Resume: The brain creates specific and distinct spaces in the cortex for each general rule of working memory and controls these spaces with brain rhythms, researchers report.
Routine tasks that require working memory, such as baking, involve remembering both some general rules (e.g., reading the oven temperature and time from the recipe and then putting it in the oven) and specific content for each occasion (e.g., 350 degrees for 45 minutes for a rye bread, but 325 degrees for 8 minutes for cookies).
A new study offers a new explanation for how the brain clearly manages the general and specific components of such cognitive demands.
The research, led by scientists at MIT’s Picower Institute for Learning and Memory and the Karolinksa Institute and KTH Royal Institute of Technology in Stockholm, Sweden, shows that the brain creates separate spaces in the cortex for each general rule and that controlling places with brain rhythms, a concept the authors call ‘Spatial Computing’.
This system, evident in the study’s animal experiments, explains how the brain can easily maintain a consistent understanding of a process even when the specific content keeps changing (such as the time and temperature for bread vs. cookies).
It also answers some questions neuroscientists have grappled with about the physiological operations that underlie working memory.
“Your brain can generalize immediately. If I teach you to follow some rules, like remember C, A, and B and put them in alphabetical order, and then I change the contents to F, D, and E, you won’t miss a second,” said Earl K. Miller , Picower Professor at MIT’s Picower Institute for Learning and Memory and co-senior author of the study in Nature communication.
“Your brain can do this because it represents the rules and the content on different physical scales. One can simply be connected to the other.”
Working memory works
Years of research by Miller’s lab, led largely by lead author Mikael Lundqvist, who now works at Karolinska, has shown that working memory tasks are determined by an interplay of brain rhythms at different frequencies. Slower beta waves carry information about task rules and selectively yield to faster gamma waves when it’s time to perform operations, such as storing information from the senses or reading it out when recall is needed.
But these waves operate on networks of millions of neurons, only a few of which actually store the individual items of information that are relevant at any given time. In addition, neurons can be found everywhere that contain information about specific items. Some become electrically excited, or “spike,” in response to different job rules than others, and they often tend to spike at least somewhat, even when their information is irrelevant.
So how can these rather imprecise rhythms selectively direct just the right neurons at the right times to do the right things? Why are neurons whose spikes relate to specific items scattered and redundant? What causes a neuron specific to “350 degrees” to perk up when that information needs to be stored, but another neuron with that information to perk up when it needs to be recalled?
The researchers realized that all these questions could be solved by the Spatial Computing theory. Individual neurons representing items of information may be widely scattered throughout the cortex, but the rule applied to them is based on the patch of the network in which they reside. Those patches are determined by the pattern of beta and gamma waves.
“Over the years, analyzing many individual neurons, we had always wondered why so many of them seemed to behave in the same way,” Lundqvist said.
“Regardless of whether they preferred the same external stimulus or not, many neurons shared similar activity patterns during working memory. And these patterns changed from task to task. It also turned out that neurons that were closer together in the prefrontal cortex shared the same pattern more often. It put us thinking that memory representations could actually flow around dynamically in the prefrontal cortex to implement task rules.”
So say your friend calls you at the gym and asks you to pick up a watch they accidentally left in their locker. This requires turning the padlock knobs to the numbers in the combination (e.g. 24, 17, 32). Spatial computing says that when you hear the combination, your brain creates different spots for each step (first, second, third).
Within each patch, the neurons representing that particular step’s combination number are highly excited by gamma waves applied at the time the rule is relevant (i.e. 24 in the “first” patch, 17 in the “second” patch, and 32 in the “Third Patch”). ” patch).
In this way, individual neurons encoding specific items of information can be selectively associated with general rules by the brain waves that control the places they inhabit. In any given patch, all neurons may be slightly excited by the gamma waves, but those representing the item that meets the rule will peak the most.
“In this way, memory representations can be dynamically reshaped to meet current task demands, independent of how individual neurons are connected or what stimulus they prefer,” said co-senior author Pawel Herman of KTH. “It may explain our impressive generalization capabilities in new situations.”
This is not to say that a patch is fixed forever. The patches can come and go as long as they are needed, wherever the brain forms them for the task at hand. There is no permanent “remember oven temperature” stain in the brain.
“This gives the brain flexibility,” Miller said. “Cognition is all about flexibility.”
The researchers weren’t just theorizing. To test spatial computing in real physical brains, they made four experimental predictions about what they should perceive when animals played working memory games, such as remembering a series of images in a sequence.
The first prediction was that there should be clear neural signals about the rules and individual item information. Indeed, the team measured that wave bursts carry rule information. Individual neural spikes, meanwhile, carried a mix of individual items and task rules, consistent with them representing individual items and having specific rules imposed on them.
The second prediction was that rule information should be spatially organized and the third prediction was that these rule-enforcing spatial patterns should be consistent as long as the game rules remained the same, regardless of whether the individual items changed.
Sure enough, the researchers found that there were different locations for gamma bursts for different rules and that they remained stable even as the individual items varied during each game.
The final prediction was that brain wave activity should ensure that neural spiking activity represents the right information at the right time. This was also reflected in the experimental observations.
The researchers saw different brain wave patterns for when the brain should store images in memory and when to remember the “correct” ones. In general, beta waves were more reduced and neurons peaked more and in a larger area during recall than during storage.
The article does not answer every question about working memory. It’s not yet clear how neurons encoding specific information in one patch might be associated with their brethren in another patch or how the brain controls the patches. More research can answer those further questions about the implications of the new theory of Spatial Computing.
About this news about memory and neuroscience research
Author: Press Office
Contact: Press Service – MIT
Image: The image is in the public domain
Original research: Open access.
“The Dynamics of Working Memory Control Follows the Principles of Spatial Computing” by Earl K. Miller et al. Nature communication
The working memory control dynamics follow the principles of spatial computing
Working memory (WM) allows us to remember and selectively control a limited number of items. Neural evidence suggests it is achieved through interactions between bursts of beta and gamma oscillations. However, it is not clear how oscillations, which reflect the coherent activity of millions of neurons, can selectively control individual WM items.
Here we propose the new concept of spatial computing, where beta and gamma interactions cause item-specific activity to flow spatially across the network during a task.
In this way, control-related information, such as item order, is stored in the spatial activity, independent of the detailed recurrent connectivity that the item-specific activity itself supports.
The spatial flow, in turn, is reflected in low-dimensional activity shared by many neurons. We verify these predictions by analyzing local field potentials and neuronal spikes.
We hypothesize that spatial computing can facilitate generalization and zero-shot learning by using spatial component as an additional information coding dimension.