Essays about: "synaptic plasticity"
Showing result 1 - 5 of 16 essays containing the words synaptic plasticity.
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1. Harnessing the Power of Voltage-dependent Synaptic Plasticity (VDSP): A Novel Paradigm for Unsupervised Learning in Neuromorphic Systems
University essay from Luleå tekniska universitet/DatavetenskapAbstract : .... READ MORE
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2. Modelling synaptic rewiring in brain-like neural networks for representation learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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3. Exploring Column Update Elimination Optimization for Spike-Timing-Dependent Plasticity Learning Rule
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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4. Dynamic synapses in neural information processing : Examining the influence of short-term synaptic plasticity on neural coding
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Short-term synaptic plasticity (STP) is a phenomenon that has been closely associated with how neurons communicate with each other. I study communication between neurons tied to synapses endowed with short-term plasticity (dynamic synapses). READ MORE
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5. Exploring the column elimination optimization in LIF-STDP networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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