For over 50 years, it has been known that in the cerebral cortex of many mammals, neurons with the same function are grouped into columns. Now, for the first time, researchers at the Max Planck Institute for Biological Intelligence have been able to demonstrate these structures in the visual cortex of mice: here, neurons that process stimuli from the same eye form clusters. This adds to our general understanding of the structural organization of the brain – and may help to solve the mystery of the columnsˈ function.

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Motion, color, light and shadow: everything we see is the result of complex computations in our brain – or, more precisely, in the visual cortex. This is where stimuli that hit our retina are broken down into their individual components, processed and assembled into what we perceive. The neurons responsible for this process can each perform different tasks: for example, some mainly process motion, others lines or colors.

In the 1960s, David Hubel and Torsten Wiesel famously discovered that in the visual cortex, neurons with the same function are organized spatially in columns. This finding, along with their other discoveries about visual processing, was awarded the Nobel Prize in Physiology or Medicine in 1981. These so-called cortical columns have been considered elementary building blocks in the cerebral cortex of many mammals – including humans. However, such structures had not yet been detected in the visual cortex of many smaller animals, such as mice. As a result, cortical columns were thought to be reserved for mammals with more complex brains and particularly good eyesight.

A team led by Mark Hübener and Tobias Bonhoeffer has now shown for the first time that neurons are also arranged in columns in the visual cortex of mice. Using a technique called 2-photon microscopy, they discovered clusters of neurons that process visual information coming from the same eye. These clusters were most distinct in the middle layers of the visual cortex. However, the spatial proximity of cells processing input from the same eye continued in the overlying and underlying layers, thereby forming so-called ocular dominance columns.

Although the columnar organization of the cerebral cortex was described more than half a century ago, the function of these columns is still a matter of speculation. “A possible explanation for the cortical columns can be illustrated by where fans sit on the stands in a football stadium,” says Pieter Goltstein, the study`s first author. “If all the fans of one team are sitting together and cheering for their team at the same time, it is much more powerful than if the fans are spread out all over the stadium. It is possible that neurons with the same function can also work more efficiently when they are close together.”

The new study not only advances our general understanding of how the brain is organized. It also makes it possible to study the function of the cortical column in the mouse model organism – to perhaps ultimately answer the question what columns are good for.

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New research from the Max Planck Florida Institute for Neuroscience demonstrates that inhibitory and excitatory neuronal circuits of the visual system develop through different processes, even if the organization of the mature circuit is similar. These findings, published in Nature Communications highlight the importance of the continued study of the development of these two systems, the understanding of which is fundamental to comprehending neurodevelopmental disorders.

An area of the brain that processes visual information, the primary visual cortex, is highly organized, forming patches of neighboring neurons that tend to be active together and respond to similar visual features. In mammals, these modular functional maps consist of both excitatory and inhibitory neurons that work together to create an accurate representation of the world.

Scientists Jeremy Chang and David Fitzpatrick have now characterized the development of these functional maps for inhibitory neurons in primary visual cortex. Although excitatory and inhibitory functional maps are matched at maturity, their development occurs through different parallel processes.

Excitatory neurons show modular organization early on, before the eyes open and visual input is received. Neighboring neurons respond to visual images in a correlated fashion and show similar preferences for stimuli presented in specific orientations. While visual experience refines particular properties of these maps, such as the alignment of visual information from each eye, the basic features of the modular organization are present before visual experience.

Dr. Chang found that inhibitory neurons, on the other hand, lack much of this modular activity before visual experience. “This came as a surprise,” he admitted. “We were not expecting the functional maps seen before eye-opening in excitatory neurons to be almost absent in inhibitory neurons.” This suggested that developing mature functional organization of inhibitory neurons requires visual experience. In fact, if visual input was delayed, the development of many features of the functional inhibitory neuron maps was also delayed.

This work contributes to the fundamental understanding of larger questions about the role of inhibition in the cortex, which the lab will continue to pursue. “New techniques developed over the last decade have allowed us to image the activity of inhibitory neurons in response to visual images. We are beginning to understand the functional importance of inhibition in visual processing and how the role of inhibition changes throughout development. During development, inhibitory and excitatory neurons have to solve different puzzles to end up in the correct place, connect to the appropriate partners, and refine their connections in response to experience,” said Chang. Future work will focus on understanding how these puzzles are solved.

