Researchers have gained a first insight into how the brain structures higher-level information. By extracting and analysing data from a neural network of grid cells, they found that the collective neural activity is shaped like the surface of a doughnut

Spontaneous grid cell activity aligns to our external world

So, what is the significance of seeing that the network activity of grid cells is always unfolding on the surface of a doughnut?

“Only one theoretical model in neuroscience has predicted what the activity of grid cells should be like regardless of the animal’s state, the CAN theory. These findings tell us something about the way the network of neurons is connected. The doughnut exists in the connectivity between the cells,” Edvard Moser said.

CAN theory proposes that grid cells with similar functions, cells that are active at nearby places in space, are strongly connected, in a reinforcing way. Cells that are active at distant locations are weakly connected in a mutually inhibitory way. From this follows two premises: (1) If this theory is correct, the only way to get hexagonal grid cell patterns from single cells, is if the joint network activity moves along on the surface of a doughnut. (2) The activity structure is a result of the brain’s intrinsic wiring rules. Thus, the doughnut remains, regardless of where the animal is or what the animal is doing, whether it is using the grid cells to navigate its external environment or not.

The results show that the grid cell pattern is created internally by the connections between grid cells and is not created by the input from the sensory systems, from the outside.


“This study demonstrates a new approach to doing neuroscience, which I think is going to see more and more usage as time goes on. A methodology for extracting dynamics from network-wide neural activity as a starting point for analysis, and just looking at what’s there. Finding structure in the data that is intrinsic to the cell populations themselves,” he said.

“The study is one of the first examples of cortical computation at a network level observed in behaving animals. We tested one of a very few existing theoretical models of brain computations, and verified one of its major and uniquely selective predictions – thus making it quite likely that this is the way that neural networks work. Probably not only for the space system, but also for a number of other brain systems that support a number of brain functions,” Moser said.

“We can now explore other parts of the brain, where we expect similar tricks are used but where the underlying features might be more abstract, like emotions or social behaviour,” Hermansen said.

“It’s a promising approach for uncovering signals in the brain which may be hidden from us because they don’t relate to anything that we can see or measure externally. This may be particularly relevant for understanding the brain circuits involved in higher cognition, which deal with highly abstract information that is difficult for us to make sense of,” Gardner said.


Study: Toroidal topology of population activity in grid cells

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