Neuromorphic Computing with Resistive Switching Devices.

Resistive switches, commonly referred to as resistive memory (RRAM) devices and modeled as memristors, are an emerging nanoscale technology that can revolutionize data storage and computing approaches. Enabled by the advancement of nanoscale semiconductor fabrication and detailed understanding of the physical and chemical processes occurring at the atomic scale, resistive switches offer high speed, low-power, and extremely dense nonvolatile data storage. Further, the analog capabilities of resistive switching devices enables neuromorphic computing approaches which can achieve massively parallel computation with a power and area budget that is orders of magnitude lower than today’s conventional, digital approaches. This dissertation presents the investigation of tungsten oxide based resistive switching devices for use in neuromorphic computing applications. Device structure, fabrication, and integration are described and physical models are developed to describe the behavior of the devices. These models are used to develop array-scale simulations in support of neuromorphic computing approaches. Several signal processing algorithms are adapted for acceleration using arrays of resistive switches. Both simulation and experimental results are reported. Finally, guiding principles and proposals for future work are discussed.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116743/1/sheridp_1.pd

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Vlsi Implementation of Olfactory Cortex Model

This thesis attempts to implement the building blocks required for the realization of the biologically motivated olfactory neural model in silicon as the special purpose hardware. The olfactory model is originally developed by R. Granger, G. Lynch, and Ambros-Ingerson. CMOS analog integrated circuits were used for this purpose. All of the building blocks were fabricated using the MOSIS service and tested at our site. The results of this study can be used to realize a system level integration of the olfactory model.Electrical Engineerin