neuronal circuit

When somebody says "the man determines the sex of the baby. you can't be trans because it doesn't exist. it's simple science you can't go against biology." this is what I tell them.

“You’re right, and you’re wrong. It’s actually not simple at all. On average, fertilization occurs about two weeks after your last menstrual period. When the sperm penetrates the egg, changes occur in the protein coating around it to prevent other sperm from entering. At the moment of fertilization, your baby’s genetic make-up is complete, including its sex.

If a Y sperm fertilizes the egg, your baby will be a boy; if an X sperm fertilizes the egg, your baby will be a girl. In that sense you are technically right, but gender is determined by so much more than that. We all know that a man’s brain is different to that of his female counterpart, right?” By which point the person who I’m talking to agrees. then I go on to say.

“In month 3 of Pregnancy

the baby has grown from embryo to fetus. by now the baby’s arms, hands, fingers, feet, and toes are fully formed. the baby can open and close its fists and mouth. Fingernails and toenails are beginning to develop and the external ears are formed. The beginnings of teeth are forming, and the baby’s reproductive organs are also developing, but the baby’s gender is difficult to distinguish on ultrasound, because the genitalia start out the same. Differentiation of the male and female reproductive systems does not occur until this crucial fetal period of development.

It is believed by scientists that during the intrauterine period the fetal brain develops in the male direction through a direct action of testosterone on the developing nerve cells, or in the female direction through the absence of this hormone surge. According to this concept, our gender identity (the conviction of belonging to the male or female gender) and sexual orientation should be programmed into our brain structures when we are still in the womb. However, since sexual differentiation of the genitals takes place in the beginning of the third trimester, (The third month of pregnancy) and sexual differentiation of the brain starts in the second half of pregnancy, these two processes can be influenced independently, which may result in trans-sexuality. This also means that in the event of ambiguous sex at birth, the degree of masculinization of the genitals may not reflect the degree of masculinization of the brain. There is no proof that social environment after birth has an effect on gender identity or sexual orientation. Data on genetic and hormone independent influence on gender identity are presently divergent and do not provide convincing information about the underlying etiology. To what extent fetal programming may determine sexual orientation is also a matter of discussion. A number of studies show patterns of sex atypical cerebral dimorphism in homosexual subjects. Although the crucial question, namely how such complex functions as sexual orientation and identity are processed in the brain remains unanswered, emerging data point at a key role of specific neuronal circuits involving the hypothalamus. So yes you are right, it is biology I’m not fighting anything other than ignorance.”


Les larmes viennent arroser des fleurs qui se trouvent à l'intérieur de mon âme
J'ai brûlé mon circuit neuronal à une époque de ma vie peu loquace
Quand ils me demandaient si je me droguais, je leur parlais de la musique
Parce que j'adore m'abrutir avec elle, me l'enveler serait un supplice
Je ne veux pas gaspiller mon temps à devenir ce que je suis déjà
J'ai dans ma tête un idéal qui est moi, sans mes penchants négatifs
—  Lonepsi
Ce train n'a ni queue ni tête, il roule à vive allure
Et écrase tout sur son passage, moteur au bord de la rupture
À l'intérieur c'est la panique, les neurones court-circuitent
Ça va trop vite pour rétablir, on connaît tous la suite
Maudit sois la race des puissants, aiguilleurs du chaos
Leurs instincts suicidaires et leurs penchants pour l'échafaud
Au coin des lèvres, toujours cette arrogance, ce p'tit rictus
Mais ils oublient que pour eux aussi il n'y a qu'un mur comme terminus

Arrêtez ce train ! Arrêtez ce train! Je- je- je veux descendre
Arrêtez ce train ! Là bas il n'y a plus rien, que du feu et des cendres !
Arrêtez ce train ! Arrêtez ce train j'ai dit ! Je veux descendre !
Arrêtez ce train ! La foule le cri, le scande et personnes ne veulent l'entendre!
—  La canaille - Arrêtez ce train
Balancing Time and Space in the Brain: A New Model Holds Promise for Predicting Brain Dynamics

For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, “balanced network theory” has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, “The spatial structure of correlated neuronal variability,” was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural “signatures” that predict brain activity associated with learning or disease, say the investigators.

“Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute,” said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). “To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network’s architecture, and this latest research brings us significantly closer to achieving this goal.”

Earlier versions of the balanced network theory captured how the timing and frequency of inputs—excitatory and inhibitory—shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

“The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior—either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between.”

