MacBooks in Neuroscience

MacBooks provided by Apple have become an integral part of the daily work of many neuroscience researchers. These powerful computers provide high performance and can efficiently process and analyze huge volumes of data obtained from neuroscience experiments.

Neuroscience Software

One of the key components of successful research in neuroscience is specialized software. MacBooks support a variety of tools for neural network modeling, image processing and time series analysis, which greatly simplifies the research process.

Neural Network Modeling

Using programming languages such as Python and deep learning libraries, researchers can create and analyze complex neural network models. MacBooks provide a convenient development environment and high performance, allowing you to quickly experiment and respond to changes in data.

Data Analysis and Visualization

MacBooks also stand out for their data analytics and visualization capabilities. Modern tools such as Jupyter Notebooks allow researchers to conduct analyzes interactively, making the process more dynamic and efficient. Visualizing data on a MacBook becomes a creative process, creating a mosaic view of the structure and function of the brain.

Teamwork and Collaboration

Modern scientific research often requires collaboration and sharing of data between researchers. MacBooks provide convenient collaboration tools, allowing teams of scientists to effectively collaborate on projects and share results.

MacBooks in Neuroscience: Modern Tools

MacBooks provide neuroscience researchers not only with high performance, but also with advanced tools for data analysis. Standard software platforms such as Python, R, and others are finding their way into scientists’ toolboxes, opening the door to creating and optimizing analysis algorithms.

Modeling Neural Networks: Virtual Experiments

Using MacBooks, researchers can conduct virtual experiments, simulating neural networks and analyzing their behavior. Integration with popular deep learning libraries such as TensorFlow and PyTorch makes building and testing complex models accessible and efficient.

Analysis and Visualization: Extracting Meaning from Data

MacBooks provide a wide range of data analysis and visualization tools, allowing researchers to extract meaning from complex sets of information. Interactive tools and development environments such as Jupyter Notebooks are becoming powerful tools for visualizing and communicating results.

Working Together: Developing Collective Knowledge

MacBooks are actively introduced into the process of collective work on scientific projects. Efficient data sharing and exchange of ideas are made possible by the devices’ high compatibility and ease of use.

To perform important tasks in the field of neuroscience, where high-tech data analysis methods are widely used, the health of the MacBook can be critical for several reasons:

• Performance: MacBooks offer powerful performance, which is key when processing and analyzing the massive volumes of data found in neuroscience research. Computer malfunctions or malfunctions can significantly slow down the data analysis process and even lead to loss of results.

• Compatibility: It is important that the software and hardware used are compatible with the tools and platforms used in neuroscience. Program development and neural network modeling often require a specific environment, and working on a working MacBook provides the necessary technological capabilities.

• Data Security: Neuroscience research processes sensitive data and its security is of utmost importance. A healthy computer with updated security and software helps protect valuable scientific results from threats such as viruses or unauthorized access.

• Comfort and Efficiency: Using a working MacBook provides a comfortable and efficient work environment for the researcher. Regular crashes, program crashes or other problems can create inconvenience and distract from the main task – conducting and analyzing neurobiological experiments.

• Visualization and Communication: Well-functioning computers provide high-quality data visualization, which is especially important when presenting results and communicating with colleagues. The lack of equipment hassles also makes it easier to create presentations and publish research results.

Overall, a well-maintained MacBook is a reliable tool for successfully completing mission-critical tasks in neuroscience, where accuracy, performance, and data security are critical.

Conclusion

In the use of MacBooks in neuroscience research at Good Zone in Brooklyn, we see not just a technological tool, but rather a key to unraveling the mosaic structure of the brain. This integrated approach not only advances the understanding of brain science, but also creates innovative methods for analyzing data in this exciting area of scientific research. That is why, with such a reliable repair company, you don’t have to worry about your discoveries.

The post Brain Mosaic: Using MacBooks to Analyze Neurobiological Data appeared first on Dam-Siantonio.

How are beliefs formed?

According to The New York Times, citing the study, some students, while studying suicide notes, discovered that they had a talent for determining who really committed suicide. Out of twenty-five pairs of notes, this group of students correctly identified the real one twenty-four times. The others realized they were hopeless – they identified the real note only ten times.

As is often the case with psychological research, the whole set-up was a sham. While half of the notes were indeed genuine-they came from the Los Angeles County coroner’s office-the assessments were fictitious. Students who were told they were almost always right were, on average, no more astute than those who were told they were mostly wrong.

In the second phase of the study, the deception was exposed. The students were told that the real purpose of the experiment was to gauge their reactions to being told they thought they were right or wrong. (This, as it turned out, was also a hoax.) Finally, the students were asked to estimate how many suicide notes they actually categorized correctly and how many they thought the average student had identified.

