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Predictable internal brain dynamics in EEG and its relation to conscious states.

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus

A neural network predictor for time series data. A multi-layer neural network consisting of k = 10 input units, 10 hidden units, and one output unit was trained. The input values were taken from k consecutive values from a time series leading up to time t (time step t − k + 1 to t), and the target output value set to the value at time step t + 1. The network is activated in a feed-forward manner, through the connections, and the error in the output vs. the target value back propagated to adjust the connection weights. See the text for more details.
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Figure 2: A neural network predictor for time series data. A multi-layer neural network consisting of k = 10 input units, 10 hidden units, and one output unit was trained. The input values were taken from k consecutive values from a time series leading up to time t (time step t − k + 1 to t), and the target output value set to the value at time step t + 1. The network is activated in a feed-forward manner, through the connections, and the error in the output vs. the target value back propagated to adjust the connection weights. See the text for more details.

Mentions: The specific method we used was based on our earlier work reported in Kwon and Choe (2008), with one minor difference that exact error values were measured in this study instead of using the adaptive error rates. We trained a multi-layer neural network where the inputs are k past data points (k = 10 in our case) and the target output is the current data point in each EEG time series (Figure 2). Each EEG time series was traversed with a window of size 10 to construct the input set and the value immediately following the time window was used as the target value, thus forming the data set.


Predictable internal brain dynamics in EEG and its relation to conscious states.

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

A neural network predictor for time series data. A multi-layer neural network consisting of k = 10 input units, 10 hidden units, and one output unit was trained. The input values were taken from k consecutive values from a time series leading up to time t (time step t − k + 1 to t), and the target output value set to the value at time step t + 1. The network is activated in a feed-forward manner, through the connections, and the error in the output vs. the target value back propagated to adjust the connection weights. See the text for more details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043151&req=5

Figure 2: A neural network predictor for time series data. A multi-layer neural network consisting of k = 10 input units, 10 hidden units, and one output unit was trained. The input values were taken from k consecutive values from a time series leading up to time t (time step t − k + 1 to t), and the target output value set to the value at time step t + 1. The network is activated in a feed-forward manner, through the connections, and the error in the output vs. the target value back propagated to adjust the connection weights. See the text for more details.
Mentions: The specific method we used was based on our earlier work reported in Kwon and Choe (2008), with one minor difference that exact error values were measured in this study instead of using the adaptive error rates. We trained a multi-layer neural network where the inputs are k past data points (k = 10 in our case) and the target output is the current data point in each EEG time series (Figure 2). Each EEG time series was traversed with a window of size 10 to construct the input set and the value immediately following the time window was used as the target value, thus forming the data set.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

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.

No MeSH data available.


Related in: MedlinePlus