This research was supported by the National Eye Institute of the National Institutes of Health under award numbers EY011488 and EY026273 and the Max Planck Florida Institute for Neuroscience. This content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Chang, J. T., & Fitzpatrick, D. (2022). Development of visual response selectivity in cortical GABAergic interneurons. Nature Communications, 13(1), 3791. Link

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This study, led by first author Dr. Joseph Schumacher and senior author Dr. David Fitzpatrick at the Max Planck Florida Institute for Neuroscience, establishes a transformative approach to studying perceptual learning in the brain. Researchers imaged the activity of large numbers of single neurons over days to track the changes that occur while a visual discrimination task is learned, performing these experiments in a novel animal model, the tree shrew.

The tree shrew is a small mammal with visual properties akin to the human, including a high degree of visual acuity and a similar orderly spatial arrangement of visually responsive neurons in the brain. As the researchers show, these animals can also learn complex behavioral tasks, making them ideal for understanding how experience shapes visual perception. In this study, tree shrews were trained to discriminate between highly similar visual images: identical black lines that differed only by a small change in orientation (22.5 degrees). In the task, the presentation of the lines at one orientation was rewarded with a drop of juice. Over days, tree shrews learned to discriminate between the two similar visual images, licking only in response to the lines at the rewarded orientation and withholding licking to the lines at the non-rewarded orientation.

The scientists combined this behavioral task with measurements of neural activity in V1, an area of the brain essential for visual processing. The neurons in this area are activated by specific features of visual input, such as the orientation of light-dark edges. Individual neurons show ‘preference’ for specific edge orientations, responding with the highest activity to these orientations and with progressively lower activity or no activity to edges orientated further from the preferred orientation. In this way, a visual scene that has edges with different orientations activates particular subsets of neurons to generate a neural activity pattern that encodes the information needed for visual perception.

Schumacher and colleagues found that visual discrimination learning in the tree shrew was accompanied by enhancement of the difference in the patterns of neural activity evoked by the two visual images. This was primarily due to an increase in the amount of neural activity in response to the presentation of the rewarded stimulus orientation relative to the non-rewarded orientation. But this was not just a general increase in neuronal responses to the rewarded stimulus. When the scientists examined the changes more closely, they found that this was mediated by changes in the activity of a remarkably specific subset of neurons: those whose orientation preference was optimal for distinguishing the orientation of the rewarded stimulus from the non-rewarded stimulus.

To fully understand the effect of learning on visual perception, the authors next investigated whether the changes in neuronal activity that improved visual discrimination persisted outside of the learned task context. Interestingly, they found that the neuronal changes not only persisted but were accompanied by changes in the trained tree shrew’s abilities to perform other discriminations. This included both enhancements for some stimulus orientations and impairments for others— behavioral changes that were exactly what would be expected given the changes in the responses of this specific subset of neurons.

“This work demonstrates specific experience-driven changes in the activity of neurons that impact the perception of visual stimuli, enhancing discriminations relevant to task performance at the expense of other related discriminations,” explains first author Joe Schumacher. Now the lab has set its sights on combining this approach with new technologies to unlock the sequence and changes that occur in multiple types of neurons in order to mediate perceptual learning. By probing these questions in the visual system of the tree shrew, scientists in the Fitzpatrick lab are discovering fundamental new insights about perceptual learning that could impact our understanding of a broad range of learning disorders.

This research was supported by the Max Planck Florida institute and the National Eye Institute of the National Institutes of Health through award numbers F32 EY028430 and R01 EY006821. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

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Sedigh-Sarvestani, M., & Fitzpatrick, D. (2022). What and Where: Location-Dependent Feature Sensitivity as a Canonical Organizing Principle of the Visual System. Frontiers in Neural Circuits, 16.
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In a new paper published in the journal, Neuron, researchers from Max Planck Florida’s Fitzpatrick lab, led by Ph.D. student Clara Tepohl and former Postdoc Benjamin Scholl, report exciting new discoveries about what takes place in the brain when signals from the right and left eye come together.

Clara is a student in the joint FAU-MPFI IMPRS program. She studied physics at the University of Heidelberg, and went on to receive her master’s degree in neuroscience from the University of Göttingen, Germany before pursuing her Ph.D in the lab of Dr. David Fitzpatrick. Dr. Benjamin Scholl worked in the Fitzpatrick lab as a postdoc before going on to start his own lab at the University of Pennsylvania in the summer of 2021. Together, they set out to better understand how the brain “sees” the world.

