto Superiore di Sanita, 2008.
  • Simeon Bamford, Alan Murray, and David Willshaw, “Large Developing Receptive Fields Using a Distributed and Locally Reprogrammable Address-Event Receiver”, IEEE Transactions on Neural Networks, 2010, 21(2), 286-304.
  • Simeon Bamford, Alan Murray, and David Willshaw, “Large Developing Axonal Arbors Using a Distributed and Locally-Reprogrammable Address-Event Receiver”, International Joint Conference on Neural Networks (IJCNN), 2008.

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