Sökning: "beräkningsneurovetenskap"

Hittade 5 uppsatser innehållade ordet beräkningsneurovetenskap.

1. 1. Evaluating the Effects of Neural Noise in the Multidigraph Learning Rule

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustav Bressler; Sigvard Dackevall; [2023]
   Nyckelord :;

   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

3. 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)

   Författare :Yu Qin; [2023]
   Nyckelord :Spinal Cord Injury; Epidural Electrical Stimulation; Computational Neuroscience; Finite Element Model; Artificial Intelligence; Optimal Transport; EMG; Muscle Activation; Ryggmärgsskada; Epidural Elektrisk Stimulering; Beräkningsneurovetenskap; Finita Elementmodellen; Artificiell Intelligens; Optimal Transport; EMG; Muskelaktivering;

   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

4. 3. Modelling synaptic rewiring in brain-like neural networks for representation learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Kunal Bhatnagar; [2023]
   Nyckelord :Adaptive Sparsity; Computational Neuroscience; Rewiring; Structural Plasticity; Brain-like Computing; Neural Networks; Hebbian Learning; Adaptiv gleshet; beräkningsneurovetenskap; omkoppling; strukturell plasticitet; Hjärnliknande beräkning; Neurala Nätverk; Hebbskt lärande;

   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

5. 4. Hierarchical Clustering using Brain-like Recurrent Attractor Neural Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Hannah Kühn; [2023]
   Nyckelord :Hierarchical Clustering; Attractor Network; Recurrent Neural Network; Brain-like computing; Hierarkisk klustring; Anlockningsnätverk; Återkommande neurala nätverk; Hjärnliknande databehandling;

   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

7. 5. Normalization in a cortical hypercolumn : The modulatory effects of a highly structured recurrent spiking neural network

   Master-uppsats, KTH/Beräkningsbiologi, CB

   Författare :Ylva Jansson; [2014]
   Nyckelord :Normalization; divisive normalization; computational neuroscience; cortical hypercolumn; short-term synaptic depression; gain control; canonical neural computations; Normalisering; kortikal hyperkolumn; beräkningsneurovetenskap; korttidsdepression; neural division; kanoniska neurala beräkningar;

   Sammanfattning : Normalization is important for a large range of phenomena in biological neural systems such as light adaptation in the retina, context dependent decision making and probabilistic inference. In a normalizing circuit the activity of one neuron/-group of neurons is divisively rescaled in relation to the activity of other neurons/­­groups. LÄS MER