Essays about: "Hebbisk inlärning"

Found 3 essays containing the words Hebbisk inlärning.

  1. 1. Regression with Bayesian Confidence Propagating Neural Networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Raghav Rajendran Bongole; [2023]
    Keywords : Machine Learning; Neural Networks; Brain-like computing; Bayesian Confidence Propagating Neural Networks; Maskininlärning; neurala nätverk; hjärnliknande datorer; Bayesian Förtroendespridande neurala nätverk;

    Abstract : 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. READ MORE

  3. 2. Exploring the column elimination optimization in LIF-STDP networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Mingda Sun; [2022]
    Keywords : SpikingNeuralNetwork SNN ; neuromorphiccomputing; memoryoptimization; Hebbian learning; Spike-timing-depend plasticity STDP learning; Spiking Neural Network SNN ; neuromorphic computing; minnesoptimering; Hebbisk inlärning; spike-timing-depend plasticity STDP inlärning;

    Abstract : Spiking neural networks using Leaky-Integrate-and-Fire (LIF) neurons and Spike-timing-depend Plasticity (STDP) learning, are commonly used as more biological possible networks. Compare to DNNs and RNNs, the LIF-STDP networks are models which are closer to the biological cortex. READ MORE

  4. 3. Sequence Disambiguation in a Brain-Like Recurrent Neural Network with Local Associative Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ivan Pavlovich Zelenin; Mina Beigi Boroujeni; [2020]
    Keywords : ;

    Abstract : The learning of sequences is a fundamental ability of biological networks. While there are many artificial networks that are able to successfully learn sequences, it is of particular interest to study networks that attempt to do so in a manner similar to how it is done by a human brain. READ MORE