Fever, reduced physical activity, loss of appetite, and fatigue: When we’re sick, the body sends a variety of signals from its “most remote parts” to—as far as we know—deeper regions of the brain. But how is this information processed in the brain to create a “general feeling of illness”? And what role do the upper, outer brain regions—such as the cortex—play in this process?

The insula is part of the outer cerebral cortex—a region where bodily and emotional information is processed.

Researchers at the European Laboratory for Molecular Biology in Heidelberg, in collaboration with scientists from the ESI, have now published their latest findings on these questions in the journal *Current Biology*.

The body’s own signaling molecules influence symptoms…

In the case of an infection, typical signs of inflammation, such as fever, occur. Such signals are a sure sign that the immune system is already working at full capacity, for example, to fend off pathogens.

The body’s own signaling molecules play an important role in this process. For example, prostaglandin E2 (PGE2) is significantly involved in regulating typical inflammatory symptoms. Among other things, this tissue hormone acts on the hypothalamus, where it influences the development of fever.

… and the perception of those symptoms

In their experiment, the scientists induced a kind of artificial disease state in mice by directly infusing PGE2 into the brain, thereby enabling them to specifically investigate the messenger substance’s effect on the brain by excluding other bodily signals.

 In this way, the researchers were then able to observe which brain regions were primarily involved in processing PGE2. Using an imaging technique that visualizes active brain regions, the researchers first confirmed the known involvement of the hypothalamus and brainstem. They then specifically measured the activity of individual neurons in the insula and the somatosensory cortex.

There, the researchers made a surprising discovery…

The insula is part of the outer cerebral cortex—a region where bodily and emotional information is processed. There, as part of their study, the researchers made a surprising discovery: Analysis of stimulus processing revealed that the disease characteristics in the insula are not processed in isolation from one another, but rather simultaneously. According to the researchers, this could indicate that the brain integrates the individual symptoms into a unified sense of illness. Based on their findings, they propose an expanded explanatory model for the processing of inflammatory responses in the brain.

Future prospects for pain management

In the future, this knowledge could, for example, become relevant for the further development of pain medications: Many active ingredients already target the mechanism of prostaglandin production and could thus potentially be improved even further.

And since the insula, among other things, stores patterns of past pain signals and can reactivate or reconstruct them in response to corresponding triggers, the study results might also one day contribute to a better understanding of phantom pain or so-called pain memory in chronic conditions. However, many further research steps are still needed to achieve clinical benefits in this area.

Click here for the paper

Source: Ernst Strüngmann Institute for Neuroscience
Original article: http://www.esi-frankfurt.de/news/2026/10/paper-on-pge2

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Humans respond to environments that change at many different speeds. 

A video game player, for example, reacts to on-screen events unfolding within hundreds of milliseconds or over several seconds. A boxer anticipates an opponent’s moves – even when their timing differs from that of previous opponents.

In each case, the brain predicts when events occur, prepares for what comes next and flexibly adapts to the demands of the situation.

A new study, recently published in Proceedings of the National Academy of Sciences (PNAS) and authored by neuroscientists from ESI, Goethe University Frankfurt, the Max Planck Institute for Empirical Aesthetics, and New York University, explains how the human brain predicts the timing of future events. 

The research shows that the brain continuously estimates how likely something is to happen within the next three seconds—and uses this estimate to prepare fast and accurate reactions.

How the brain “knows” when something will happen

Using psychophysical experiments, the researchers measured how quickly people responded to simple visual and acoustic signals, such as flashes or tones, while carefully controlling when these signals were likely to occur. From these experiments, they identified two key principles that the brain uses to predict the timing:

The brain uses the same basic probability calculation regardless of whether an event is expected in a few hundred milliseconds or in several seconds. This means the brain predicts the future in a consistent, scale-free way across different time ranges – up to at least three seconds.