“Binocular vision is a particularly interesting ‘problem’ to me,” Clara explained. “Having two eyes improves your vision in various ways but it also poses the problem that the brain must integrate the information from the two eyes. Each eye sees the world from a slightly different angle and yet we perceive one coherent image. How the brain and specifically individual cells manage to combine visual information from the two eyes so that it is properly aligned, is really fascinating to me.”

To better understand the neuronal circuits of vision, researchers used 2 photon calcium imaging to visualize the activity of individual cells and the inputs they receive from other cells via synapses on their dendritic spines. This allowed the researchers to measure what information the cells receive from other cells and how it is integrated during visual stimulation. Later, a smaller sample of these cells were studied using electron microscopy which allowed the researchers to measure the strength of each connection.

What they found is that individual cells receive a variety of different inputs. Some convey information from one eye only (monocular), some convey signals from both eyes (binocular). The simple but dominant idea in the field is that alignment comes from the convergence of matched monocular input streams onto individual neurons. However, they were surprised to find that the monocular inputs to the same neuron don’t match each other. Instead, they found that synaptic interactions between binocular cortical neurons generate aligned responses.

“I was at first surprised that binocular inputs were the key players for binocular alignment. Especially since they make up only 1/3 of inputs to a single cell and electron microscopy tells us they aren’t ‘stronger’ than other inputs. But with further analyses we found that they have additional properties that allow them to have a disproportionate impact in generating aligned responses ,” Clara said.

As a Ph.D. student, projects like this help Clara lay a foundation toward a scientific career. One of the benefits of Max Planck Florida’s IMPRS Ph.D. program is that students not only receive training, but they also benefit from mentorship, such as working with postdoctoral scholars like Ben. IMPRS students work in collaborative environments and are given opportunities to play key roles pursuing new and exciting questions. Clara, who has been an IMPRS student since 2018, has enjoyed the experience of discovering new insights.

“When we started this project, I was new in the lab and new to the technique, so recording the data was very exciting to me. Seeing individual cells and dendritic spines light up according to the stimulus you play, seeing the brain and such small components of it live in action – that was so fascinating to me and actually mesmerizes me to this day,” she said.

Now that the researchers have determined which inputs are most important to binocular alignment, the next step is to better understand where these inputs are coming from, and to unravel how these complex networks work together to create a coherent picture in our minds. Not only does this research have implications for disorders of the vision, but it is also an important piece of the puzzle of how the brain processes signals from distinct sources.

Whatever direction her research goes next, the entire team is looking forward to the challenge of studying sensory integration. “The computations the brain performs truly fascinate me and vision is a particularly intriguing example to me,” Clara explained. “At the level of the retina, all that is detected by little receptors is changes in luminance. From that, the brain manages to extract and distinguish various features, such as edge orientation, motion, and ultimately more complex objects like faces. How cool is that?”

Benjamin Scholl, Clara Tepohl, Melissa A. Ryan, Connon I. Thomas, Naomi Kamasawa, David Fitzpatrick. A binocular synaptic network supports interocular response alignment in visual cortical neurons. Neuron, Online Publication
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Requiring a discerning eye, mathematical precision, and keen sense of aesthetics, map making is a unique application of both art and science. Though the scale may differ, neuroscientists that study vision are like cartographers of the brain; investigating and mapping how our brain represents and makes sense of what we see in the world. The visual cortex, a specialized region responsible for visual processing, contains intricate neural circuits that evaluate information arriving from our eyes and preferentially respond to distinguishing visual features such as color, edges, motion, and location in visual space. Despite the sheer complexity of this information, our brains do a remarkable job of efficiently organizing neurons together, helping us to better understand our visual landscapes.

One major organizational property the visual cortex employs is called retinotopic mapping, where neurons within are arranged in an orderly way that preserves the spatial information arriving from the retina (light sensing portion of the eye). Much like the Mercator projection is to cartography, retinotopic maps in the visual cortex are thought to follow a widely adopted and well-characterized pattern. The prevailing theory is that brain areas like the primary visual cortex (V1) follow a smooth and simple method of mapping. What you see is what you get; objects in visual space that activate portions of the retina, will light up neurons in an identical pattern in the brain.

Despite the wealth of evidence in multiple species supporting this type of linear mapping, small hints and discrepancies existed within previous studies that suggested the possibility of other arrangements. The question remained, do additional methods of spatial mapping exist in the brain?

