This ramping pattern corresponded with a proposed mechanism of neural computation called an attractor network. The brain is made up of vast numbers of neurons connected together into networks, and the attractor network is a theoretical model of how patterns of connected neurons can give rise to brain activity by collectively working together. The attractor network theory was first proposed 30 years ago by John Hopfield, Princeton's Howard A. Prior Professor in the Life Sciences, Emeritus.
The team found that their measurements of grid cell activity corresponded with the attractor network model but not a competing theory, the oscillatory interference model. This competing theory proposed that grid cells use rhythmic activity patterns, or oscillations, which can be thought of as many fast clocks ticking in synchrony, to calculate where animals are located. Although the Princeton researchers detected rhythmic activity inside most neurons, the activity patterns did not appear to participate in position calculations.
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