At the same time, probability sharpens the sense of time: When an event is likely to happen at a certain point in time, the brain tracks time precisely. When an event is less likely, timing becomes less precise. This finding challenges a classic explanatory approach in psychology and neuroscience known as Weber’s law, which suggests that timing precision should not depend on probability.

Highly flexible and efficient: The brain uses simple key principles 

The brain has the fascinating ability to anticipate what will happen. The current study now explains how the brain predicts when something will happen, enabling people to react faster and more accurately.

“Our research shows that the brain relies on simple and flexible principles: it predicts the timing of future events in the same way across different situations and speeds. This helps explain why humans can adapt so easily to new environments,” says Dr. Matthias Grabenhorst, commenting on the findings. 

These insights can help us better understand many aspects of human behavior, including attention, decision-making, and even disorders that impair timing and prediction. The study sheds light on how the brain continuously prepares for the near future-second by second.

Click here to get to the original paper.

Source: Ernst Strüngmann Institute for Neuroscience
Original article: http://www.esi-frankfurt.de/news/2026/01/paper-on-anticipation

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Imagine a boxer dodging a punch, a musician perfectly timing a note, or a driver anticipating a green light—the brain can be seen as an amazing tool that is constantly predicting the future. But how does it do this? A team of neuroscientists from the Max Planck Institute for Empirical Aesthetics (MPIEA), the Ernst Strüngmann Institute (ESI) for Neuroscience, both in Frankfurt am Main, Germany, and the Goethe University Frankfurt, Germany, has shown for the first time how specific brain rhythms predict the timing of future events. The results were recently published in the journal Nature Communications.

The study reveals the key variable shaping our ability to anticipate upcoming events: probability over time. This probability is represented in brain waves that have a certain number of oscillations per second. These oscillations occur in the frequency ranges alpha, with seven to twelve hertz, and beta, with 15 to 30 hertz:

“The results show that brain waves in these frequency ranges forecast the time point at which future events will occur. The more predictable an event, the stronger the neural oscillations. This allows the brain to react faster and more efficiently,” explains first author Matthias Grabenhorst from ESI (formerly MPIEA).

Using magnetoencephalography (MEG), the research team was able to locate three key areas of the brain that track when something is likely to happen: First, the posterior parietal cortex, which is a hub for timing and motor preparation, plays an important role. In addition, the posterior middle temporal gyrus, which is crucial for processing events over time, is active. And also, the sensorimotor cortex plays a role, as it is directly involved in movements to predicted events.

The findings could have wide-ranging implications for our understanding of decision-making and attention, as well as athletic performance and neurological disorders:

“The study opens new doors for exploring the fundamental role of brain rhythms in how we navigate time and the future. For example, these rhythms could one day be used to train the brain to think ahead. Or disruptions in these signals could explain why certain diseases such as ADHD or Parkinson’s affect timing and reaction speed,” concludes senior author Georgios Michalareas of Goethe University, who participated in the study as a researcher at the MPIEA.

The study is part of a larger research project, “The Anticipation of Events in Time,” from which two studies have already been published.

Grabenhorst, M., Poeppel, D., & Michalareas, G. (2025). Neural Signatures of Temporal Anticipation in Human Cortex Represent Event Probability Density. Nature Communications. Link

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Emotional voices attract considerable attention. A search on any browser using “emotional prosody” as a key phrase leads to more than a million entries. Such interest is evident in the scientific literature as well; readers are reminded in the introductory paragraphs of countless articles of the great importance of prosody and that listeners easily infer the emotional state of speakers through acoustic information. However, despite decades of research on this topic and important achievements, the mapping between acoustics and emotional states is still unclear. In this article, we chart the rich literature on emotional prosody for both newcomers to the field and researchers seeking updates. We also summarize problems revealed by a sample of the literature of the last decades and propose concrete research directions for addressing them, ultimately to satisfy the need for more mechanistic knowledge of emotional prosody.