Shedding light on this question and challenging the prevailing theory, researchers in MPFI’s Fitzpatrick Lab have uncovered for the very first time a new type of spatial mapping within the secondary area (V2) of the visual cortex. Published recently in Neuron, the team employed a combination of single-cell functional imaging, computational modeling and connectivity studies, to reveal a sinusoidal or wavelike organization in area V2 of the tree shrew. Their surprising insight has deepened our understanding of neural representations of visual space and underlined the importance of precise retinotopic mapping in the visual cortex.

Madineh Sedigh-Sarvestani, Ph.D., a postdoctoral researcher at MPFI and first author of the publication, joined the Fitzpatrick lab interested in understanding the organization, function, and behavioral link of visual areas beyond the well-studied V1. Her investigation began in V1’s closely related neighbor, V2, a visual area that has been extensively studied in primates but less so in animals amenable to recent genetic tools developed in mice. The tree shrew perfectly fits this criterion, as it’s a close relative of primates and has a smooth brain ideal for imaging. Utilizing high resolution calcium imaging, Sedigh-Sarvestani expected to find a map of visual space very similar to V1’s golden-standard.

Presenting tree shrews with visual stimuli that varied in position within the visual field, the team mapped the corresponding neurons in V2 that lit up in response to a visual stimulus’ location in space. What they discovered was two very distinct maps in V2. The map of an object’s elevation, how high or low it is, followed closely with the smooth linear map found in V1 but mapping the azimuth, its horizontal position left or right of center, revealed a dramatically different sinusoidal, or oscillating pattern. But why would simple spatial maps exist in V1 and more complex in V2, could differences in the regions’ shapes play a role? To answer this question the MPFI researchers turned to computer modeling to recreate the conditions found within the brain, with the goal of producing a spatial map that optimizes coverage of the visual field. By varying only shape, the algorithm found that the optimal spatial map for the square V1 region followed the smooth, linear arrangement but for the thin, elongated V2, a sinusoidal map emerged corroborating previous results. Cementing this idea, the MPFI team, led by the lab’s histology coordinator Nicole Shultz, used colored dyes to trace the connections from V1 to the V2 region, finding that the neuronal projections from V1 perfectly aligned with the sinusoidal map in V2.

“Our results demonstrate that orderly organization of visual space in the brain, does not necessarily have to follow the guiding principles we are accustomed to thinking about,” notes David Fitzpatrick, Ph.D., CEO and Scientific Director of MPFI. “Though this organization may be less straightforward than we originally thought, it still has remarkable and beautiful order.”

Beyond this intriguing finding, researchers in the Fitzpatrick lab made one more critically important discovery with broad implications for the field of visual neuroscience; neuronal preference for certain visual features is tied directly to the retinotopic map of visual space. Predominantly thought to be independent organizational principles, the MPFI team demonstrated their interconnectedness by studying the response properties of neurons in V2 for binocular or monocular stimuli. They found that the oscillating map of visual space completely overlapped with the functional feature map, illustrating that the sensitivity neurons have for visual features is not uniform but can vary depending on where the features are in visual space.

“This type of synergy between these two principles, preference for visual features and their location in space, starts to reveal unique information about the behavior or environment of certain animals,” describes Sedigh-Sarvestani. “The wiring of visual circuits that determine the patterns that end up in the brain are influenced by our visual experience; What you see and where you see it.”

In the future, Sedigh-Sarvestani plans to investigate if other visual features are tethered to mapping of visual space in different regions of the visual cortex and if this organization can be traced back to the retina and eventually, an animal’s native environment and their movements within that environment; leading to a more comprehensive understanding of visual perception.

“What we found really forced us to rethink how maps of visual space are formed and to recognize that neural circuits in the visual cortex can be functionally specialized for different regions of visual space,” explains Fitzpatrick. “Our findings open the door to a different way of thinking about how cortical circuits are organized, how they contribute to visual perception, and ultimately, behavior.”

This work was supported by the National Institutes of Health Grants, and the Max Planck Florida Institute for Neuroscience. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Sinusoidal transform of the visual field in V2

The video shows the sinusoidal transformation between the visual field and V2. It shows how regions in the visual field are mapped onto regions in V2. The colors correspond to the azimuth (left/right) axis of the visual field. 

