Sökning: "Hebbian learning"
Visar resultat 1 - 5 av 9 uppsatser innehållade orden Hebbian learning.
1. Comparison of Hebbian Learning and Backpropagation for Image Classification in Convolutional Neural Networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Current commonly used image recognition convolutional neural networks share some similarities with the human brain. However, the differences are many and the well established backpropagation learning algorithm is not biologically plausible. LÄS MER
2. Regression with Bayesian Confidence Propagating Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Bayesian Confidence Propagating Neural Networks (BCPNNs) are biologically inspired artificial neural networks. These networks have been modeled to account for brain-like aspects such as modular architecture, divisive normalization, sparse connectivity, and Hebbian learning. LÄS MER
3. Modelling synaptic rewiring in brain-like neural networks for representation learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This research investigated the concept of a sparsity method inspired by the principles of structural plasticity in the brain in order to create a sparse model of the Bayesian Confidence Propagation Neural Networks (BCPNN) during the training phase. This was done by extending the structural plasticity in the implementation of the BCPNN. LÄS MER
4. Comparison of Hebbian Learning and Backpropagation for Image Classification in Convolutional Neural Networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Current commonly used image recognition convolutional neural networks sharesome similarities with the human brain. However, the differences are many and the wellestablished backpropagation learning algorithm is not biologically plausible. LÄS MER
5. Exploring Column Update Elimination Optimization for Spike-Timing-Dependent Plasticity Learning Rule
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Hebbian learning based neural network learning rules when implemented on hardware, store their synaptic weights in the form of a two-dimensional matrix. The storage of synaptic weights demands large memory bandwidth and storage. LÄS MER