The team discovered a point mutation in the fruit fly Drosophila melanogaster which leads to a temperature-dependent lengthening of circadian clock periods. It is located in a central “clock gene” known as a “period” (per). Flies which have this perI530A mutation display a normal sleep-wake rhythm of 24 hours at 18 degrees Celsius. In contrast, at 29 degrees Celsius the inner clock runs about five hours slower, i.e. it lasts 29 hours. This lengthening of periods also affects the expression, in other words the activity, of the period gene in the clock neurones of the brain.

Normally, the protein concerned (PERIOD) is gradually changed chemically in the course of 24 hours – specifically, it is phosphorylated. After maximum phosphorylation it is degraded. Here, too, this process is normally the same at temperatures between 18 and 29 degrees Celsius, at which fruit flies are active. As the researchers showed, phosphorylation occurs in a normal way in the perI530A mutant at 18 degrees Celsius but decreases as the temperature increases. This leads to a stabilisation of the “PERIOD” protein at warmer temperatures.

The mutation studied by the team affects a so-called nuclear export signal (NES), which also occurs in this form in the period genes of mammals and plays a role in transporting the PERIOD proteins out of the cell nucleus. No biological function of this export from the cell nucleus had previously been known of. The current study shows that the mutation leads to an extended retention of the PERIOD protein in the cell nucleus of central clock neurones – and again, only at higher temperatures. “We therefore assume,” says Ralf Stanewsky, “that the export of the protein from the cell nucleus plays an important role in temperature compensation – at least as far as the fruit fly is concerned.”

Method used

In their investigations, the scientists used fruit fly mutants with a modification in the period gene (perI530A) which they had produced using modern molecular genetics methods (CRISPR/Cas9 mutagenesis and homologous recombination). These animals were then tested to see whether their sleep-wake cycle – and, as a result, their running activity – differed, depending on the ambient temperature. Using a variety of methods, the researchers visualised the clock genes and their activity in the brain neurones. One of the things they used was a new method called Locally Activatable BioLuminescence (LABL) which the Münster team had developed in collaboration with researchers in Canada. This method involving bioluminescence makes it possible to measure, in living flies, the rhythmic gene expression in clock neurones – which only make up a fraction of all brain neurones.

Story Source:

Materials provided by University of Münster. Note: Content may be edited for style and length.

In a study in PLOS Computational Biology, scientists at The Picower Institute for Learning and Memory compared measurements of brain cell activity in an animal performing a working memory task with the output of various computer models representing two theories of the underlying mechanism for holding information in mind. The results strongly favored the newer notion that a network of neurons stores the information by making short-lived changes in the pattern of their connections, or synapses, and contradicted the traditional alternative that memory is maintained by neurons remaining persistently active (like an idling engine).

While both models allowed for information to be held in mind, only the versions that allowed for synapses to transiently change connections (“short-term synaptic plasticity”) produced neural activity patterns that mimicked what was actually observed in real brains at work. The idea that brain cells maintain memories by being always “on” may be simpler, acknowledged senior author Earl K. Miller, but it doesn’t represent what nature is doing and can’t produce the sophisticated flexibility of thought that can arise from intermittent neural activity backed up by short-term synaptic plasticity.

“You need these kinds of mechanisms to give working memory activity the freedom it needs to be flexible,” said Miller, Picower Professor Neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS). “If working memory was just sustained activity alone, it would be as simple as a light switch. But working memory is as complex and dynamic as our thoughts.”

Co-lead author Leo Kozachkov, who earned his PhD at MIT in November for theoretical modeling work including this study, said matching computer models to real-world data was crucial.

“Most people think that working memory ‘happens’ in neurons — persistent neural activity gives rise to persistent thoughts. However, this view has come under recent scrutiny because it does not really agree with the data,” said Kozachkov who was co-supervised by co-senior author Jean-Jacques Slotine, a professor in BCS and mechanical engineering. “Using artificial neural networks with short-term synaptic plasticity, we show that synaptic activity (instead of neural activity) can be a substrate for working memory. The important takeaway from our paper is: these ‘plastic’ neural network models are more brain-like, in a quantitative sense, and also have additional functional benefits in terms of robustness.”

Matching models with nature

Alongside co-lead author John Tauber, an MIT graduate student, Kozachkov’s goal was not just to determine how working memory information might be held in mind, but to shed light on which way nature actually does it. That meant starting with “ground truth” measurements of the electrical “spiking” activity of hundreds of neurons in the prefrontal cortex of an animal as it played a working memory game. In each of many rounds the animal was shown an image that then disappeared. A second later it would see two images including the original and had to look at the original to earn a little reward. The key moment is that intervening second, called the “delay period,” in which the image must be kept in mind in advance of the test.

The team consistently observed what Miller’s lab has seen many times before: The neurons spike a lot when seeing the original image, spike only intermittently during the delay, and then spike again when the images must be recalled during the test (these dynamics are governed by an interplay of beta and gamma frequency brain rhtyhms). In other words, spiking is strong when information must be initially stored and when it must be recalled but is only sporadic when it has to be maintained. The spiking is not persistent during the delay.

