Revisiting the mechanics of the action potential (Nature Communications)

By Staff


The action potential (AP) and the accompanying action wave (AW) constitute an electromechanical pulse traveling along the axon.

The action potential is widely understood as an electrical phenomenon. However, a long experimental history has documented the existence of co-propagating mechanical signatures.

In a new paper in the journal Nature Communications, two Princeton University researchers have proposed a theoretical model to explain these mechanical signatures, which they term “action waves.” The research was conducted by Ahmed El Hady, a visiting postdoctoral research associate at the Princeton Neuroscience Institute and a postdoctoral fellow at the Howard Hughes Medical Institute, and Benjamin Machta, an associate research scholar at the Lewis-Sigler Institute for Integrative Genomics and a lecturer in physics and the Lewis-Sigler Institute for Integrative Genomics.

In the model, the co-propagating waves are driven by changes in charge separation across the axonal membrane, just as a speaker uses charge separation to drive sound waves through the air. The researchers argue that these forces drive surface waves involving both the axonal membrane and cytoskeleton as well as its surrounding fluid. Their model may help shed light on the functional role of the surprisingly structured axonal cytoskeleton that recent super-resolution techniques have uncovered, and suggests a wider role for mechanics in neuronal function.

Read the paper.

Ahmed El Hady & Benjamin B. Machta. Mechanical surface waves accompany action potential propagation. Nature Communications 6, No. 6697 doi:10.1038/ncomms7697

When attention is a deficit: How the brain switches strategies to find better solutions (Neuron)

By Catherine Zandonella, Office of the Dean for Research

2015_03_26_JW_Schuck_NYC3Sometimes being too focused on a task is not a good thing.

During tasks that require our attention, we might become so engrossed in what we are doing that we fail to notice there is a better way to get the job done.

For example, let’s say you are coming out of a New York City subway one late afternoon and you want to find out which way is west. You might begin to scan street signs and then suddenly realize that you could just look for the setting sun.

A new study explored the question of how the brain switches from an ongoing strategy to a new and perhaps more efficient one. The study, conducted by researchers at Princeton University, Humboldt University of Berlin, the Bernstein Center for Computational Neuroscience in Berlin, and the University of Milan-Bicocca, found that activity in a region of the brain known as the medial prefrontal cortex was involved in monitoring what is happening outside one’s current focus of attention and shifting focus from a successful strategy to one that is even better. They published the finding in the journal Neuron.

“The human brain at any moment in time has to process quite a wealth of information,” said Nicolas Schuck, a postdoctoral research associate in the Princeton Neuroscience Institute and first author on the study. “The brain has evolved mechanisms that filter that information in a way that is useful for the task that you are doing. But the filter has a disadvantage: you might miss out on important information that is outside your current focus.”

Schuck and his colleagues wanted to study what happens at the moment when people realize there is a different and potentially better way of doing things. They asked volunteers to play a game while their brains were scanned with magnetic resonance imaging (MRI). The volunteers were instructed to press one of two buttons depending on the location of colored squares on a screen. However, the game contained a hidden pattern that the researchers did not tell the participants about, namely, that if the squares were green, they always appeared in one part of the screen and if the squares were red, they always appeared in another part. The researchers refrained from telling players that they could improve their performance by paying attention to the color instead of the location of the squares.

Volunteers played a game where they had to press one button or another depending on the location of squares on a screen. Participants that switched to a strategy based on the color of the square were able to improve their performance on the game. (Image source: Schuck, et al.)

Volunteers played a game where they had to press one button or another depending on the location of squares on a screen. Participants that switched to a strategy based on the color of the squares were able to improve their performance on the game. (Image source: Schuck, et al.)

Not all of the players figured out that there was a more efficient way to play the game. However, among those that did, their brain images revealed specific signals in the medial prefrontal cortex that corresponded to the color of the squares. These signals arose minutes before the participants switched their strategies. This signal was so reliable that the researchers could use it to predict spontaneous strategy shifts ahead of time, Schuck said.

“These findings are important to better understand the role of the medial prefrontal cortex in the cascade of processes leading to the final behavioral change, and more generally, to understand the role of the medial prefrontal cortex in human cognition,” said Carlo Reverberi, a researcher at the University of Milan-Bicocca and senior author on the study. “Our findings suggest that the medial prefrontal cortex is ‘simulating’ in the background an alternative strategy, while the overt behavior is still shaped by the old strategy.”