Larrouy-Maestri, P., Poeppel, D., & Pell, M. D. (2024). The Sound of Emotional Prosody: Nearly 3 Decades of Research and Future Directions. Perspectives on Psychological Science, 0(0). Link 

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The red traffic light makes us stop. Thanks to their color, we immediately spot ripe cherries on a tree. The color red is attributed to a signaling and warning effect. But is this also reflected in the brain? Researchers at the Ernst Strüngmann Institute (ESI) for Neuroscience have now investigated this question. They wanted to know whether red triggers brain waves more strongly than other colors. They published their results regarding “Human visual gamma for color stimuli” in the journal eLife.

The research of Benjamin J. Stauch, Alina Peter, Isabelle Ehrlich, Zora Nolte, and ESI director Pascal Fries focuses on the early visual cortex, also known as V1. It is the largest visual area in the brain. And the first to receive input from the retina. When this area is stimulated by strong and spatially homogeneous images, brain waves (oscillations) arise at a specific frequency called the gamma band (30-80 Hz). But not all images generate this effect to the same extent!

Color is hard to define

“Recently, a lot of research has attempted to explore which specific input drives gamma waves,” explains Benjamin J. Stauch, first author of the study. “One visual input seems to be colored surfaces. Especially if they are red. Researchers interpreted this to mean that red is evolutionarily special to the visual system because, for example, fruits are often red.” But how can the effect of color be scientifically proven? Or refuted? After all, it is difficult to define a color objectively, and it is equally difficult to compare colors between different studies. Every computer monitor reproduces a color differently, so red on one screen is not the same as on another. In addition, there are a variety of ways to define colors: based on a single monitor, perceptual judgments, or based on what their input does to the human retina.

Colors activate photoreceptor cells

Humans perceive color when photoreceptor cells, the so-called cones, are activated in the retina. They respond to light stimuli by converting them into electrical signals, which are then transmitted to the brain. To recognize colors, we need several types of cones. Each type is particularly receptive to a specific range of wavelengths: red (L cones), green (M cones), or blue (S cones). The brain then compares how strongly the respective cones have reacted and deduces a color impression.

It works similarly for all human beings. It would therefore be possible to define colors objectively by measuring how strongly they activate the different retinal cones. Scientific studies with macaques have shown that the early primate visual system has two color axes based on these cones: the L-M axis compares red to green, and the S – (L+M) axis is yellow to violet. “We believe that a color coordinate system based on these two axes is the right one to define colors when researchers want to explore the strength of gamma oscillations. It defines colors according to how strongly and in what way they activate the early visual system,” Benjamin J. Stauch says. Because previous work on color-related gamma oscillations has mostly been run with small samples of a few primates or human participants, but the spectra of cone activation can vary genetically from individual to individual, he and his team wanted to measure a larger sample of individuals (N = 30).

Humans perceive color when photoreceptor cells, the so-called cones, are activated in the retina. They respond to light stimuli by converting them into electrical signals, which are then transmitted to the brain. If maximally colored colors are selected in the RGB color space, the brain reacts with gamma oscillations to different degrees (upper row). The bottom row shows colors that activate the retinal cones to the same extent and create equally strong gamma oscillations in the brain. © ESI/C. Kernberger

Red and green have equal effect

In doing so, Benjamin J. Stauch and his team investigated whether the color red is something special and whether this color causes stronger gamma oscillations than green of comparable color intensity (i.e., cone contrast). And a side question was: Can color-induced gamma oscillations also be detected by magnetoencephalography (MEG), a method for measuring the magnetic activities of the brain?

They conclude that the color red is not particularly strong in terms of the strength of the gamma oscillations it induces. Rather, red and green produce equally strong gamma oscillations in the early visual cortex at the same absolute L-M cone contrast. Moreover, color-induced gamma waves can be measured in human MEG when treated carefully, so future research could follow the 3R principles for animal experiments (Reduce, Replace, Refine) by using humans rather than nonhuman primates. Colors that activate only the S-cone (blue) generally appear to elicit only weak neuronal responses in the early visual cortex. To some extent, this is to be expected, since the S-cone is less common in the primate retina, evolutionarily older, and more sluggish.