Madineh Sedigh-Sarvestani, Kuo-Sheng Lee, Juliane Jaepel, Rachel Satterfield, Nicole Shultz, David Fitzpatrick (2021). A sinusoidal transformation of the visual field is the basis for periodic maps in area V2. Neuron, Online now.
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Researchers unravel a mechanism behind distinct organizational features in the visual cortex of mouse and ferrets

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Micha Heilbron, David Richter, Matthias Ekman, Peter Hagoort, and Floris P. de Lange. (2020). Word contexts enhance the neural representation of individual letters in early visual cortex. Nature Communications 11, 321.
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The post Word contexts enhance the neural representation of individual letters in early visual cortex first appeared on Max Planck Neuroscience.

Ramachandra, V., Pawlak, V., Wallace, D. J., Kerr, J.N (2020). “Impact of visual callosal pathway is dependent upon ipsilateral thalamus”. Nat Comm, 11, 1889.
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The post Impact of visual callosal pathway is dependent upon ipsilateral thalamus first appeared on Max Planck Neuroscience.

Neurons are complex, highly connected cells engaged with multiple networks throughout the brain, and they exhibit a wide range of activity. As such, individual neurons can perform many functions. Neurons are generally classified as either excitatory or inhibitory based on downstream effects on other cells, with each cell receiving a diverse array of excitatory and inhibitory synaptic inputs that help shape that cell’s unique properties. In a recent study, researchers at the Max Planck Florida Institute for Neuroscience (MPFI) revealed that inhibitory inputs to neurons in the visual cortex are more diverse than previously thought, suggesting that our current notion of neuronal connectivity may only reflect a part of the whole picture. The team of researchers explored how neurons are wired together and what effect these connections have on neuronal properties. Using genetic tools, imaging techniques, and optogenetics, they showed that inhibitory inputs onto single neurons can deviate from the canonical view of cortical circuits. The presence of this surprising, differentially-tuned inhibition suggests that cortical connectivity is more flexible than originally assumed, allowing for multiplexed computations.

Few studies have mapped inhibitory inputs onto neurons within intact brain circuits. Despite the wide variety of techniques to visualize excitatory connections, there are almost none readily available to study co-occurring inhibitory connections. Dr. Benjamin Scholl, Senior Research Scientist in the lab of Dr. David Fitzpatrick, and Dr. Daniel Wilson, now a postdoctoral researcher at Harvard Medical School, developed a strategy for labeling and mapping local inhibitory inputs onto a cell. They expressed a fluorescent protein specifically in inhibitory neurons, taking advantage of genetic markers to target only these cells, and combined whole-cell patch-clamp recordings with patterned stimulation of neurons to record their individual activity. In the same cells, they also measured their selectivity for different orientations of moving edges.

Scholl and colleagues found that the selectivity of inhibitory inputs may parallel or completely diverge from that of target neurons, revealing a “diverse palette” of inhibition. Previously, it was thought that these inputs should all be co-tuned, with aligned functional preferences. Data from this study suggests that, depending on network activation, inhibitory cells with different tuning profiles are able to uniquely contribute and allow for flexibility of network responses. “These networks are highly interconnected and dynamic, and these studies are beginning to show us that the functional connectivity we hope to uncover is more complicated than we previously believed,” remarks Scholl. Further, understanding the anatomical connectivity, or “connectome,” may not be entirely sufficient to understand brain circuits, emphasizing the need to map and elucidate functional connectomes in the brain.

The rules governing neuronal tuning are in no way simple, with different stimulus conditions evoking different patterns of excitation and inhibition, and there is much more about the intricacies of the visual system that has yet to be uncovered. But advancements in labeling and imaging techniques such as that outlined in Scholl’s paper open the door for future examination of synaptic inputs to individual neurons. “We have to appreciate the full complexity of how individual neurons are engaged in circuits,” states Fitzpatrick. “The power of this paper lies in the technology that allows us to record from individual neurons while presenting a visual stimulus and selectively activating inhibitory neurons.” Scholl and the Fitzpatrick lab hope to understand how excitation and inhibition are used flexibly to encode information, including during early development. Subsequent studies may explore the role of experience in shaping neuronal networks and the varying impact of individual neurons under different stimuli or contexts. The lab also hopes to develop new techniques for better resolution imaging and precise stimulation of single cells to further characterize the role of inhibitory neurons in the visual cortex.

This work was supported by the Max Planck Florida Institute for Neuroscience and National Institutes of Health Grant. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Benjamin Scholl, Daniel Wilson, Juliane Jaepel, David Fitzpatrick. (2019). Functional logic of layer 2/3 inhibitory connectivity in ferret visual cortex. Neuron, Online Publication.
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