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, “It’s like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication.”

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons—whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

“This model will help us to better understand how the brain computes information because it’s a big step forward in describing how network structure determines network variability,” said Doiron. “Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson’s disease and epilepsy.”

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

“A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don’t have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected,” said Nathan Urban, associate director of UPBI.

Supporting the damaged brain

A new study shows that embryonic nerve cells can functionally integrate into local neural networks when transplanted into damaged areas of the visual cortex of adult mice.

(Image caption: Neuronal transplants (blue) connect with host neurons (yellow) in the adult mouse brain in a highly specific manner, rebuilding neural networks lost upon injury. Credit: Sofia Grade, LMU/Helmholtz Zentrum München)

When it comes to recovering from insult, the adult human brain has very little ability to compensate for nerve-cell loss. Biomedical researchers and clinicians are therefore exploring the possibility of using transplanted nerve cells to replace neurons that have been irreparably damaged as a result of trauma or disease. Previous studies have suggested there is potential to remedy at least some of the clinical symptoms resulting from acquired brain disease through the transplantation of fetal nerve cells into damaged neuronal networks. However, it is not clear whether transplanted intact neurons can be sufficiently integrated to result in restored function of the lesioned network. Now researchers based at LMU Munich, the Max Planck Institute for Neurobiology in Martinsried and the Helmholtz Zentrum München have demonstrated that, in mice, transplanted embryonic nerve cells can indeed be incorporated into an existing network in such a way that they correctly carry out the tasks performed by the damaged cells originally found in that position. Such work is of importance in the potential treatment of all acquired brain disease including neurodegenerative illnesses such as Alzheimer‘s or Parkinson’s disease, as well as strokes and trauma, given each disease state leads to the large-scale, irreversible loss of nerve cells and the acquisition of a what is usually a lifelong neurological deficit for the affected person.

In the study published in Nature, researchers of the Ludwig Maximilians University Munich, the Max Planck Institute of Neurobiology, and the Helmholtz Zentrum München have specifically asked whether transplanted embryonic nerve cells can functionally integrate into the visual cortex of adult mice. “This region of the brain is ideal for such experiments,” says Magdalena Götz, joint leader of the study together with Mark Hübener. Hübener is a specialist in the structure and function of the mouse visual cortex in Professor Tobias Bonhoeffer’s Department (Synapses – Circuits – Plasticity) at the MPI for Neurobiology. As Hübener explains, “we know so much about the functions of the nerve cells in this region and the connections between them that we can readily assess whether the implanted nerve cells actually perform the tasks normally carried out by the network.” In their experiments, the team transplanted embryonic nerve cells from the cerebral cortex into lesioned areas of the visual cortex of adult mice. Over the course of the following weeks and months, they monitored the behavior of the implanted, immature neurons by means of two-photon microscopy to ascertain whether they differentiated into so-called pyramidal cells, a cell type normally found in the area of interest. “The very fact that the cells survived and continued to develop was very encouraging,” Hübener remarks. “But things got really exciting when we took a closer look at the electrical activity of the transplanted cells.” In their joint study, PhD student Susanne Falkner and Postdoc Sofia Grade were able to show that the new cells formed the synaptic connections that neurons in their position in the network would normally make, and that they responded to visual stimuli.

The team then went on to characterize, for the first time, the broader pattern of connections made by the transplanted neurons. Astonishingly, they found that pyramidal cells derived from the transplanted immature neurons formed functional connections with the appropriate nerve cells all over the brain. In other words, they received precisely the same inputs as their predecessors in the network. In addition, they were able to process that information and pass it on to the downstream neurons which had also differentiated in the correct manner. “These findings demonstrate that the implanted nerve cells have integrated with high precision into a neuronal network into which, under normal conditions, new nerve cells would never have been incorporated,” explains Götz, whose work at the Helmholtz Zentrum and at LMU focuses on finding ways to replace lost neurons in the central nervous system. The new study reveals that immature neurons are capable of correctly responding to differentiation signals in the adult mammalian brain and can close functional gaps in an existing neural network.

Can the brain feel it? The world’s smallest extracellular needle-electrodes

A research team in the Department of Electrical and Electronic Information Engineering and the Electronics-Inspired Interdisciplinary Research Institute (EIIRIS) at Toyohashi University of Technology developed 5-μm-diameter needle-electrodes on 1 mm × 1 mm block modules. This tiny needle may help solve the mysteries of the brain and facilitate the development of a brain-machine interface. The research results were reported in Scientific Reports
on Oct 25, 2016.

