There are myriad benefits of hunkering down for a good night's sleep. Adequate rest has a wide range of positive effects ranging from enhanced problem solving and reasoning skills to reduced stress and irritability. Now, new research suggests that artificial brains may also benefit from restorative rest.
These artificial systems were inspired by the neurological connectivity of the human brain. Imagine a sprawling, layered network of interconnected nodes communicating with one another; a wave of information passing through the structure via a series of electronic firings. Each of these nodes, or "neurons," within the network is fed its own supply of data and signals to transfer data to the next layer of nodes. Over time, the neural network adjusts the unique interactions between these neurons to improve its problem-solving capabilities. This roadmap of neural combinations is slowly fine-tuned until the system develops an optimal strategy for a given task.
For this study, the researchers focused on spiking neural networks that function differently than standard artificial neural networks. These computing systems are stylized more closely to the neurological circuitry of the human brain, with neurons generating a signal after receiving a number of input signals. Scientists are still learning how to train spiking neural networks, as these systems require entirely different methods than typical artificial neural networks.
The researchers found the spiking neural network became increasingly unstable after extended periods of unsupervised learning. In an attempt to stabilize the networks, the team implemented various types of noise, with Gaussian noise having the best results. The research team postulates that this is because Gaussian noise may mimic the inputs biological neurons receive throughout slow-wave sleep.
"Why is slow-wave sleep so indispensable?" said senior author of the study Garrett Kenyon. "Our results make the surprising prediction that slow-wave sleep may be essential for any spiking neural network, or indeed any organism with a nervous system, to be able to learn from its environment."