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dc.contributor.advisorWillshaw, David
dc.contributor.advisorMurray, Alan
dc.contributor.authorBamford, Simeon A.
dc.date.accessioned2010-10-18T14:36:03Z
dc.date.available2010-10-18T14:36:03Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/1842/3997
dc.description.abstractA generalised model of biological topographic map development is presented which combines both weight plasticity and the formation and elimination of synapses (synaptic rewiring) as well as both activity-dependent and -independent processes. The question of whether an activity-dependent process can refine a mapping created by an activity-independent process is investigated using a statistical approach to analysingmapping quality. The model is then implemented in custom mixed-signal VLSI. Novel aspects of this implementation include: (1) a distributed and locally reprogrammable address-event receiver, with which large axonal fan-out does not reduce channel capacity; (2) an analogue current-mode circuit for Euclidean distance calculation which is suitable for operation across multiple chips; (3) slow probabilistic synaptic rewiring driven by (pseudo-)random noise; (4) the application of a very-low-current design technique to improving the stability of weights stored on capacitors; (5) exploiting transistor non-ideality to implement partially weightdependent spike-timing-dependent plasticity; (6) the use of the non-linear capacitance of MOSCAP devices to compensate for other non-linearities. The performance of the chip is characterised and it is shown that the fabricated chips are capable of implementing the model, resulting in biologically relevant behaviours such as activity-dependent reduction of the spatial variance of receptive fields. Complementing a fast synaptic weight change mechanism with a slow synapse rewiring mechanism is suggested as a method of increasing the stability of learned patterns.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.contributor.sponsorMedical Research Council (MRC)en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionSA Bamford, AF Murray, and DJ Willshaw. Synaptic rewiring for topographic map formation. International Conference on Artificial Neural Networks (ICANN), 2008en
dc.relation.hasversionSA Bamford, AF Murray, and DJWillshaw. Large developing axonal arbors using a distributed and locally-reprogrammable address-event receiver. International Joint Conference on Neural Networks (IJCNN), 2008.en
dc.subjectsynaptic rewiringen
dc.subjectneuromorphic VLSIen
dc.subjecttopographic map formationen
dc.subjectvery large scale integrationen
dc.titleSynaptic rewiring in neuromorphic VLSI for topographic map formationen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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