(Image caption: Extracellular needle-electrode with a diameter of 5 μm mounted on a connector)

The neuron networks in the human brain are extremely complex. Microfabricated silicon needle-electrode devices were expected to be an innovation that would be able to record and analyze the electrical activities of the microscale neuronal circuits in the brain.

However, smaller needle technologies (e.g., needle diameter < 10 μm) are necessary to reduce damage to brain tissue. In addition to the needle geometry, the device substrate should be minimized not only to reduce the total amount of damage to tissue but also to enhance the accessibility of the
electrode in the brain. Thus, these electrode technologies will realize new experimental neurophysiological concepts.

A research team in the Department of Electrical and Electronic Information Engineering and the EIIRIS at Toyohashi University of Technology developed 5-
μm-diameter needle-electrodes on 1 mm × 1 mm block modules.

The individual microneedles are fabricated on the block modules, which are small enough to use in the narrow spaces present in brain tissue; as demonstrated in the recording using mouse cerebrum cortices. In addition, the block module remarkably improves the design variability in the packaging, offering numerous in vivo recording applications.

“We demonstrated the high design variability in the packaging of our electrode device, and in vivo neuronal recordings were performed by simply placing the device on a mouse’s brain. We were very surprised that high quality signals of a single unit were stably recorded over a long period using the 5-μm-diameter needle,” explained the first author, Assistant Professor Hirohito Sawahata, and co-author, researcher Shota Yamagiwa.

The leader of the research team, Associate Professor Takeshi Kawano said: “Our silicon needle technology offers low invasive neuronal recordings and provides novel methodologies for electrophysiology; therefore, it has the potential to enhance experimental neuroscience.” He added, “We expect the development of applications to solve the mysteries of the brain and the development of brain–machine interfaces.”

Scientists Outline How Brain Separates Relevant & Irrelevant Information

Imagine yourself sitting in a noisy café trying to read. To focus on the book at hand, you need to ignore the surrounding chatter and clattering of cups, with your brain filtering out the irrelevant stimuli coming through your ears and “gating” in the relevant ones in your vision—words on a page.

In a new paper in the journal Nature Communications, New York University researchers offer a new theory, based on a computational model, on how the brain separates relevant from irrelevant information in these and other circumstances.

“It is critical to our everyday life that our brain processes the most important information out of everything presented to us,” explains Xiao-Jing Wang, Global Professor of Neural Science at NYU and NYU Shanghai and the paper’s senior author. “Within an extremely complicated neural circuit in the brain, there must be a gating mechanism to route relevant information to the right place at the right time.”

The analysis focuses on inhibitory neurons—the brain’s traffic cops that help ensure proper neurological responses to incoming stimuli by suppressing other neurons and working to balance excitatory neurons, which aim to stimulate neuronal activity.

“Our model uses a fundamental element of the brain circuit, involving multiple types of inhibitory neurons, to achieve this goal,” Wang adds. “Our computational model shows that inhibitory neurons can enable a neural circuit to gate in specific pathways of information while filtering out the rest.”

In their analysis, led by Guangyu Robert Yang, a doctoral candidate in Wang’s lab, the researchers devised a model that maps out a more complicated role for inhibitory neurons than had previously been suggested.

Of particular interest to the team was a specific subtype of inhibitory neurons that targets the excitatory neurons’ dendrites—components of a neuron where inputs from other neurons are located. These dendrite-targeting inhibitory neurons are labeled by a biological marker called somatostatin and can be studied selectively by experimentalists. The researchers proposed that they not only control the overall inputs to a neuron, but also the inputs from individual pathways—for example, the visual or auditory pathways converging onto a neuron.

“This was thought to be difficult because the connections from inhibitory neurons to excitatory neurons appeared dense and unstructured,” observes Yang. “Thus a surprising finding from our study is that the precision required for pathway-specific gating can be realized by inhibitory neurons.”

The study’s authors used computational models to show that even with the seemingly random connections, these dendrite-targeting neurons can gate individual pathways by aligning with excitatory inputs through different pathways. They showed that this alignment can be realized through synaptic plasticity—a brain mechanism for learning through experience.