Moreover, the team trained software “decoders” to read out the working memory information from the measurements of spiking activity. They were highly accurate when spiking was high, but not when it was low, as in the delay period. This suggested that spiking doesn’t represent information during the delay. But that raised a crucial question: If spiking doesn’t hold information in mind, what does?

Researchers including Mark Stokes at the University of Oxford have proposed that changes in the relative strength, or “weights,” of synapses could store the information instead. The MIT team put that idea to the test by computationally modeling neural networks embodying two versions of each main theory. As with the real animal, the machine learning networks were trained to perform the same working memory task and to output neural activity that could also be interpreted by a decoder.

The upshot is that the computational networks that allowed for short-term synaptic plasticity to encode information spiked when the actual brain spiked and didn’t when it didn’t. The networks featuring constant spiking as the method for maintaining memory spiked all the time including when the natural brain did not. And the decoder results revealed that accuracy dropped during the delay period in the synaptic plasticity models but remained unnaturally high in the persistent spiking models.

In another layer of analysis, the team created a decoder to read out information from the synaptic weights. They found that during the delay period, the synapses represented the working memory information that the spiking did not.

Among the two model versions that featured short-term synaptic plasticity the most realistic one was called “PS-Hebb,” which features a negative feedback loop that keeps the neural network stable and robust, Kozachkov said.

Workings of working memory

In addition to matching nature better, the synaptic plasticity models also conferred other benefits that likely matter to real brains. One was that the plasticity models retained information in their synaptic weightings even after as many as half of the artificial neurons were “ablated.” The persistent activity models broke down after losing just 10-20 percent of their synapses. And, Miller added, just spiking occasionally requires less energy than spiking persistently.

Furthermore, Miller said, quick bursts of spiking rather than persistent spiking leaves room in time for storing more than one item in memory. Research has shown that people can hold up to four different things in working memory. Miller’s lab plans new experiments to determine whether models with intermittent spiking and synaptic weight-based information storage appropriately match real neural data when animals must hold multiple things in mind rather than just one image.

In addition to Miller, Kozachkov, Tauber and Slotine, the paper’s other authors are Mikael Lundqvist and Scott Brincat.

The Office of Naval Research, the JPB Foundation, and ERC and VR Starting Grants funded the research.

Rich Get Richer

Therefore, they need financial resources and scientists. “Early investment in emerging areas of research is a key driver of scientific dominance,” he continues. Once the pioneer has established an area or technology, researchers are also more likely to move into this new, stimulating environment. This “get-rich-richer” phenomenon underlies the development of scientific strength in a region. And scientists’ mobility drives the development of scientific disciplines. The question is, then, how many scientists does a region need to hire so that other scientists find its environment attractive and join its institutions?

No Critical Mass

The team found no evidence for the existence of a minimum number of researchers to hire. Or in technical terms: there is no critical mass to start and carry on a new research field successfully. Here, the scientists focused on three scientific areas: semiconductors, embryonic stem cells (ESC) and internet research.

“In a way, this contradicts the generally held belief that you need a minimum number or critical mass of researchers to make a field successful. In our study, it becomes apparent that this is not the case,” Stefan Thurner from CSH states.

Be a Pioneer

In fact, regions seem to be successful when they manage to jump early on a train and become pionEe rs in a field. “We also find, as common sense suggests, that regions moving early into new technologies tend to dominate the corresponding scientific fields in the future,” Thurner explains.

China: Expensive Race to Catch Up

If a region was not a pioneer, but still wants to achieve leadership in a given area of research it has to make extraordinary efforts to catch up. “Strategic interventions must be sustained over decades to compete for a leading position in a field, as is evident, for example, in Chinese semiconductor science, where the catch up-process began in the late 1970s and has led to a dominant role today,” Servedio says.

A model specially developed by the researchers can explain the development pattern of China — how they take over specific research areas and grow the number of scientists publishing in those fields. “After all, China has some of the fastest-growing institutions worldwide. With our model, you can clearly see that China is closing the gap with the US, potentially taking on huge costs, but also showing their ability to effectively engage with high impact research. While first movers may have an advantage in some contexts, it is not necessarily impossible for late-comers to catch up or even surpass first movers in a scientific field.” CSH researcher Márcia R. Ferreira says.

Three Research Fields and Millions of Data

With the help of the Dimensions database, the researchers at CSH could track scientists moving across regions of the world (they derived these movements from the scientists’ affiliations) in three fields — namely semiconductors, embryonic stem cells (ESC) and internet research. “In this way, we analyzed data spanning several decades with information on millions of publications, 20 million researchers and more than 98,000 research institutions worldwide,” Servedio states.

In the field of semiconductor research, they tracked 5,062,639 articles and 2,011,170 researchers in 1,633 regions worldwide between 1941 and 2019; in the field of stem cell research, they tracked 1,083,100 articles and 752,575 researchers in 1,161 regions worldwide over the same period; and in the field of internet research, they tracked 246,953 articles and 109,098 researchers in 1,032 regions worldwide between 1956 and 2019.

“What our results clearly show is if regions want to become a leader in a field, they must try to get involved early on. It is possible to catch up, but this comes at tremendous costs,” Thurner and Servedio state. “Nonetheless, our model of scientific capacity building is a simplification. There are other factors contributing to the success of a field that we have not yet been able to explore here and which will be the subject of future analysis,” Ferreira states.