Development of visual prostheses

The results of this study led by ESI scientists, understanding how the early human visual cortex encodes images, may one day be used to help develop visual prostheses. These prostheses may attempt to activate the visual cortex to induce vision-like perceptual effects in people with damaged retinas. However, this goal is still a long way off. Before, much more needs to be understood about the specific responses of the visual cortex to visual input.

Stauch BJ, Peter A, Ehrlich I, Nolte Z, Fries P (2022). Human visual gamma for color stimuli. eLife 11, e75897. Link

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Through the interaction between thousands to millions of neurons, the cerebral cortex (outer layer of the brain) generates dynamic patterns of activity at many levels: From the scalp to mesoscopic field potentials at the brain surface to the voltage potentials inside neurons. Just like the interaction between particles can cause certain rhythmic behaviors at a macroscopic pattern, like molecules in a gas, the interactions between millions of individual neurons can lead to network phenomena that show certain statistical regularities. One example is the rhythmic patterns of activity around 30-80Hz (gamma frequency) which can be observed in the visual cortex when animals and subjects see stimuli with high spatial regularity. A new paper from researchers at the Ernst Strüngmann Institute (ESI) for Neuroscience in Frankfurt, Germany, published in Nature Communications, sheds new light on how these rhythms emerge from basic interactions between inhibitory and excitatory neurons. ESI scientist Georgios Spyropoulos led the study.

Rhythms in nature can be very regular

The heartbeat, the circadian rhythm, and the earth’s rotation around its axis are just a few examples of rhythms in nature that can be very regular sometimes. In these cases, we can speak of self-sustained, autonomous oscillators, which can be synchronized by an external force. For example, think of the entrainment of our circadian rhythms by daylight or the heart by a cardiac pacemaker. Yet, gamma rhythms found in the brain show a lot of variability in the amplitude and duration of cycles. They also may have different characteristics and functional properties compared to these self-sustained, regular oscillators (like the heart). Nevertheless, almost all previous models of these gamma rhythms are based on models of self-sustained oscillators.

Georgios Spyropoulos and co-authors now show that the properties of the gamma rhythm can be very well explained by a different kind of model: a linear, damped harmonic oscillator driven by noise. In such a system, quasi-oscillatory behavior emerges because of resonance to noise (some non-rhythmic input). Damped harmonic oscillators are not genuine, self-sustained oscillators that can show synchronization or can be entrained. Imagine a ping pong ball: If you apply some force to it by pushing it off the table, it can transiently move in a certain frequency range and will dissipate its energy before it comes to a halt again. So in this case, we can think of the brain area (or a part of a brain area) as a damped harmonic oscillator that is driven by some external, non-rhythmic inputs from the previous brain area and shows some quasi-period behavior because it amplifies the noisy inputs in some frequency range. This kind of system does not really oscillate by itself but simply amplifies the noisy inputs in a certain frequency range.

A simple and elegant model

The authors analyze many properties of how the amplitude and duration of the rhythm vary. They provide a simple and elegant model of many properties of the gamma rhythm. The authors show that certain strong relationships that have been reported in the past, like a correlation between the instantaneous amplitude and duration of the gamma rhythm, can be explained by a much simpler mechanism than assumed. For example, they show that the duration of the current and the next cycle are essentially uncorrelated, which can be explained by the noise-driven behavior of the oscillator. Furthermore, they demonstrate why bursts emerge when amplitudes remain high for a while, as it naturally takes time for the oscillator to relax.

Finally, they show that, unlike previously proposed, the amplitude and the frequency of the rhythm are only weakly correlated. “We started this project by trying to find similar statistical properties as previously described for the hippocampus but this time in the monkey visual cortex,” Georgios Spyropolous tells. “But when we used the same method on noise signals that did not contain any rhythm, we observed the same correlations. We thus had to spend a lot of time developing and testing new methods to characterize the instantaneous properties of brain signals. When we applied these methods, a simple linear model was sufficient to explain the remaining correlations.“ Even though the authors only analyzed activity in area V1, they believe that their model may potentially explain quasi-rhythmic behavior in many other brain areas or species.