At this point, something curious happened: the students in the high-scoring group said that they thought they had actually done quite well – significantly better than the average student – even though, as they had just been told, they had no reason to think so. Conversely – those students in the low-scoring group reported that they had, in their own opinion, done significantly worse than the average student – a conclusion that was equally unwarranted. So what’s the point?

A few years later, a new set of Stanford students were recruited for a similar study. This time they were handed packets of information about a pair of firefighters, Frank K. and George H. Frank had a young daughter and loved to scuba dive. George had a young son and played golf. The packets also included the men’s responses to what the researchers called a “risky-conservative choice test.” In one packet of information, Frank was a successful firefighter who almost always chose the safest option. In another version, Frank also chose the safest option, but was a lousy firefighter who received several warnings from his superiors.

In the middle of the study, the students were told that they had been deliberately misled and that the information they had received was completely fictitious. They were then asked to describe their own beliefs: how do they think a firefighter should view risk? Students who received the first packet thought that a firefighter would try to avoid risk, while students in the second group believed that a firefighter would take risks.

It turns out that even after “the evidence for their beliefs has been completely refuted, people are unable to make appropriate changes to those beliefs,” the researchers wrote. In this case, the failure was “particularly impressive” because two data points would never be enough to generalize the information.

Ultimately, the Stanford study became famous. The claim made by a group of scientists in the seventies that humans could not think straight sounded shocking. Today it is not – thousands of subsequent experiments have confirmed the discovery of American scientists. Today, any graduate student with a tablet can demonstrate that seemingly reasonable people are often completely irrational. Rarely has this insight seemed more relevant than it does today, has it not?

The post How are our beliefs formed and why does our brain deceive us? appeared first on Dam-Siantonio.

When asked to recommend a travel itinerary for a city you’ve been to often, selective snippets of memories of places from different trips may come to mind. Researchers from several universities, including Columbia University in New York and Emory University in Atlanta, USA, studied individual neurons – called “memory cells” – of 19 patients who had undergone brain surgery for epilepsy treatment.

The patients performed a task designed to assess spatial memory performance. During the task, the subjects were placed on a road using virtual reality (VR) goggles and asked to press a button when they encountered specific objects. In part of the task, the researchers asked participants to walk along a path and mark the location of a distant object. By examining the medial temporal lobe (MTL) and, in particular, the entorhinal cortex, the scientists found that memory-tracking cells were “spatially tuned” to the location and could then retrieve the location information that the person had to recall.

The scientists’ work shows that neurons in the human brain keep track of events we intentionally recall and can change their activity patterns to distinguish between memories. They’re like dots on a Google map that mark where important events in your life happened. This discovery could serve as a potential mechanism for our ability to selectively utilize different experiences from the past and help experts understand how these memories affect the spatial map of the human brain.

In the past, scientists have already tried to understand how this can be done. They found that the cells of the neural network are very important for the operation of spatial memory, as it works similarly to a GPS system. Spatial tuning of neurons is the idea that individual neurons are “activated to represent a location in the environment during navigation.” Previous work has suggested that spatially tuned cells reassign their triggering circuits in different environments, so events that occur in different locations are associated with different spatial maps,” the researchers explain.

Building on this work, the scientists suggested that individual neurons in the MTL, and especially in the entorhinal cortex, will demonstrate a kind of “spatial tuning of neurons” modeled by past experience. The implication is that the human brain creates the truest map of memories.

The post How do brain cells make a map of memories? appeared first on Dam-Siantonio.

Why brain-computer interfaces are flawed

A year ago, Dutch scientists developed a brain-computer interface (BCI) that can translate a patient’s brain activity into words. The researchers detailed it in the journal Nature Communications. Even earlier, a similar brain-computer interface was created by Chinese developers.

These devices effectively recognize words, but work on the basis of electrodes that are implanted in the human brain. In addition, the interfaces focus on the part of the brain that is responsible for the mouth movements made by a person to pronounce a word. That is, in order for the device to recognize a thought, the person has to try to pronounce it with their mouth.

Why brain-computer interfaces are flawed. A brain-computer interface designed to voice thoughts has a number of flaws that make it difficult to use. Photo.
Brain-computer interface, designed to voice thoughts, has a number of shortcomings, which complicates its use

Other ways of reading thoughts, for example, based on electroencephalogram (measuring brain activity with the help of electrodes fixed on the head), were less effective. They only allowed to decipher individual words, but are not able to construct a coherent text.

