Sökning: "Bayesian Confidence Propagation Neural Network"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Bayesian Confidence Propagation Neural Network.

1. 1. Modelling Long Term Memory in the Bayesian Confidence Neural Network Model

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

   Författare :Charu Karn; Samir Samahunov; [2023]
   Nyckelord :;

   Sammanfattning : Memory is a fascinating and complex part of human life. Understanding memory and simulating itthrough modelling can help society take steps towards understanding health issues such asAlzheimer's, dementia and amnesia. LÄS MER

3. 2. Exploring Column Update Elimination Optimization for Spike-Timing-Dependent Plasticity Learning Rule

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

   Författare :Ojasvi Singh; [2022]
   Nyckelord :Spike-Timing Dependent Plasticity; neuromorphic computing; Hebbian Learning; Spiking Neural Networks; memory optimization.; Spike-Timing Beroende Plasticitet; neuromorfisk beräkning; Hebbiansk inlärning; Spiking Neural Networks; Minnes optimering;

   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

4. 3. Role of Context in Episodic Memory : A Bayesian-Hebbian Neural Network Model of Episodic Recall

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

   Författare :Rohan Raj; [2022]
   Nyckelord :episodic memory; long-term memory; Bayesian Confidence Propagation Neural Network; synaptic plasticity; plasticity modulation; computational neuroscience;

   Sammanfattning : Episodic memory forms a fundamental aspect of human memory that accounts for the storage of events as well as the spatio-temporal relations between events during a lifetime. These spatio-temporal relations in which episodes are embedded can be understood as their contexts. Contexts play a crucial role in episodic memory retrieval. LÄS MER

5. 4. The Impact of Selective Plasticity Modulationon Simulated Long Term Memory

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

   Författare :Silvia Barrett; Alicia Palmér; [2021]
   Nyckelord :BCPNN; Long-term memory; Computational brain modeling; Reminiscence bump; Plasticity modulation;

   Sammanfattning : Understanding the brain and its functions is achallenging undertaking. To facilitate this work, brain-inspiredtechnology may be used to examine cognitive phenomena to acertain extent, by replacing real biological brains with simulations. LÄS MER

7. 5. Unsupervised learning of data representations in brain-like neural networks

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

   Författare :Arian Javdan; [2021]
   Nyckelord :;

   Sammanfattning : Recently, there has been a growing interest in brain-plausible neural networks that closely resemble the brain’s structure. However, conventional networks do not make good models for the brain since these connections are modelled differently, hence the interest in brain-plausible networks. LÄS MER