Posts tagged ganglion cells
Articles and news from the latest research reports.
Wiring diagram of retinal neurons is first step toward mapping the human brain.
The human brain has 100 billion neurons, connected to each other in networks that allow us to interpret the world around us, plan for the future, and control our actions and movements. MIT neuroscientist Sebastian Seung wants to map those networks, creating a wiring diagram of the brain that could help scientists learn how we each become our unique selves.
In a paper appearing in the Aug. 7 online edition of Nature, Seung and collaborators at MIT and the Max Planck Institute for Medical Research in Germany have reported their first step toward this goal: Using a combination of human and artificial intelligence, they have mapped all the wiring among 950 neurons within a tiny patch of the mouse retina.
Composed of neurons that process visual information, the retina is technically part of the brain and is a more approachable starting point, Seung says. By mapping all of the neurons in this 117-micrometer-by-80-micrometer patch of tissue, the researchers were able to classify most of the neurons they found, based on their patterns of wiring. They also identified a new type of retinal cell that had not been seen before.
“It’s the complete reconstruction of all the neurons inside this patch. No one’s ever done that before in the mammalian nervous system,” says Seung, a professor of computational neuroscience at MIT.
Other MIT authors of the paper are former postdoc Srinivas Turaga and former graduate student Viren Jain. The Max Planck team was led by Winfried Denk, a physicist and the Max Planck Institute’s director. Moritz Helmstaedter, a research group leader at the Max Planck Institute, is the lead author of the paper, and Kevin Briggman, a former postdoc at Max Planck, is also an author.
Tracing connections
Neurons in the retina are classified into five classes: photoreceptors, horizontal cells, bipolar cells, amacrine cells and ganglion cells. Within each class are many types, classified by shape and by the connections they make with other neurons.
“Neurons come in many types, and the retina is estimated to contain 50 to 100 types, but they’ve never been exhaustively characterized. And their connections are even less well known,” Seung says.
In this study, the research team focused on a section of the retina known as the inner plexiform layer, which is one of several layers sandwiched between the photoreceptors, which receive visual input, and the ganglion cells, which relay visual information to the brain via the optic nerve. The neurons of the inner plexiform layer help to process visual information as it passes from the surface of the eye to the optic nerve.
To map all of the connections in this small patch of retina, the researchers first took electron micrographs of the targeted section. The Max Planck researchers obtained these images using a technique called serial block face scanning electron microscopy, which they invented to generate high-resolution three-dimensional images of biological samples.
Developing a wiring diagram from these images required both human and artificial intelligence. First, the researchers hired about 225 German undergraduates to trace the “skeleton” of each neuron, which took more than 20,000 hours of work (a little more than two years).
To flesh out the bodies of the neurons, the researchers fed these traced skeletons into a computer algorithm developed in Seung’s lab, which expands the skeletons into full neuron shapes. The researchers used machine learning to train the algorithm, known as a convolutional network, to detect the boundaries between neurons. Using those as reference points, the algorithm can fill in the entire body of each neuron.
“Tracing neurons in these images is probably one of the world’s most challenging computer vision problems. Our convolutional networks are actually deep artificial neural networks designed with inspiration from how our own visual system processes visual information to solve these difficult problems,” Turaga says.
If human workers were to fill in the entire neuron body, it would take 10 to 100 times longer than just drawing the skeleton. “This speeds up the whole process,” Seung says. “It’s a way of combining human and machine intelligence.”
The only previous complete wiring diagram, which mapped all of the connections between the 302 neurons found in the worm Caenorhabditis elegans, was reported in 1986 and required more than a dozen years of tedious labor.
“I think this is going to be a really significant paper in the history of how we study complex systems,” says Richard Masland, a professor of ophthalmology at the Massachusetts Eye and Ear Infirmary, who was not part of the research team. “This paper identifies circuit motifs that are interesting but really are just symbolic of the many types of questions that could be answered using these techniques.”
Classifying neurons
Wiring diagrams allow scientists to see where neurons connect with each other to form synapses — the junctions that allow neurons to relay messages. By analyzing how neurons are connected to each other, researchers can classify different types of neurons.
The researchers were able to identify most of the 950 neurons included in the new retinal-wiring diagram based on their connections with other neurons, as well as the shape of the neuron. A handful of neurons could not be classified because there was only one of their type, or because only a fragment of the neuron was included in the imaged sample.
“We haven’t completed the project of classifying types but this shows that it should be possible. This method should be able to do it, in principle, if it’s scaled up to a larger piece of tissue,” Seung says.
In this study, the researchers identified a new class of bipolar cells, which relay information from photoreceptors to ganglion cells. However, further study is needed to determine this cell type’s exact function.
Seung’s lab is now working on a wiring diagram of a larger piece of the retina — 0.3 millimeter by 0.3 millimeter — using a slightly different approach. In that study, the researchers first feed their electron micrographs into the computer algorithm, then ask human volunteers to check over the computer’s work and correct mistakes through a crowd-sourcing project known as EyeWire.
Switching night vision on or off
Neurobiologists at the Friedrich Miescher Institute have been able to dissect a mechanism in the retina that facilitates our ability to see both in the dark and in the light. They identified a cellular switch that activates distinct neuronal circuits at a defined light level. The switch cells of the retina act quickly and reliably to turn on and off computations suited specifically for vision in low and high light levels thus facilitating the transition from night to day vision. The scientists have published their results online in Neuron.