How the mind-reading device works

Employees of the University of Texas at Austin have developed an interface based on functional magnetic resonance imaging (fMRI). It allows you to monitor the activity of certain areas of the brain during its functioning, and has a high resolution. Sometimes this technology is used to monitor blood flow in the brain. Earlier we told you that with the help of fMRI, during one of the studies scientists tracked the level of stress in people.

In this case, fMRI monitors the part of the brain that is responsible for a person’s imagined speech. The human brain responds to each word in a specific way. So the scientists’ task was to link each word to a specific pattern of brain activity. To do this, the team scanned the brains of three people for 16 hours while they listened to podcasts.

In this way, the team was able to create a specific set of maps of brain activity triggered by different words, phrases or phrase meanings. The authors then trained the artificial intelligence to determine what a person was thinking based on fMRI data. Unlike developments that existed before, this technology does not recognize individual words, but determines the overall meaning of each phrase or sentence.

To construct text based on the fMRI data and processed by the AI, the researchers connected the language model of the GPT-1 neural network, which was the predecessor of ChatGPT. Gradually, they were able to train the AI to recognize words, phrases and sentences. As reported by the authors of the work in the journal Nature Neuroscience, even if the AI developed by them is wrong in some words, it well conveys the essence of the speech that a person hears or mentally pronounce.

In addition, the system has even learned to voice what a person sees in front of him. For example, in one of the experiments, participants watched a video without sound, in which a dragon knocks a man down. In doing so, the system, based on brain activity, voiced the scene as: “He’s knocking me down.” However, participants were not asked to mentally voice what they saw. The short video below shows how accurately the system recognizes thoughts.

The post Artificial intelligence has been taught to read human thoughts appeared first on Dam-Siantonio.

It is widely known that the speed of cognitive processes in our brain begins to slow down in our youth, passing its peak around the age of 20. Indeed, many people can recall that in their 20s, various tasks seemed to be thought about faster than in more mature years. Various scientific studies also attest to this.

Scientists from the University of Heidelberg (Germany) do not deny this, but after conducting their own study, they found a new cause of cognitive slowing, not related to the speed of the brain. They presented their findings in the journal Nature Human Behavior. The study involved more than 1.2 million people.

The experiment was conducted online. In the task of volunteers was to classify certain words and images that appeared on the screen, by pressing the “right” keys on the keyboard. And what did the researchers see? That this response time was indeed slower in subjects over the age of 20 compared to younger participants.

However, according to experts, this was hardly due to the slowing down of their cognitive processes. By using mathematical models (Bayesian hierarchical modeling), the scientists concluded that such a phenomenon lies beyond the speed of the brain and can be associated with other factors. First of all, with the fact that with age people who have gained certain life experience become more cautious and in principle slower to make certain decisions.

And also with age a person’s physical reflexes slow down, so he or she may react slower to something (for example, pressing buttons on a keyboard) for purely physiological reasons. But after the age of 60, cognitive processes in the brain really start to slow down. Although in different people it happens in different ways: someone has a degree of “mental” speed is still very high, someone – quite low.

The reasons for this are still unknown to scientists – they hope to get an answer to this question in future studies. In addition, the work of experts has shown that the speed of the brain is at about the same level in very different groups of people. It depends little on gender, race or level of education. Scientists, however, emphasize that for the final conclusions need, as usual, additional research and study of the speed of response during the execution of other tasks by the subjects

The post The speed of cognitive processes appeared to be unchanged up to the age of 60 years appeared first on Dam-Siantonio.

Earlier studies have shown that classical music can have a positive effect on people’s ability to process conflicting information. Neuroscientists believe that listening to it is useful in alleviating various neuropsychological disorders. Despite this, the issue remains poorly understood. Scientists from Monash University (Australia) conducted their own study on this issue, the results of which were published in the journal Frontiers in Neuroscience.

The work involved 67 students who were asked to take two different tests – color and verbal. This was done under different conditions – with classical music playing in the background, with white noise and in silence. The interval between the tests was several days.

Live music helped analgesics in spinal rehabilitation
Researchers from Israel and the USA have shown that music therapy can eliminate postoperative pain in patients with spinal disorders more effectively than medications.

After the tests, scientists analyzed the results of cognitive skills during the tests. It turned out that neither music nor white noise increased the level of cognitive abilities of the participants, and the latter, like other stressors, even worsened it. At least, this applied to the processing of contradictory stimuli. The analysis of the literature showed that classical music can influence the improvement of some cognitive abilities not related to the processing of contradictory information, but definitely not white noise

The post Classical music and white noise did not improve cognitive performance appeared first on Dam-Siantonio.

