Working on lab rats, an international team led by Nobel laureates and including neuroscientists from the Hebrew University of Jerusalem (HUJI) has helped to reveal mechanisms of nerve cells through which the brain represents the location of the animal in their surroundings. Their findings are the clue to all high-level cognitive functions of the brain.”
The study has just been published in the prestigious journal Nature under the title “Toroidal topology of population activity in grid cells.” Among the team members was Prof. Yoram Burak of HUJI’s Institute of Physics and the Edmond and Lily Safra Center (ELSC) for Neuroscience. They helped reveal mechanisms by which nerve cells called grid cells represent the position of the animal in their surroundings. Burak’s group has been investigating how neural circuits in the brain perform biologically relevant computations, such as sensory inference, maintenance of short-term memory and the generation of motor output.
This study presents a new approach to doing neuroscience that will be used more and more in the future. Neuroscientists can now explore other parts of the brain, including emotions and social behavior. It’s a promising approach for uncovering signals in the brain that may be hidden because they don’t relate to anything that we can see or measure externally. This may be particularly relevant, the team said, for understanding the brain circuits involved in higher cognition that deal with highly abstract information and is difficult for us to make sense out of it.
By extracting and analyzing data from a neural network of the brain’s grid cells, the researchers found that the collective neural activity is shaped like the surface of a doughnut. “High-level brain functions result from the orchestration of activity among many thousands of neurons in neural networks. For grid cells, these neural network conversations result in our understanding of location, our capacity to navigate, and our mental maps,” said neuroscience Prof. Edvard Moser, who is co-director of the Norwegian University of Science and Technology’s and (NTNU) Kavli Institute for Systems Neuroscience.
“This discovery provides one of the first insights into how brain cells operate collectively, as a society. It provides an unprecedented glimpse into how large networks of neurons produce properties that cannot be inferred from the activities of single cells. These collective codes are the clue to all high-level cognitive functions of the brain,” he noted.
In neuroscience, theory and experiment go together like map and terrain. Without a map, you’d be lost in the unknown. Without access to the neural landscapes, you’re stuck just speculating. One of the most promising neuroscience theories in the last 50 years predicts how neural networks in the brain organize information. It proposes that neural networks are self-organized, and that the activity is defined not by sensory or motoric input, but by the specific way cells in the network are connected.
This theory is called continuous attractor networks (CAN), and it has never before been tested. Testing it would require analyzing the simultaneous recorded activity from hundreds or thousands of cells in the same brain network, while the animal is actively performing different tasks. This has not been possible – until now.
The first is a recently developed super-tool called Neuropixels, which provides researchers access to neural activity from hundreds to thousands of cells, while the second is the most well understood high-level brain function, namely the network of grid cells in the entorhinal cortex that generates the brain’s GPS. NTNU’s Department of Mathematical Sciences has developed the third component, advanced topological methods. Last but not least is the most promising CAN theory, pioneered at HUJI’s ELSC.
The researchers set up three experiments with conditions that put the network’s intrinsic behavior to the test. In the first experiment, the rat freely explores an open landscape. In this environment, single cells usually produce perfectly hexagonal grid patterns. In the second experiment, the rat runs along a wagon wheel-shaped maze. These types of linear paths are known to distort grid patterns. In the third experiment, the rat rests in the REM (deep) sleep stage and in slow-wave sleep stage. REM is short for rapid eye movements and is known to be the sleep stage where dreams occur. In the slow-wave sleep stage, the brain doesn’t receive motoric or sensory input from the body or the environment, nor does it simulate sensory experiences such as happens with dreams in REM sleep.
This last experiment would turn out to be the real test for the theories, because it would either support or reject one of the strongest predictions from the CAN theory of grid cells. All experiments were done by using Neuropixels probes to extract raw brain data from hundreds of grid cells in the same neural network.
“What we found was that the joint activity of the grid cell network resided on and moved along the surface of a torus, a doughnut. For the awake rat, the activity moved across the doughnut in synchrony with the animal’s movement in the room. At any given time, we could describe the rat’s network activity by coordinates on that doughnut,” said Moser.
“It was remarkable to observe the rigidity of grid cell representations across various conditions, including two different stages of sleep, where this result was far from obvious or expected,” said Burak.
“First and foremost, this study teaches us something about what neural networks in the brain can do. For more than a decade, theoreticians like myself developed theories that attempt to explain grid cell activity. Due to the present study, we can now confirm key predictions that were made by these theories. This is very exciting for me personally,” Burak added.
“It was one thing to find the torus in the animals when they were moving around in the box – and then we looked when they were sleeping and I couldn’t believe it. Finding the torus during sleep surprised me the most but that was after already being surprised by the maze — the maze was supposed to disrupt the regularity but there was the torus. It’s always a torus, even when we think it might not be”, said Prof. Benjamin Dunn, of NTNU’s department of mathematical sciences.
“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 networkwide 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,” the researchers 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 behavior.”