Our thoughts are specified by our knowledge and plans, yet our cognition can also be fast and flexible in handling new information. How does the well-controlled and yet highly nimble nature of cognition emerge from the brain's anatomy of billions of neurons and circuits? A new study by researchers in The Picower Institute for Learning and Memory at MIT provides new evidence from tests in animals that the answer might be a theory called "Spatial Computing."
First proposed in 2023 by Picower Professor Earl K. Miller and colleagues Mikael Lundqvist and Pawel Herman, Spatial Computing theory explains how neurons in the prefrontal cortex can be organized on the fly into a functional group capable of carrying out the information processing required by a cognitive task. Moreover, it allows for neurons to participate in multiple such groups, as years of experiments have shown that many prefrontal neurons can indeed participate in multiple tasks at once. The basic idea of the theory is that the brain recruits and organizes ad hoc "task forces" of neurons by using "alpha" and "beta" frequency brain waves (about 10-30 Hz) to apply control signals to physical patches of the prefrontal cortex. Rather than having to rewire themselves into new physical circuits every time a new task must be done, the neurons in the patch instead process information by following the patterns of excitation and inhibition imposed by the waves.
Think of the alpha and beta frequency waves as stencils that shape when and where in the prefrontal cortex groups of neurons can take in or express information from the senses, Miller said. In that way, the waves represent the rules of the task and can organize how the neurons electrically "spike" to process the information content needed for the task.
"Cognition is all about large-scale neural self-organization," said Miller, senior author of the paper in Current Biology and a faculty member in MIT's Department of Brain and Cognitive Sciences. "Spatial Computing explains how the brain does that."
Testing five predictions
A theory is just an idea. In the study, lead author Zhen Chen and other current and former members of Miller's lab, put Spatial Computing to the test by examining whether five predictions it makes about neural activity and brain wave patterns were actually evident in measurements made in the prefrontal cortex of animals as they engaged in two working memory and one categorization tasks. Across the tasks there were distinct pieces of sensory information to process (e.g. "a blue square appeared on the screen followed by a green triangle") and rules to follow (e.g. "when new shapes appear on the screen, do they match the shapes I saw before and appear in the same order?")
The first two predictions were that alpha and beta waves should represent task controls and rules, while the spiking activity of neurons should represent the sensory inputs. When the researchers analyzed the brain wave and spiking readings gathered by the four electrode arrays implanted in cortex, they found that indeed these predictions were true. Neural spikes, but not the alpha/beta waves, carried sensory information. While both spikes and the alpha/beta waves carried task information, it was strongest in the waves and it peaked at times relevant to when rules were needed to carry out the tasks.
Notably, in the categorization task the researchers purposely varied the level of abstraction to make categorization more or less cognitively difficult. The researchers saw that the greater the difficulty, the stronger the alpha/beta wave power was, further showing that it carries task rules.
The next two predictions were that alpha/beta would be spatially organized and that when and where it was strong, the sensory information represented by spiking would be suppressed but where and when it was weak, spiking would increase. These predictions also held true in the data. Under the electrodes Chen, Miller and the team could see distinct spatial patterns of higher or lower wave power, and where power was high, the sensory information in spiking was low and vice versa.
Finally, if Spatial Computing is valid, the researchers predicted, then trial by trial alpha/beta power and timing should accurately correlate with the animals' performance. Sure enough, there were significant differences in the signals on trials where the animals performed the tasks correctly vs. when they made mistakes. In particular, the measurements predicted mistakes due to messing up task rules vs. sensory information. For instance, alpha/beta discrepancies pertained to the order in which stimuli appeared (first square then triangle) rather than the identity of the individual stimuli (square or triangle).
Compatible with findings in humans
By experimenting with animals, the researchers were able to make direct measurements of individual neural spikes as well as brain waves, but in the paper, they note that other studies in humans report some similar findings. For instance, studies using non-invasive EEG and MEG brain wave readings show that humans use alpha oscillations to inhibit activity in task-irrelevant areas under top-down control and that alpha oscillations appear to govern task-related activity in the prefrontal cortex.
While Miller said he finds the results of the new study, and their intersection with human studies, to be encouraging, he acknowledges that more evidence is still needed. For instance, his lab has shown that brain waves are typically not still (like a jump rope) but travel across areas of the brain. Spatial Computing should account for that, he said.
In addition to Chenand Miller, the paper's other authors are Scott Brincat, Mikael Lundqvist, Roman Loonis and Melissa Warden.
The Office of Naval Research, The Freedom Together Foundation and The Picower Institute for Learning and Memory funded the study.