The study design – specifically, not telling the participants that there was a more effective strategy – enabled the researchers to show that the brain can monitor background information while focused on a task, and choose to act on that background information.

“What was quite special about the study was that the behavior was completely without instruction,” Schuck said. “When the behavior changed, this reflected a spontaneous internal process.”

Before this study, he said, most researchers had focused on the question of switching strategies because you made a mistake or you realized that your current approach isn’t working. “But what we were able to explore,” he said, “is what happens when people switch to a new way of doing things based on information from their surroundings.” In this way, the study sheds light on how learning and attention can interact, he said.

The study has relevance for the question of how the brain balances the need to maintain attention with the need to incorporate new information about the environment, and may eventually help our understanding of disorders that involve attention deficits.

Schuck designed and conducted the experiments while a graduate student at Humboldt University and the International Max Planck Research School on the Life Course (LIFE) together with the other authors, and conducted the analysis at Princeton University in the laboratory of Yael Niv, assistant professor of psychology and the Princeton Neuroscience Institute in close collaboration with Reverberi.

The research was supported through a grant from the U.S. National Institutes of Health, the International Max Planck Research School LIFE, the Italian Ministry of University, the German Federal Ministry of Education and Research, and the German Research Foundation.

Read the abstract.

Nicolas W. Schuck, Robert Gaschler, Dorit Wenke, Jakob Heinzle, Peter A. Frensch, John-Dylan Haynes, and Carlo Reverberi. Medial Prefrontal Cortex Predicts Internally Driven Strategy Shifts, Neuron (2015)



Genome-wide search reveals new genes involved in long-term memory (Neuron)

By Catherine Zandonella, Office of the Dean for Research

Whole genome expression data reveals new genes involved in long-term memory formation in worms. (Image source: Murphy lab)

Whole genome expression data reveals new genes involved in long-term memory formation in worms. (Image source: Murphy lab)

A new study has identified genes involved in long-term memory in the worm as part of research aimed at finding ways to retain cognitive abilities during aging.

The study, which was published in the journal Neuron, identified more than 750 genes involved in long-term memory, including many that had not been found previously and that could serve as targets for future research, said senior author Coleen Murphy, an associate professor of molecular biology and the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

“We want to know, are there ways to extend memory?” Murphy said. “And eventually, we would like to ask, are there compounds that could maintain memory with age?”

Long-term memory training in worms (left) led to induction of the transcription factor CREB in AIM neurons (shown by arrows in right). CREB-induced genes were shown to be involved in forming long-term memories in worm neurons. (Image source: Murphy lab)

Long-term memory training in worms (left) led to induction of the transcription factor CREB in AIM neurons (shown by arrows in right). CREB-induced genes were shown to be involved in forming long-term memories in worm neurons. (Image source: Murphy lab)

The newly pinpointed genes are “turned on” by a molecule known as CREB (cAMP-response element-binding protein), a factor known to be required for long-term memory in many organisms, including worms and mice.

“There is a pretty direct relationship between CREB and long-term memory,” Murphy said, “and many organisms lose CREB as they age.” By studying the CREB-activated genes involved in long-term memory, the researchers hope to better understand why some organisms lose their long-term memories as they age.

To identify the genes, the researchers first instilled long-term memories in the worms by training them to associate meal-time with a butterscotch smell. Trained worms were able to remember that the butterscotch smell means dinner for about 16 hours, a significant amount of time for the worm.

The researchers then scanned the genomes of both trained worms and non-trained worms, looking for genes turned on by CREB.

The researchers detected 757 CREB-activated genes in the long-term memory-trained worms, and showed that these genes were turned on primarily in worm cells called the AIM interneurons.

They also found CREB-activated genes in non-trained worms, but the genes were not turned on in AIM interneurons and were not involved in long-term memory. CREB turns on genes involved in other biological functions such as growth, immune response, and metabolism. Throughout the worm, the researchers noted distinct non-memory (or “basal”) genes in addition to the memory-related genes.

The next step, said Murphy, is to find out what these newly recognized long-term memory genes do when they are activated by CREB. For example, the activated genes may strengthen connections between neurons.

Worms are a perfect system in which to explore that question, Murphy said. The worm Caenorhabditis elegans has only 302 neurons, whereas a typical mammalian brain contains billions of the cells.