Sleep is an integral part of every person’s life and essential to their health. Doctors believe that it is optimal for an adult to sleep seven hours a day, and that lack and excess of sleep are harmful.

However, many people (10-30% of the population) regularly face sleep disorders, including insomnia. Of equal importance is the quality of sleep, including the duration of its individual phases (slow sleep, REM sleep and so on) and their ratio.

Interestingly, the average duration of spontaneous daily sleep in mammals varies widely, from two to 20 hours per night. Assessing this parameter helps to better understand the physiological and other functions of sleep. It turns out that sleep duration does not correlate with brain size and cognitive abilities. Rather, it is determined by their ecology, lifestyle and how a particular animal eats.

Thus, the cyclical alternation of sleep and wakefulness was found to be important precisely in the context of nutrition and optimal energy utilization.

It is noteworthy that the temperature of the animal’s brain drops sharply during the transition from wakefulness to the phase of slow-wave sleep – that is, as the animal falls asleep. At the same time, the brain warms up again with the onset of the rapid eye movement phase of sleep (REM phase).

The REM (rapid eye movement) phase of sleep is indeed accompanied by rapid eyeball movements. In this phase, the brain is most active – and it is in this phase that humans dream.

As a result, it was concluded that the duration of the rapid sleep phase in warm-blooded animals is inversely proportional to the temperature of their body and brain. Thus, having a body temperature of only 31 degrees, monotremes (like platypus and echidna) spend about 7.5 hours in the REM-phase every day. They are followed at intermediate rates by marsupials and placental mammals (most members of this class, including humans). Finally, the shortest phase of REM sleep (only 0.7 hours per day) is noted in birds, which have the most “hot” blood – about 41 degrees.

It turns out that the phase of REM sleep plays a key role in the regulation of brain temperature and the rate of metabolism in this organ. It also helps the brain to move into a state of wakefulness.

The author of the study believes that during the slow phase of sleep brain temperature of animals should not fall below a certain critical value. Otherwise, they simply will not be able to wake up quickly in case of danger.

According to the scientist, his conclusions are quite applicable to human sleep. He emphasizes that the duration of the phase of REM sleep in Homo sapiens has average values, the same can be said about the temperature of his brain. All this rather denies the role of the REM phase of sleep in the outstanding cognitive and emotional capabilities of our species.

The post REM sleep phase is needed to regulate brain temperature appeared first on Dam-Siantonio.

That cognitive functioning – the ability to perform various mental activities closely related to learning and problem solving – changes with age is no secret. Neuroscientists also know that these differences between young and old people are due to changes in the brain’s connectivity – in how its regions interact with each other.

While the authors of previous studies mainly focused on how differences in neural networks affect how well people of different ages cope with cognitive tasks, the new work puts the problem, in the words of the scientists themselves, “upside down”. “We wondered: can we engage patterns of brain connectivity when people are not performing any tasks to predict their age?” – Chrysikou added.

The sample consisted of 547 people 18-88 years old for whom neuroimaging results from the Cambridge Center for Aging and Neuroscience (Cam-CAN) were available. Using their data, the researchers first analyzed whether connectivity between the central executive network and the brain’s passive mode network (unlike the former, it is active when a person is doing nothing, inactive, resting and immersed) could be a marker of age. To this end, they used multiple regression analysis (allows you to establish the dependence of one variable on two or more independent variables)

In addition, the scientists tested how the strength of the connection between the two networks is affected by the relevance detection network: it is activated when there is a discrepancy between what a person knows and can predict, and what he sees, hears or feels. It is this network that is responsible for conscious attention – when it is activated, the brain comes out of passive mode (and vice versa).

As the results have shown, by the functional connection between large-scale brain networks, which changes in the process of growing up and aging, it is indeed possible to guess the age of a person with high accuracy. And when the significance detection network was taken into account, it was even better to determine the number of years from birth.

The post A person’s age has been predicted by the connections between certain brain networks appeared first on Dam-Siantonio.

Sometimes it can be difficult to know if we can control a particular situation. Many factors affect this ability, including our mental state. Often high levels of stress, anxiety, and depression weaken our sense of control and lead people to believe that none of their actions matter, even if they really don’t.

For several decades, scientists have been trying to uncover the cognitive mechanisms that provide a sense of control. Now researchers from Portugal and the Netherlands have come closer to unraveling it. The cognitive mechanism they described in a paper published in the journal Nature Human Behavior has not previously been considered by scientists.