Scientists Develop Computer Model Explaining How Brain Learns to Categorize

New York University researchers have devised a computer model to explain how a neural circuit learns to classify sensory stimuli into discrete categories, such as “car vs. motorcycle.” Their findings, which appear in the journal Nature Communication, shed new light on the brain processes underpinning judgments we make on a daily basis.

“Categorization is vital for survival, such as distinguishing food from inedible things, as well as for formation of concepts, for instance ‘dog vs. cat,’ and relationship between concepts, such as hierarchical classification of animals,” says author Xiao-Jing Wang, Global Professor of Neural Science, Physics, and Mathematics at NYU and NYU Shanghai. “Our proposed model can only explain category learning of simple visual stimuli. Future research is needed to explore if the general principles extracted from this model are applicable to more complex categorizations.”

Wang conducted the study with Tatiana Engel, a postdoctoral associate at the time of the study, and Jah Chaisangmongkon, a doctoral candidate in his group, in collaboration with experimentalist David Freedman, a neurobiologist at the University of Chicago. Freedman had previously developed a behavioral paradigm for investigating electrical activity of single-neurons that are correlated with category memberships of visual stimuli.

In this neural-circuit model, which incorporates what we know about the organization and neurophysiology of the cortex, lower-level neural circuits send information about visual stimuli to a higher-level neural circuit where an analog stimulus feature (like the direction of a random pattern of moving dots) is classified into binary categories (A or B). The researchers’ results showed that the model captured a wide range of experimental observations and yielded specific predictions that were confirmed by an analysis of single-neuron electrical activity recorded in a category-learning experiment.

Interestingly, the researchers found that learning a correct category boundary (dividing the continuous feature into A and B) requires top-down feedback projection from category-selective neurons to feature-coding neurons.

Since the pioneering work by NYU’s J. Anthony Movshon, Stanford’s William Newsome, and others, it has been well known that feature-coding sensory neurons reflect an animal’s choice about categorical membership (A or B) of a stimulus in a probabilistic way (quantified as “choice probability”). The common belief was that this is because a category choice is influenced by stochastic, or random, activity of sensory neurons through bottom-up, sensory-to-category pathways.

The new model, reported in the Nature Communications article, suggests a novel interpretation, namely that such “choice probability” results from category-to-sensory, top-down signaling.

This finding offers new insights into feedback projections in the brain whose functional significance had previously been a long-standing puzzle, the researchers note.

Leptin also influences brain cells that control appetite

Twenty years after the hormone leptin was found to regulate metabolism, appetite, and weight through brain cells called neurons, Yale School of Medicine researchers have found that the hormone also acts on other types of cells to control appetite.

Published in the June 1 issue of Nature Neuroscience, the findings could lead to development of treatments for metabolic disorders such as obesity and diabetes.

“Up until now, the scientific community thought that leptin acts exclusively in neurons to modulate behavior and body weight,” said senior author Tamas Horvath, the Jean and David W. Wallace Professor of Biomedical Research and chair of comparative medicine at Yale School of Medicine. “This work is now changing that paradigm.”

Leptin, a naturally occurring hormone, is known for its hunger-blocking effect on the hypothalamus, a region in the brain. Food intake is influenced by signals that travel from the body to the brain. Leptin is one of the molecules that signal the brain to modulate food intake. It is produced in fat cells and informs the brain of the metabolic state. If animals are missing leptin, or the leptin receptor, they eat too much and become severely obese.

Leptin’s effect on metabolism has been found to control the brain’s neuronal circuits, but no previous studies have definitively found that leptin could control the behavior of cells other than neurons.

To test the theory, Horvath and his team selectively knocked out leptin receptors in the adult non-neuronal glial cells of mice. The team then recorded the water and food intake, as well as physical activity every five days. They found that animals responded less to feeding reducing effects of leptin but had heightened feeding responses to the hunger hormone ghrelin.

“Glial cells provide the main barrier between the periphery and the brain,” said Horvath. “Thus glial cells could be targeted for drugs that treat metabolic disorders, including obesity and diabetes.”

How Traumatic Memories Hide In The Brain, And How To Retrieve Them

Some stressful experiences – such as chronic childhood abuse – are so overwhelming and traumatic, the memories hide like a shadow in the brain.

At first, hidden memories that can’t be consciously accessed may protect the individual from the emotional pain of recalling the event. But eventually those suppressed memories can cause debilitating psychological problems, such as anxiety, depression, post-traumatic stress disorder or dissociative disorders.

