So why do neurons respond in this remarkable way? A new study by Professor Jeff Bowers and colleagues at the University of Bristol argues that highly selective neural representations are well suited to co-activating multiple things, such as words, objects and faces, at the same time in short-term memory.
The researchers trained an artificial neural network to remember words in short-term memory. Like a brain, the network was composed of a set of interconnected units that activated in response to inputs; the network ‘learnt’ by changing the strength of connections between units. The researchers then recorded the activation of the units in response to a number of different words.
When the network was trained to store one word at a time in short-term memory, it learned highly distributed codes such that each unit responded to many different words. However, when it was trained to store multiple words at the same time in short-term memory it learned highly selective (‘grandmother cell’) units – that is, after training, single units responded to one word but not any other. This is much like the neurons in the cortex that respond to one face amongst many.