Sökning: "Computational Neuroscience"
Visar resultat 1 - 5 av 18 uppsatser innehållade orden Computational Neuroscience.
1. Evaluating the Effects of Neural Noise in the Multidigraph Learning Rule
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : There exists a knowledge gap in the field of Computational Neuroscience, where many learning models for neural networks fail to take into account the influence of neural noise. The purpose of this thesis was to address this knowledge gap by investigating the robustness of the Multidigraph learning rule (MDGL) when exposed to two kinds of neural noise: external noise and internal noise. LÄS MER
2. The data-driven CyberSpine : Modeling the Epidural Electrical Stimulation using Finite Element Model and Artificial Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Every year, 250,000 people worldwide suffer a spinal cord injury (SCI) that leaves them with chronic paraplegia - permanent loss of ability to move their legs. SCI interrupts axons passing along the spinal cord, thereby isolating motor neurons from brain inputs. To date, there are no effective treatments that can reconnect these interrupted axons. 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. Hierarchical Clustering using Brain-like Recurrent Attractor Neural Networks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Hierarchical clustering is a family of machine learning methods that has many applications, amongst other data science and data mining. This thesis belongs to the research area of brain-like computing and introduces a novel approach to hierarchical clustering using a brain-like recurrent neural network. LÄS MER
5. Spiking Reinforcement Learning for Robust Robot Control Under Varying Operating Conditions
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Over the last few years, deep reinforcement learning (RL) has gained increasing popularity for its successful application to a variety of complex control and decision-making tasks. As the demand for deep RL algorithms deployed in challenging real-world environments grows, their robustness towards uncertainty, disturbances and perturbations of the environment becomes more and more important. LÄS MER
