Simon Kern- MSc Artificial Intelligence
- PhD Student at Central Institute of Mental Health
Simon Kern
- MSc Artificial Intelligence
- PhD Student at Central Institute of Mental Health
PhD candidate @ CIMH Mannheim with Gordon Feld
About
18
Publications
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Citations
Introduction
Current institution
Additional affiliations
December 2020 - present
Education
September 2015 - July 2017
September 2011 - July 2015
Publications
Publications (18)
Abstract Background A lucid dream is a dream in which one is aware of the fact that one is dreaming. Various cognitive and technical methods exist to induce lucid dreaming, most of which show only little success when tested scientifically. Until now, only few studies have dealt with inducing lucid dreaming by supplements, with, however, promising r...
There have been many studies that have shown that waking life is mirrored in dream content. In a previous study, a relation between time spent on sports-related tasks and sport-related dreams has been found showing that sports students dream of sports more often than psychology students. In this study the aim was to test whether this trend is also...
Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alterna...
Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alterna...
Declarative memory retrieval is thought to involve reinstatement of neuronal activity patterns elicited and encoded during a prior learning episode. Furthermore, it is suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or, alterna...
Rewards paid out for successful retrieval motivate the formation of long-term memory. However, it has been argued that the Motivated Learning Task does not measure reward effects on memory strength but decision-making during retrieval. We report three large-scale online experiments in healthy participants ( N = 200, N = 205, N = 187) that inform th...
Acute exercise has been shown to affect long‐term memory and sleep. However, it is unclear whether exercise‐induced changes in sleep architecture are associated with enhanced memory. Recently, it has been shown that exercise followed by a nap improved declarative memory. Whether these effects transfer to night sleep and other memory domains has not...
Declarative memory retrieval is thought to involve reinstatement of the neuronal activity patterns elicited and encoded during a prior learning episode. Recently, it has been suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or,...
Declarative memory retrieval is thought to involve reinstatement of the neuronal activity patterns elicited and encoded during a prior learning episode. Recently, it has been suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or,...
Declarative memory retrieval is thought to involve reinstatement of the neuronal activity patterns elicited and encoded during a prior learning episode. Recently, it has been suggested that two mechanisms operate during reinstatement, dependent on task demands: individual memory items can be reactivated simultaneously as a clustered occurrence or,...
Rewards paid out for successful retrieval motivate the formation of long-term memory. However, it has been argued that the Motivated Learning Task does not measure reward effects on memory strength but decision-making during retrieval. We report three large-scale online experiments in healthy participants (N = 201, N = 205, N = 187) that inform thi...
Dreams in which the dreamer is aware of the dream state (lucid dreams, LD) are difficult to induce in naïve subjects in-laboratory. Recently, Stumbrys and Erlacher (2014) used a combination of existing induction techniques together with a self-developed experiment protocol and achieved comparatively high LD induction rates. In this study, we simpli...
Several methods exist to help novices experience a lucid dream, i.e. a dream in which the dreamer is aware of the dream state. However, when validated polysomnographically in a sleep laboratory, these techniques proved to be only of limited success in beginners. Recently, Stumbrys and Erlacher developed a protocol, which combines several lucid drea...
The division of sleep into different stages using EEG signals is a commonplace practice in sleep laboratories and an indispensable tool for clinicians and researchers. Despite the advances in artificial intelligence, the sleep stage scoring process is in most cases still performed manually. As the scoring process is tedious and time-consuming, its...
Several methods exist to help novices experience a lucid dream, i.e. a dream in which the dreamer is aware of the dream state. However, when validated polysomnographically in a sleep laboratory, these techniques proved to be only of limited success in beginners. Recently, Stumbrys and Erlacher developed a protocol, which combines several lucid drea...
Questions
Question (1)
I have a list of 100 cases with age and gender, and a list of 150 controls.
I want to match the cases to controls based on gender and age +-3 years.
Currently I'm using a greedy algorithm that tries to find first exact matches, then matches +-1year, +-2year, +-3years. However, this is not optimal way, and I could potentially get more matches with a non-greedy matching within the constrained +-3 years. I would prefer to optimize for maximum number of matches within +-3y and not optimize for minimum age difference.
Which algorithm/package can I use for this problem? (preferably in Python, but must be open-source)
