If you recall this sentence a few seconds from now, you can thank a simple network of neurons for the experience. That is the conclusions of researchers who have built a computer model that can reproduce an important aspect of short-term memory.
The key, they say, is that the neurons form a "small world" network. Small-world networks are surprisingly common. Human social networks, for example, famously connect any two people on Earth - or any actor to Kevin Bacon - in six steps or less.
Properties like this have made them the focus of much research. It turns out that regardless of the size of these networks, any two points within them are always linked by only a small number of steps.
Now it looks as if working memory, which allows short-term recall of fleetingly remembered information such as phone numbers, relies on the same property. This type of memory resides in an area at the front of the brain called the prefrontal cortex, which is involved in learning, planning and many higher cognitive functions.
The late Patricia Goldman-Rakic of Yale University School of Medicine and others have suggested that neurons in this region might be able to switch between two stable states, a property called bistability. When storing a memory, neurons would participate in self-sustaining bursts of electrical activity. When not involved in memory storage, the neurons become quiet.
Just how the brain controls this behaviour has puzzled neuroscientists. The prefrontal cortex is home to a wide variety of neurons with different properties, such as response to different chemical signals and the ability to activate or inhibit neighbouring neurons. So researchers have resorted to equally complex computer models.
Now a team at Northwestern University in Evanston, Illinois, has reproduced the behaviour with the simplest of networks - by connecting it together to form a small world. "The philosophical conclusion is that connectivity matters," says team member Sara Solla. "Our model uses only a simple caricature of neurons, yet this network shows this working memory-like behaviour."
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