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Search Results: 1 - 2 of 2 matches for " Neuroevolution "
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Predictable internal brain dynamics in EEG and its relation to conscious states
Jaewook Yoo,Jaerock Kwon,Yoonsuck Choe
Frontiers in Neurorobotics , 2014, DOI: 10.3389/fnbot.2014.00018
Abstract: Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.
Neuroevolution Mechanism for Hidden Markov Model
Nabil M. Hewahi
Brain. Broad Research in Artificial Intelligence and Neuroscience , 2011,
Abstract: Hidden Markov Model (HMM) is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
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