“Gamma is only a real ‘rhythm’ during certain periods”

Senior author and ESI research group leader Martin Vinck explains some aspects of the functional ramifications of the work: “A lot of the conceptual theories on gamma rhythms and communication are based on the idea of synchronization and entrainment. But for these linear, damped harmonic oscillators, it remains unclear how those ideas and concepts make any sense. Furthermore, we observe a high degree of variability from cycle to cycle, which means that small differences in the frequency of a rhythm would need a long integration time to be decoded. That is quite consistent with work by Thomas Akam and Dimitri Kullmann. Finally, the gamma ‘rhythm’ is only a real ‘rhythm’ during certain periods when the amplitude is high, just by chance. But there are many periods where the rhythm disappears. If these rhythms were necessary for inter-areal communication, you would expect a system that showed a lot of variabilities. Whether this is a bug in the system or a design feature – we don’t know…”

It is clear that the authors have set a new standard for analyzing rhythms in the cortex and how to understand (and not to understand!) their moment-to-moment variability. The work challenges existing conceptions of these rhythms and their function but may pave the way toward new theories based on the model developed by the authors.

Spyropoulos G, Saponati M, Dowdall JR, Schölvinck ML, Bosman CA, Lima B, Peter A, Onorato I, Klon-Lipok J, Roese R, Neuenschwander S, Fries P, Vinck M. Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise. Nature Communications 13(1), 2019 (2022). Link

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What is the favorite snack of macaques? How are marmosets housed? Which room is a must-stop for anyone before entering the rodent husbandry? A 360-degree tour of the animal facility gives all the answers. This newly created feature on the website of the Ernst Strüngmann Institute (ESI) for Neuroscience in Frankfurt/Main shows how research animals are kept, cared for, and fed.

Only those who communicate transparently about animal research can hope to demystify the subject and achieve greater understanding among the general public. For this reason, the ESI has published this virtual tour of its animal facilities.

Click here for the tour.

To provide the macaques with all the necessary nutrients, they are given a wide variety of dry food, fruits, and vegetables, as well as yogurt and oatmeal. A balanced diet is crucial to the health of the animals.

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Anyone who has ever had to concentrate on an important task knows how difficult this is when people are talking around you. Especially, if the talk is unnecessary. The primary visual cortex, also known as V1, faces a similar problem time and again: In V1, the flashing of an image on the retina sets millions of neurons in motion. Their job is to segment and filter visual information to produce an accurate and useful representation of what the eye sees. To do this neighboring neurons talk to each other. It can be redundant if they see similar things.

V1 cannot ask for silence as we do. However, it has mechanisms to avoid unnecessary talking. One of these mechanisms may be predictive coding. It involves comparing predictions in the brain with actual sensory input and passing only the prediction errors to the next higher area of the visual cortex. Thus, neurons in V1 would predict or complement what their neighbors are trying to say because they see the same or very similar parts of an image. It prevents every neuron speaks up at the same time. And the brain can concentrate on more important things.

Tests with natural stimuli

Researchers at the Ernst Strüngmann Institute (ESI) for Neuroscience have now shown that synchronization and firing rate of neurons play important, albeit different, roles in this context. They recently published their findings in the prestigious journal Neuron.

The neuroscientists showed photos of flowers, trees, buildings, and other natural or man-made objects to three macaque monkeys. And simultaneously measured their brain activity in the primary visual cortex (V1). For natural stimuli, the information for the receptive fields of V1 can often be predicted by context. For example, in the case of a tree trunk, the outline, even if partially obscured, is likely to continue vertically. Or, in the case of an apple, slightly curved. To measure the predictability of the image features, the researchers programmed an artificial neural network.