The authors designed a special experiment in a virtual reality space. It consisted of a house with several doors in each room. It was possible to move around the house in one of two scenarios. In the first, “controlled” scenario, the color of the door depended on which room it led to (for example, a yellow door always led only to the bathroom and a pink door led only to the living room). In the “unmanaged” house, the order of the rooms was fixed (any door in the kitchen could only lead to the bathroom, from the bathroom only to the living room).

As the participants moved around the virtual house, the scientists switched between the “controlled” and “uncontrolled” house scenarios without their knowledge. The test subjects walked around the house for a while, after which they had to answer a question asking which rooms were behind the two doors in front of them. If the volunteers did not feel in control, they answered that both doors led to the room they were supposed to get to according to a fixed scenario. Otherwise, they pointed to different rooms behind the doors that matched their color.

The scientists attributed the results to the fact that the participants’ brains were undergoing two parallel processes of learning the roles of “actor,” who could choose which room he or she wanted to go to based on the color of the door, and “spectator,” who got to the room determined by the sequence. As the scenarios changed over the course of the experiment, the brain constantly compared the results of the “actor” and “spectator” models to determine which one gave a more accurate prediction.

A stressor – weak electric shocks – was then added to the experiment to test its effect on the sense of control. Participants who received more shocks tended to take a “spectator” position. The higher their initial stress level was, the more they were affected by the stimulus. At the same time, people who could take action to avoid being hit were better at implementing the “actor” model of behavior.

The researchers proposed two hypotheses to explain this result. First, high levels of stress may trigger emotional processes that impair cognitive task performance. Second, under conditions of stress, the role of “spectator” seems more rational, since experience shows that the situation is uncontrollable

The post Scientists have figured out how the brain decides it can control a situation appeared first on Dam-Siantonio.

Developmental neurobiology: a specialized section of neurobiology. Scientists in this field study the processes that contribute to brain development and function. It all starts with a few specialized embryonic cells that transform into the nervous system. Specialists study how these cells change before forming a functional nervous system. Such research may improve understanding of the mechanisms of development of some neurological disorders.

Molecular biology. Researchers in this field study neuroanatomy at the molecular level and how molecular interactions contribute to the nervous system. They also study the influence of genetics on neuronal development and the molecular basis of neuroplasticity and neurodegenerative diseases. This is a relatively new and dynamic field.

Neurobiology of addiction: a specialized branch of neurobiology that studies the mechanisms of neuronal connectivity disorders in the development of addiction. Studies using neuroimaging are important in understanding the key changes in the human brain that contribute to the development of addiction.

Neurobiology of aging: a specialized section of neurobiology. Researchers in this field study the physiological aging processes of the brain, focusing on the mechanisms of nervous system changes with age or age-related diseases such as epilepsy, Alzheimer’s disease, and age-related memory impairment. These studies pay attention to changes in synaptic connections as neurons die off, the influence of the endocrine system and electrolytes, particularly calcium, both in normal brain aging and in neurodegenerative diseases.

Neurobiology of anxiety disorders. Researchers in this field study changes in neuronal, neuroendocrine, and neurotransmitter activity in anxiety and depression. Scientists are primarily interested in those areas of the brain in which the greatest changes occur. This research contributes to the development of effective treatments and prevention of anxiety disorders.

Autism neurobiology: a specialized branch of neurobiology that studies the developmental factors in the nervous system that contribute to autism spectrum disorders (ASD). Research focuses on genetic mechanisms, brain developmental processes, and the relationship between neuroanatomical brain regions associated with the onset of autism.

Neurobiology of Mental Disorders: Researchers in this field seek to understand the role of neurotransmitters in the formation of mental disorders using molecular genetics techniques. One of the challenges that researchers face in studying this field is the lack of patients for the control group who are not currently taking psychotropic medications when the disorder relapses.

Neurobiology of bipolar disorder: a specialized section of neurobiology and psychiatric disorder research. The complex nature of bipolar disorder’s oscillation between mania and depression makes it difficult for researchers to identify its causes. Researchers study the brain connections and neurotransmitter systems that contribute to the development of this disorder.

Neurobiology of mental trauma. Researchers in this field study the interrelationships of different brain regions with endocrine and neurotransmitter responses to traumatic exposure. These effects are studied in the short and long term at the molecular, cellular, and behavioral levels. Research in this area overlaps with the developmental neurobiology of childhood trauma or negative childhood experiences (NDEs).

Sleep neurobiology. Researchers in this field study the nervous system functions involved in the processes of sleep and wakefulness and the genetic and behavioral factors that regulate sleep. Discoveries in this field contribute to the understanding and treatment of circadian rhythm and sleep disorders.

The post What types of neurobiologic research are there? appeared first on Dam-Siantonio.