Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"Synaptic weights"”.
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An hybrid architecture for multi-layer feed-forward neural networks.
… hardware realization; easy long term storage of synaptic weights and massive interconnections, are addressed and solved by the mixed signal architecture for implementation of feed-forward neural network. The hybrid architecture is analyzed and implemented in 0.5 micron CMOS technology. The analog …
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Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications
… use</p><p>learning algorithms which adjust the synaptic weights according to an update rule.</p><p>In most cases the weights are modied directly. In potential applications such as</p><p>driving plasticity in neural culture (in-vitro) and training neuromorphic chips the</p><p>directly …
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Universal and succinct source coding of deep neural networks
… of deep feedforward networks with synaptic weights drawn from discrete sets, and directly performing inference without full decompression. The basic insight that allows less rate than naive approaches is recognizing that the bipartite graph layers of feedforward networks have a kind …
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In-the-loop training of a VLSI implementation of a smart sensor with low resolution programmable digital weights.
… training of neural networks with low resolution synaptic weights is developed in this thesis. This research was motivated by the need to train an intelligent sensor that had been designed and fabricated with low resolution weights in order to meet constraints that were imposed upon the designers. …
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Neural Networks for Control of Artificial Life Form
… the artificial life form is stored as value of weights in the synaptic connections of neurons. STDP is the learning rule implemented to the network in this project. STDP adjusts the synaptic weights according to the precise timing of pre and postsynaptic spikes. The artificial life form itself …
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Analog On-chip Training and Inference with Non-volatile Memory Devices
… matrix-vector multiplications by encoding synaptic weights into the conductance of nonvolatile memory devices. These devices are structured into crossbar arrays. To explore the potential of non-volatile memory devices in AIMC, investigations involve simulating crossbar array operations …
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Stepwise Evolutionary Training Strategies for Hardware Neural Networks
… algorithms to efficiently optimize the synaptic weights of a fast mixed-signal neural network chip. The training strategy is tested on a set of nine well-known classification benchmarks: the breast cancer, diabetes, heart disease, liver disorder, iris plant, wine, glass, E.coli, and …
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Long Term Plasticity Induced by Intracellular Tetanization in the Rat Auditory Cortex
<p> <p>Associative Hebbian-type synaptic plasticity underlies the mechanisms of learning and memory, but it leads to runaway dynamics of synaptic weights and lacks mechanisms for synaptic competition. Heterosynaptic plasticity may solve these problems by complementing plasticity at synapses that …
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Two-Phase Buck Converter Optimize by Echo State Network
… neural is similar with the human neural, and the synaptic weights can treat as the connection between two nodes. Reservoir computing can be seen as an extension of the neural network since it is a framework for computation. Echo State Network(ESN) is one of the major types of reservoir computing, …
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Network Structures Arising from Spike-Timing Dependent Plasticity
… dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains, and organizes neural circuits in functionally useful ways. n this dissertation, I study the structures arising from STDP in a population of synapses with …
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On the 3D point cloud for human-pose estimation
… we have created a part-based approach to learn synaptic weights by decomposing a neural network into parts. Based on the concept of distributed representation, the NN-AMM is further modified into a scalable neural network called NND-AMM. A neural-network-based system is then built by using VISH …
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Un modelo neuronal basado en la metaplasticidad para la clasificación de objetos en señales 1-d y 2-d
… that produces greater modifications in the synaptic weights with less frequent patterns than frequent patterns, as a way of extracting more information from the former than from the latter.