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dc.contributor.advisorHasler, Jennifer
dc.contributor.authorBrink, Stephen Isaac
dc.date.accessioned2014-01-10T19:36:41Z
dc.date.available2014-01-10T19:36:41Z
dc.date.issued2012-08-24
dc.identifier.urihttp://hdl.handle.net/1853/50143
dc.description.abstractThe goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectNeuromorphic engineering
dc.subjectAnalog computing
dc.subjectSpike timing dependent plasticity
dc.subjectFloating gate
dc.subjectTransistor
dc.subjectAdaptive circuits
dc.subjectBi-directional tunneling
dc.subject.lcshNeural computers
dc.subject.lcshArtificial intelligence
dc.subject.lcshNeurosciences
dc.subject.lcshComputational neuroscience
dc.titleLearning in silicon: a floating-gate based, biophysically inspired, neuromorphic hardware system with synaptic plasticity
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentBiomedical Engineering
dc.contributor.committeeMemberAnderson, David
dc.contributor.committeeMemberBhatti, Pamela
dc.contributor.committeeMemberDeWeerth, Steve
dc.contributor.committeeMemberLiu, Robert


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