Prediction errors increase firing rates

“We observed that neurons fire particularly strongly when prediction errors occur. By using deep neural networks, we were able to precisely pinpoint what kind of prediction errors V1 neurons care about. In principle, V1 neurons could predict all kinds of features in the image, its precise pixel structure for example. However, it turns out that they mostly care about predicting features that are important for the recognition of objects. This kind of predictability determines the salience of objects in an image, that is where humans would look at in an image,” says Cem Uran. He is a PhD student in the Vinck Lab and one of the first authors of the study, along with ESI-scientist Alina Peter.

The researchers also shed light on the long-standing question of the function of brain waves in the gamma frequency range. There have been many theories on this subject. A paper from Vinck & Bosman had proposed in 2016 that gamma brain waves occur when visual inputs can be well predicted, while other researchers were of the opposite opinion. Cem Uran adds: “Indeed, gamma brain waves are particularly strong when predictions are correct. Surprisingly, they were strongest for regions in the image with low salience, containing a lot of redundant information. We are proud to claim that it predicts the amplitude of gamma oscillations better than any competing model. With these results, we get a step closer to answering the question of how the brain processes information.”

Implications for gamma brain waves

Research group leader Martin Vinck specifies, that these findings have important implications for the function of gamma brain waves. They fit well with new evidence from their laboratory that these waves may suppress feedforward communication to higher areas rather than promote it: “You can think of gamma oscillations as an extreme state that occurs when all the information in an image is redundant and where V1 can already figure out what’s happening in the image without having to pass this information on to higher areas. But when there is something salient and non-predicted in the image, these gamma waves collapse and neurons increase their firing rates, which will lead to communication with higher brain areas.”

Ultimately, these findings may have important implications for how the brain learns in a self-supervised way from sensory data itself. The researchers speculate that the brain can continuously learn from the sensory inputs it receives by playing a “prediction game” where it tests its predictions against the incoming visual inputs. The kind of artificial network the researchers developed may emulate how the brain implements this. Which again could lead to a fundamental theory of learning in the visual cortex.

Uran C, Peter A, Lazar A, Barnes W, Klon-Lipok J, Shapcott KA, Roese R, Fries P, Singer W, Vinck M (2022). Predictive coding of natural images by V1 firing rates and rhythmic synchronization. Neuron.
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A new mathematical theory of coherence and communication

Marius Schneider, Martin Vinck, and their lab colleagues have now turned this approach on its head. In their current article, they developed a new mathematical theory of coherence and communication. They show that coherence simply occurs because individual neurons are active in one area and generate (synaptic) inputs into other areas. As a result, the electric signals in separate areas become coherent in a predictable way. “We developed an elegant mathematical formula (shown above) to prove that coherence is a direct consequence of the anatomical connection between two brain areas and signal power. Coherence is not a prerequisite for communication,” Martin Vinck summarizes.

They also found out that coherence is mainly determined by the sending brain area, not the receiving one, and that coherence provides little information on whether communication is actually taking place. This can be compared to the following scene: A person is walking on the street when somebody calls them from the other side. Maybe the person stops because they heard something. Maybe the person continues walking because they did not understand the language. Maybe there is no reaction at all because a passing truck drowns out the call. In all cases, the call was made, and coherence was created between the sounds on the opposite side of the street. But just because the call was made, does not mean that the message was received or processed. And it does not mean that coherence is the mechanism by which the sound is transmitted.

Inter-areal coherence scales with: the sender oscillation strength (alpha), the connection strength (w), the synchronization of neurons in the sender region (phi), and the number of projecting neurons (N) | © ESI/C. Kernberger

Results in record time thanks to teamwork

To prove their theory, the two neuroscientists applied their equations to a large number of already existing data sets. Their colleagues also collected new data from mice and macaques, using new technologies like Neuropixels probes and optogenetics, a light-driven technique to control the activity of neurons. “This was a real team effort. Without the support and great work of all the people at our lab, we would never have progressed so quickly,” says Martin Vinck, praising the contribution of the members of his research group to this paper. “The whole thing really happened in record time,” adds Marius Schneider, letting his gaze wander through the ESI library. Sure enough, he is referring to the teamwork of his lab mates. But it also goes for him and his first major publication as a PhD student.