A process known as state-dependent learning is believed to contribute to the formation of memories that are inaccessible to normal consciousness. Thus, memories formed in a particular mood, arousal or drug-induced state can best be retrieved when the brain is back in that state.

In a new study with mice, Northwestern Medicine scientists have discovered for the first time the mechanism by which state-dependent learning renders stressful fear-related memories consciously inaccessible.

“The findings show there are multiple pathways to storage of fear-inducing memories, and we identified an important one for fear-related memories,” said principal investigator Dr. Jelena Radulovic, the Dunbar Professor in Bipolar Disease at Northwestern University Feinberg School of Medicine. “This could eventually lead to new treatments for patients with psychiatric disorders for whom conscious access to their traumatic memories is needed if they are to recover.”

It’s difficult for therapists to help these patients, Radulovic said, because the patients themselves can’t remember their traumatic experiences that are the root cause of their symptoms.

The best way to access the memories in this system is to return the brain to the same state of consciousness as when the memory was encoded, the study showed.

The study was published August 17 in Nature Neuroscience.

Changing the Brain’s Radio Frequencies

Two amino acids, glutamate and GABA, are the yin and yang of the brain, directing its emotional tides and controlling whether nerve cells are excited or inhibited (calm). Under normal conditions the system is balanced. But when we are hyper-aroused and vigilant, glutamate surges. Glutamate is also the primary chemical that helps store memories in our neuronal networks in a way that they are easy to remember.

GABA, on the other hand, calms us and helps us sleep, blocking the action of the excitable glutamate. The most commonly used tranquilizing drug, benzodiazepine, activates GABA receptors in our brains.

There are two kinds of GABA receptors. One kind, synaptic GABA receptors, works in tandem with glutamate receptors to balance the excitation of the brain in response to external events such as stress.

The other population, extra-synaptic GABA receptors, are independent agents. They ignore the peppy glutamate. Instead, their job is internally focused, adjusting brain waves and mental states according to the levels of internal chemicals, such as GABA, sex hormones and micro RNAs. Extra-synaptic GABA receptors change the brain’s state to make us aroused, sleepy, alert, sedated, inebriated or even psychotic. However, Northwestern scientists discovered another critical role; these receptors also help encode memories of a fear-inducing event and then store them away, hidden from consciousness.

“The brain functions in different states, much like a radio operates at AM and FM frequency bands,” Radulovic said. “It’s as if the brain is normally tuned to FM stations to access memories, but needs to be tuned to AM stations to access subconscious memories. If a traumatic event occurs when these extra-synaptic GABA receptors are activated, the memory of this event cannot be accessed unless these receptors are activated once again, essentially tuning the brain into the AM stations.”

Retrieving Stressful Memories in Mice

In the experiment, scientists infused the hippocampus of mice with gaboxadol, a drug that stimulates extra-synaptic GABA receptors. “It’s like we got them a little inebriated, just enough to change their brain state,” Radulovic said.

Then the mice were put in a box and given a brief, mild electric shock. When the mice were returned to the same box the next day, they moved about freely and weren’t afraid, indicating they didn’t recall the earlier shock in the space. However, when scientists put the mice back on the drug and returned them to the box, they froze, fearfully anticipating another shock.

“This establishes when the mice were returned to the same brain state created by the drug, they remembered the stressful experience of the shock,” Radulovic said.

The experiment showed when the extra-synaptic GABA receptors were activated with the drug, they changed the way the stressful event was encoded. In the drug-induced state, the brain used completely different molecular pathways and neuronal circuits to store the memory.

“It’s an entirely different system even at the genetic and molecular level than the one that encodes normal memories,” said lead study author Vladimir Jovasevic, who worked on the study when he was a postdoctoral fellow in Radulovic’s lab.

This different system is regulated by a small microRNA, miR-33, and may be the brain’s protective mechanism when an experience is overwhelmingly stressful.

The findings imply that in response to traumatic stress, some individuals, instead of activating the glutamate system to store memories, activate the extra-synaptic GABA system and form inaccessible traumatic memories.

Traumatic Memories Rerouted and Hidden Away

Memories are usually stored in distributed brain networks including the cortex, and can thus be readily accessed to consciously remember an event. But when the mice were in a different brain state induced by gaboxadol, the stressful event primarily activated subcortical memory regions of the brain. The drug rerouted the processing of stress-related memories within the brain circuits so that they couldn’t be consciously accessed.