“Worms use the same molecular machinery that higher organisms, including mammals, use to carry out long-term memory,” said Murphy. “We hope that other researchers will take our list and look at the genes to see whether they are important in more complex organisms.”

Murphy said that future work will involve exploring CREB’s role in long-term memory as well as reproduction in worms as they age.

The team included co-first-authors Postdoctoral Research Associate Vanisha Lakhina, Postdoctoral Research Associate Rachel Arey, and Associate Research Scholar Rachel Kaletsky of the Lewis-Sigler Institute for Integrative Genomics. Additional research was performed by Amanda Kauffman, who earned her Ph.D. in Molecular Biology in 2010; Geneva Stein, who earned her Ph.D. in Molecular Biology in 2014; William Keyes, a laboratory assistant in the Department of Molecular Biology; and Daniel Xu, who earned his B.A. in Molecular Biology in 2014.

Funding for the research was provided by the National Institutes of Health and the Paul F. Glenn Laboratory for Aging Research at Princeton University.

Read the abstract

Citation: Vanisha Lakhina, Rachel N. Arey, Rachel Kaletsky, Amanda Kauffman, Geneva Stein, William Keyes, Daniel Xu, and Coleen T. Murphy. Genome-wide Functional Analysis of CREB/Long-Term Memory-Dependent Transcription Reveals Distinct Basal and Memory Gene Expression Programs, Neuron (2015),

Pupil study reveals learning styles, brain activity (Nature Neuroscience)

Test of learning styles experiment

To test people’s learning styles, participants were presented with a choice between two images (objects or words) and rewarded according to their choices. In this exercise, to maximize reward, participants had to learn by trial and error that office-related images provide a higher reward than food-related images (semantic, or related to meaning), and that grayscale images provide a higher reward than color images (visual features). Image credit: Nature Neuroscience.

By Catherine Zandonella, Office of the Dean for Research

People are often said to have “learning styles” – for example, some people pay attention to visual details while others grab onto abstract concepts and meanings. A new study from Princeton University researchers found that changes in pupil size can reveal whether people are learning using their dominant learning style, or whether they are learning in modes outside of that style.

The researchers found that pupil dilation was smaller when people learned using their usual style and larger when people diverged from their normal style. The study was published in the journal Nature Neuroscience.

The study compared brain activity in individuals with two different learning styles – those who learn best by absorbing concrete visual details and those who are better at learning abstract concepts or meanings.

“We showed that changes in pupil dilation are associated with the degree to which learners use the style for which they have a predisposition,” said Eldar Eran, a graduate student in the Princeton Neuroscience Institute, who led the study.

The researchers used changes in pupil size as an indicator of variations in “neural gain,” which can be thought of as an amplifier of neural communication: when gain is increased, excited neurons become even more active and inhibited neurons become even less active. Smaller pupil dilation indicates more neural gain and larger pupil dilation indicates less neural gain.

The team showed that neural gain was correlated with different modes of communication between parts of the brain. In studies of human volunteers undergoing brain scans, when neural gain was high, communication tended to be tightly concentrated in certain regions of the brain that govern specific tasks, whereas low neural gain is associated with communication across wider regions of the brain.

“We showed that the brain has different modes of communication,” said Yael Niv, assistant professor of psychology and the Princeton Neuroscience Institute, “one mode where everything talks to everything else, and another mode where communication is more segregated into areas that don’t talk to each other.” The study also involved Jonathan Cohen, Princeton’s Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience.

These modes are linked to the level of neural gain and to learning style, Niv said. Neural gain can be thought of as a contrast amplifier that increases intensity of both activation and inhibition of communication among brain areas. “If one area is trying to activate another, or trying to inhibit another – both effects are stronger, everything is more potent,” she said. “This is correlated with communication being segregated into clusters of activation in the brain, so each network is talking to itself loudly, but connections across networks are inhibited. In situations of lower gain, however, the areas can talk to each other across networks, so information flows more globally.”

“These two modes [of communication in the brain] seem to be associated with different constraints on learning,” she said. “According to our study, in the mode where everything talks to everything, learning is very flexible. In contrast, in the mode where communication is stronger and more focused, but also more segregated between brain areas, subjects were more true to their personal learning style. Neither of these modes are better than the other – in both cases participants were equally successful in the task, but in different ways.”