Schneider M, Broggini AC, Dann B, Tzanou A, Uran C, Sheshadri S, Scherberger H, Vinck M (2021). A mechanism for inter-areal coherence through communication based on connectivity and oscillatory power. Neuron 109(24), 4050-4067.e12.
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We know that neurons of different shapes perform different computations. But do the different shapes also help to make their functions more similar? Hermann Cuntz, a researcher at the Ernst Strüngmann Institute (ESI) for Neuroscience, and colleagues report that neurons with vastly different sizes and shapes of their processes, called dendrites, display surprising equality. The researchers termed this new principle dendritic constancy. Their findings were recently published in Neuron, one of the leading journals in their field.

Dendrites have been ignored
In the 1960s, Wilfrid Rall successfully applied equations from cable theory to neurons. It meant that he could calculate the propagation of currents in dendritic trees, the input structures of neurons. Until then, the contribution of dendrites to neuronal function had been largely ignored. But Rall could show that electrical signals from individual inputs across the dendrites attenuated dramatically and could interact in sophisticated ways – a turning point for our understanding of neural computation. Rall’s cable theory is today the basis for all detailed models representing single neurons or entire circuits in the brain. Meanwhile, because of the distinct shape of their dendrites, different cell types have been attributed to a wide variety of electrophysiological behaviors.

Responses are independent of shape or size
In their recent paper, Cuntz and colleagues explored a feature of cable theory that seems to generalize across diverse dendritic trees: instead of following the impact of single inputs, the authors considered synaptic activity when it distributes across the whole or parts of the dendritic tree. Interestingly, the cable then tends to collapse to a single point, making the neuron’s responses independent of the dendrite’s shape or size. This means that the input-output function of neurons may conveniently not change during development when dendrites undergo massive structural changes. Hence the term “dendritic constancy” to name this phenomenon. The constancy of dendrites may not only play a role during development but may also be of relevance in the context of neurological diseases. Early during the course of such a disease, neurons not yet affected remodel their trees to maintain information processing in the neuronal network for as long as possible. Thus, dendritic constancy is a naturally occurring mechanism partially counteracting the damage caused by various brain disorders such as Alzheimer’s disease or epilepsy. As a result, dendritic constancy may help to delay the onset of disease symptoms, such as memory loss.

Contribution to the 3R
This computational work was a collaboration between the Cuntz lab at the ESI, Thomas Deller from the Anatomy department of the Goethe University in Frankfurt, and the group of Peter Jedlicka at the newly established 3R Center of the Justus Liebig University in Giessen. Indeed, in line with the 3R principle of animal welfare (reduce, replace, refine), the work has shown to be a prime example for computational models being excellent tools for exploiting shared experimental datasets thereby potentially reducing the number of new required animal experiments.

A better understanding of general principles
So in a way, neurons are more equal than one may think. Importantly, however, dendritic constancy does not diminish the importance of the large palette of synaptic learning rules and local computations in dendrites that make neurons unique and are sure to keep neuroscientists on their toes for times to come. Nevertheless, the authors believe that seeing how dendrites can behave equally across scales will contribute to a better understanding of general principles in neuronal function. Intriguingly, a recent follow-up study has shown that the normalization of synaptic input weights based on the dendritic constancy mechanism improves the learning performance in artificial neuronal networks. Thus, dendritic constancy may be of interest not only for neuroscience but also for the machine learning community.

Cuntz H, Bird AD, Mittag M, Beining M, Schneider M, Mediavilla L, Hoffmann FZ, Deller T, Jedlicka P (2021). A general principle of dendritic constancy: A neuron’s size- and shape-invariant excitability. Neuron 109(22), 3647-3662.
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