Print Email Facebook Twitter Digital Neuron Cells for Highly Parallel Cognitive Systems Title Digital Neuron Cells for Highly Parallel Cognitive Systems Author Lin, Haipeng (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Microelectronics) Contributor Van Leuken, Rene (mentor) Galuzzi, Carlo (graduation committee) Van Genderen, Arjan (graduation committee) Zjajo, Amir (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2017-08-31 Abstract The high level of realism of spiking neuron networks and their complexity require a considerable computational resources limiting the size of the realized networks. Consequently, the main challenge in building complex and biologically accurate spiking neuron network is largely set by the high computational and data transfer demands. In this thesis, I implement several efficient models of the spiking neuron with characteristics such as axon conduction delays and spike timing-dependent plasticity in a real-time data-flow learning network. With the performance analysis, the trade-offs between the biophysical accuracy and computation complexity are defined for the different models. The experimental results indicate that the proposed real-time data-flow learning network architecture allows the capacity of over 1,188 (max.6,300, depending on the model complexity) biophysically accurate neurons in a single FPGA device. Subject digital spiking neuron cellsneuron networklearning networkreal-time data-flow architecture To reference this document use: http://resolver.tudelft.nl/uuid:51d9827a-013a-4c7f-be0c-eceb0733b027 Part of collection Student theses Document type master thesis Rights © 2017 Haipeng Lin Files PDF MSc_LinHaipeng.pdf 2.21 MB Close viewer /islandora/object/uuid:51d9827a-013a-4c7f-be0c-eceb0733b027/datastream/OBJ/view