“We interpreted these results to mean that although we tend to have a dominant learning style, we are not a slave to that style, and when operating in the proper mode, we can overcome dominant styles to learn in other ways,” she said.

This research was funded by NIH grants R03 DA029073 and R01 MH098861, a Howard Hughes Medical Institute International Student Research fellowship, and a Sloan Research Fellowship. The authors also wish to thank the generous support of the Regina and John Scully Center for the Neuroscience of Mind and Behavior within the Princeton Neuroscience Institute.

Read the article

Eldar, Eran, Jonathan D Cohen & Yael Niv. 2013. The effects of neural gain on attention and learning.  Nature Neuroscience. Published online June 16, 2013 doi:10.1038/nn.3428

Researchers discover workings of brain’s ‘GPS system’ (Nature)

By Catherine Zandonella, Office of the Dean for Research

Just as a global positioning system (GPS) helps find your location, the brain has an internal system for helping determine the body’s location as it moves through its surroundings.

A new study from researchers at Princeton University provides evidence for how the brain performs this feat. The study, published in the journal Nature, indicates that certain position-tracking neurons — called grid cells — ramp their activity up and down by working together in a collective way to determine location, rather than each cell acting on its own as was proposed by a competing theory.

Grid cells are neurons that become electrically active, or “fire,” as animals travel in an environment. First discovered in the mid-2000s, each cell fires when the body moves to specific locations, for example in a room. Amazingly, these locations are arranged in a hexagonal pattern like spaces on a Chinese checker board.  (See figure.)


As the mouse moves around in a square arena (left), a single grid cell in the mouse’s brain becomes active, or spikes, when the animal arrives at particular locations in the arena (right). These locations are arranged in a hexagonal pattern. The red dots indicate the mouse’s location in the arena when the grid cell fired. (Image credit: Cristina Domnisoru, Princeton University)

“Together, the grid cells form a representation of space,” said David Tank, Princeton’s Henry L. Hillman Professor in Molecular Biology and leader of the study. “Our research focused on the mechanisms at work in the neural system that forms these hexagonal patterns,” he said. The first author on the paper was graduate student Cristina Domnisoru, who conducted the experiments together with postdoctoral researcher Amina Kinkhabwala.

Domnisoru measured the electrical signals inside individual grid cells in mouse brains while the animals traversed a computer-generated virtual environment, developed previously in the Tank lab. The animals moved on a mouse-sized treadmill while watching a video screen in a set-up that is similar to video-game virtual reality systems used by humans.

She found that the cell’s electrical activity, measured as the difference in voltage between the inside and outside of the cell, started low and then ramped up, growing larger as the mouse reached each point on the hexagonal grid and then falling off as the mouse moved away from that point.

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.

Read the abstract.

Domnisoru, Cristina, Amina A. Kinkhabwala & David W. Tank. 2013. Membrane potential dynamics of grid cells. Nature. doi:10.1038/nature11973. Published online Feb. 10, 2013.

This work was supported by the National Institute of Neurological Disorders and Stroke under award numbers 5RC1NS068148-02 and 1R37NS081242-01, the National Institute of Mental Health under award number 5R01MH083686-04, a National Institutes of Health Postdoctoral Fellowship grant F32NS070514-01A1 (A.A.K.), and a National Science Foundation Graduate Research Fellowship (C.D.).



How our brains keep track of where we are in the world (Journal of Neuroscience)

Libraries, supermarkets, classrooms…the world is full of places that look very similar, and yet our brains always seem to keep track of where we are. In a new study published in the Journal of Neuroscience, researchers at Princeton University and Ohio State University have uncovered one way in which the brain does this.

Similar-looking places can be distinguished from each other because of differences in what we experience when navigating to them. As we head toward a destination, our brains catalogue details such as other nearby buildings, the look of the doorway, even the people nearby.

The researchers discovered that the parahippocampal cortex, a part of the visual system that analyzes the current scene in front of us, also incorporates the details leading up to the scene, or its “temporal context.” As a result, even when two scenes look identical, we create different memory traces for them when their temporal contexts are different. Ultimately, this can help our brains to keep track of where we are in the world.

Learn more about Nicholas Turk-Browne‘s research at Princeton University.

Journal Citation: Turk-Browne NB, Simon MG, Sederberg PB. Scene representations in parahippocampal cortex depend on temporal context.  J Neurosci. 2012 May 23;32(21):7202-7.