This post starts with the last paragraph I wrote in a post on Attention and Learning:
The aim of this blog post was to share ideas about attention and learning and to support explorations in personalised teaching, coaching and learning. Fortunately I did not lose a lot of sleep over this post. Researching attention and learning is a wonderful way to ensure high quality of sleep. But just when it is safe to go to bed you might want to think about the attention and learning possibilities of sleep, dreams and nightmares. Richard Stickgold’s work and Antti Revonsuo’s research open up fascinating opportunities to explore the learning possibilities of dreams and nightmares.
One of my Twitter contacts shared with me a link to ScienceDaily and an article titled The Mathematics Behind a Good Night’s Sleep. Mark Holmes and Lisa Rogers at Rensselaer Polytechnic Institute are using mathematical approaches to sleep research. The ScienceDaily article quotes Mark Holmes: “We wanted to create a very interdisciplinary tool to understand the sleep-wake cycle. We based the model on the best and most recent biological findings developed by neurobiologists on the various phases of the cycle and built our mathematical equations from that foundation. This has created a model that is both mathematically and biologically accurate and useful to a variety of scientists”.
A press release from Rensselaer reports that:
- The interdisciplinary model is based on the best and most recent biological findings developed by neurobiologists on the various phases of the cycle and built our mathematical equations from that foundation. This has created a model that is both mathematically and biologically accurate and useful to a variety of scientists.
- Lisa Rogers spent last summer with neurobiologists at Harvard Medical School to learn about the biology of the brain. She investigated the role of specific neurotransmitters within the brain at various points in the sleep-wake cycle. This work trained Lisa to read electroencephalography (EEG) and electromyography (EMG) data on the brainwaves and muscle activity that occur during the sleep cycle. This biologic data forms the foundation of their mathematic calculations.
- An 11-equation model of the sleep-wake cycle was developed. The research team is working to input differential equations into an easy-to-use graphic computer model for biologists and doctors to study.
- Lisa Rogers will continue her work on the program after receiving her doctoral degree in applied mathematics from Rensselaer. Her work on the mathematics of the sleep-wake cycle has earned her a postdoctoral research fellowship from the National Science Foundation (NSF). With the fellowship, she will continue her work at New York University and begin to incorporate other aspects of the sleep-wake cycle in the model such as the impacts of circadian rhythms.
A newsletter reporting Lisa’s postdoctoral fellowship notes that:
The human sleep-wake system is a widely researched yet still only partially understood frontier in both the biological and mathematical sciences. Even though extensive measurements have been made of brainwave activity generated during sleep, and much progress has been made on the anatomy of the brain and it’s neurotransmitters, even the basic questions associated with sleep as yet have no definite answers. For example: Why do we sleep? Do all animals sleep? Is the sleep function invariable across species? Should sleep be viewed as a recovery process? Does sleep contribute to brain function by reversing some consequences of wakefulness? Alternatively, is sleep a distinct state, not thought to directly contribute to waking brain function? There is a wide variation of sleep patterns within mammalian species, and thus it is important to stay focused on one particular system. In this case, we are focusing on the human system and we are constructing a neurochemically based mathematical system representing the essential steps in the dynamics of the human sleep-wake cycle.
Mark Holmes and Lisa Rogers’ work has received a great deal of publicity since the publication of the Rensselaer Polytechnic Institute release. Their work has enormous relevance to those working with athletes and underscores the interdisciplinary dimensions of performance. The aim of this post is to add resources to the applied research in athlete (and coach) behaviour and to make an explicit link with Richard Stickgold’s work and Antti Revonsuo’s research.
I am grateful to Matthias Melcher for an introduction to Daniel Erlacher‘s work in the Institut für Sport und Sportwissenschaft at the University of Heidelberg. This is a link to Daniel’s thesis (2005) Motor Learning in Lucid Dreams. This a link to research directions in Daniel’s work (and this a translation from German of his research directions). Daniel is researching: memory consolidation during sleep, sleep before competition, motor activity in REM dreams, and better sleep following physical activity.