"It was fascinating to see how modern neurobiological methods allowed us to answer a question about vision that has been controversially discussed for the last 50 years", said Karl Farrow, postdoctoral fellow in Botond Roska’s group at the Friedrich Miescher Institute for Biomedical Research. Since the late 1950 scientists debated how the retina handles the different visual processes at low and high light intensities, at starlight and at daylight. Farrow and his colleagues have now identified a cellular switch in the retina that controls perception during these two settings.
At first glance, everything seems clear. The interplay of two photoreceptor types in the retina, the rods and the cones, allow us to see across a wide range of light intensities. The rods are highly sensitive and spring into action in the dark; the cones are activated during the day and in humans come in three diversities allowing us to see color. The rods help us detect objects during the night; while the cones allow us to discriminate the fine details of those objects during the day. The plethora of initial signals originating from the photoreceptors is computed in a system of only approximately 20 neuronal channels that transport information to the brain. The relay stations are the roughly 20 types of ganglion cells in the retina. How they manage the transition from light to dark and enable vision at the different light regimes has remained unclear.
In the retina several cell layers are stacked on top of each other. The photoreceptors are the first to be activated by light; they relay the information to bipolar cells, which in turn activate ganglion cells. The different types of ganglion cells take on distinct tasks during vision. These ganglion cells are embedded in a mesh of amacrine cells that modulate their activity. “Here is where our new genetic tools proofed very helpful,” said Farrow, “because they allowed us to look at individual ganglion cell types and to specifically measure their activities at different light intensities.” Farrow and colleagues could thus show that the activity of one particular type of ganglion cells, called PV1, is modulated like a switch by amacrine cells. The amacrine cells inhibit the ganglion cell strongly at high light intensities and weakly at low ambient light levels. This switch is abrupt and reversible and it occurs at the light intensities where cones are starting to be activated. “We were surprised to see how fast this switch occurs and how reliable we were able to switch between the two states at defined light intensities”, comments Farrow.
While the above experiments were done in a mouse model, the FMI neurobiologists could show that a similar switch operates in human vision. Their volunteers had to look at narrow and broader stripes at different light levels. They could show that there again a switch operates. While the general ability to see all striped patterns improved with increasing light intensity, suddenly, at a certain light level, the volunteers were much better able to detect thinner patterns as compared to the broader ones. Interestingly enough this switch happened at precisely the light level where the volunteers were also able to discriminate between red and blue, hence where the cones spring into action. “We think we have found a regulatory principle that could apply to several processes in the brain”, said Roska, “This principle could explain some situations when gradual changes in the sensory environment leads to abrupt changes in brain computations and perception”
(Source: medicalxpress.com)
The end of a dogma: Bipolar cells generate action potentials
To make information transmission to the brain reliable, the retina first has to “digitize” the image. Until now, it was widely believed that this step takes place in the retinal ganglion cells, the output neurons of the retina. Scientists in the lab of Thomas Euler at the University of Tübingen, the Werner Reichardt Centre for Integrative Neuroscience and the Bernstein Center Tübingen were now able to show that already bipolar cells can generate “digital” signals. At least three types of mouse BC showed clear evidence of fast and stereotypic action potentials, so called “spikes”. These results show that the retina is by no means as well understood as is commonly believed.
The retina in our eyes is not just a sheet of light sensors that – like a camera chip – faithfully transmits patterns of light to the brain. Rather, it performs complex computations, extracting several features from the visual stimuli, e.g., whether the light intensity at a certain place increases or decreases, in which direction a light source moves or whether there is an edge in the image. To transmit this information reliably across the optic nerve - acting as a kind of a cable - to the brain, the retina reformats it into a succession of stereotypic action potentials – it “digitizes” it. Classical textbook knowledge holds that this digital code – similar to the one employed by computers – is applied only in the retina’s ganglion cells, which send the information to the brain. Almost all other cells in the retina were believed to employ graded, analogue signals. But the Tübingen scientists could now show that, in mammals, already the bipolar cells, which are situated right after the photoreceptors within the retinal network, are able to work in a “digital mode” as well.
Using a new experimental technique, Tom Baden and colleagues recorded signals in the synaptic terminals of bipolar cells in the mouse retina. Based on the responses of these cells to simple light stimuli, they were able to separate the neurons into eight different response types. These types closely resembled those expected from physiological and anatomical studies. But surprisingly, the responses of the fastest cell types looked quite different than expected: they were fast, stereotypic and occurred in an all-or-nothing instead of a graded fashion. All these are typical features of action potentials. Such “digital” signals had occasionally been observed in bipolar cells before, but these were believed to be rare exceptional cases. Studies from the past two years on the fish retina had already cast doubt on the long-held belief that BCs do not spike. The new data from Tübingen clearly show that these “digital” signals are systematically generated in certain types of mammalian bipolar cells. Action potentials allow for much faster and temporally more precise signal transmission than graded potentials, thus offering advantages in certain situations. The results from Tübingen call a widely held dogma of neuroscience into question - and open up many new questions.