Information

What is the relationship between topographic maps and sensory memory?

What is the relationship between topographic maps and sensory memory?

Sensory maps are defined functionally: they exist for a certain time window, are overwritten quickly, are generally inaccessible to introspective control.

Topographic maps are defined biologically: neurons within the map are organized spatially, so that nearby neurons store subtly different information.

Has the relationship between the two been studied in much detail? Wikipedia tells me that the basis of, say, visual persistence iconic memory is neural persistence. The kind of question I want an answer to: how adequately can the properties of various retinotopic maps explain known features of visual persistence iconic memory?


Results

Individual’s Mood Manipulation Check

The PANAS was used to control for individual’s mood changes. Following the procedure used by Phillips et al. (2002), mood scores at both the first and the second administration of the PANAS were obtained by subtracting the total negative affect score (computed summing the scores for each of the 10 negative adjectives) from the positive affect score (computed summing the scores for each of the 10 positive adjectives). Then, comparing mood scores at the first administration (baseline) to mood scores at the second administration (after the completion of the WalCT) in terms of group conditions (positive, negative and neutral landmarks) no significant results were found: no main effects of ‘group’ [F(2,72) = 1.5743, p = 0.21, partial η 2 = 0.042] and ‘time’ [F(1,72) = 1.4298, p = 0.24, partial η 2 = 0.019] no interaction effect of ‘group and time’ [F(2,72) = 0.44, p = 0.65, partial η 2 = 0.012]. No significant results were obtained even considering separately positive (no main effects of ‘group’ [F(2,72) = 2.513, p = 0.09, partial η 2 = 0.065], ‘time’ [F(1,72) = 0.029, p = 0.87, partial η 2 = 0.00] and interaction effect of ‘group and time’ [F(2,72) = 0.812, p = 0.45, partial η 2 = 0.022]) and negative affect (no main effects of ‘group’ [F(2,72) = 0.0928, p = 0.91, partial η 2 = 0.003], ‘time’ [F(1,72) = 3.0565, p = 0.08, partial η 2 = 0.041] and interaction effect of ‘group and time’ [F(2,72) = 0.038, p = 0.96, partial η 2 = 0.001]). These results indicated that any effect on topographical memory performance would be due to the emotional landmarks rather than to the participants’ mood changes.

Learning of the Eight-Square Sequence

Descriptive statistics for this measure follows: Mean = 130.8 SD = 9.11 SE = 1.05 Min = 103–Max = 144.

The Univariate ANOVA carried out on the learning score revealed an effect of group [F(2,72) = 5.17, p = 0.008, partial η 2 = 0.126]: Post hoc analysis (LSD: p < 0.05) showed that both the PLG (mean = 134.08 SE = 1.73) and the NLG (mean = 131.84 SE = 1.73) scored higher than the NeuLG (mean = 122.44 SE = 1.73) no difference was found between the PLG and the NLG (See Figure 3).

FIGURE 3. Landmark group differences in the learning score of the Walking Corsi Test. The error bars represent the standard errors of the means (confidence interval = 0.95).

Delayed Recall

Descriptive statistics for this measure follows: Mean = 7.76 SD = 1.11 SE = 0.13 Min = 0–Max = 8.

The Univariate ANOVA carried out on the delayed recall score showed no difference [F(2,72) = 0.126, p = 0.88, partial η 2 = 0.003] among the PLG (mean = 7.84 SE = 0.23), the NLG (mean = 7.68 SE = 0.23) and the NeuLG (mean = 7.76 SE = 0.23).

Reproduction of the Eight-Square Sequence on the Outline of the WalCT

Descriptive statistics for this measure follows: Mean = 6.32 SD = 2.4 SE = 0.28 Min = 0–Max = 8.

The Univariate ANOVA carried out on the reproduction score revealed an effect of group [F(2,72) = 3.6372, p = 0.03, partial η 2 = 0.092]: Post hoc analysis (LSD: p < 0.05) showed that the PLG (mean = 7.32 SE = 0.44) scored higher than both the NLG (mean = 6 SE = 0.44) and the NeuLG (mean = 5.64 SE = 0.44) no difference was found between the NLG and the NeuLG (See Figure 4).

FIGURE 4. Landmark group differences in the reproduction of the Walking Corsi Test. The error bars represent the standard errors of the means (confidence interval = 0.95).


Behavioral Neuroscience

The concentration in behavioral neuroscience is designed for students with a focused interest in the biological bases of behavior and thought.

The concentration is well suited for students that are contemplating professional or research careers in medicine, pharmaceuticals, veterinarian medicine, animal science, neurology, and neuroscience. Yet, the courses in the concentration are open to all psychology majors and even to other majors. Because BN concentrators have additional laboratory requirements beyond those needed for the psychology major, at graduation all students who complete the BN track receive a Bachelors of Science (BS) degree and a note on their transcript that they successfully completed the BN concentration.

Neuroscience is an extremely large discipline that encompasses an extremely wide variety of scientists and scientific interests. However, all neuroscientists are interested in the brain and how the brain works. Some neuroscientists are only interested on learning about the basic physiology of the brain and of brain tissue. These neuroscientists may be interested in such questions as: What cellular processes enable neurons to communicate with each other via neurotransmitters? But behavioral neuroscientists are interested in the relationship between the physiological processes that occur in the brain and the behavior of an organism. The Behavioral Neuroscientist is likely to be interested in the answer to the question: What types of altered communication between neurons is responsible for the dramatic changes in behavior and cognition that are observed in people with bipolar depression? This focus on the behavior of the entire organism is what is distinctive about behavioral neuroscience.

The facts are that neuroscience or biopsychology concentrations are among the most demanding undergraduate programs offered. There is a significant degree of overlap between the requirements for the BN concentration and the courses that comprise most pre-med programs. It is very likely that no matter how capable a student you may be you will be challenged at some point in the curricullum. Before you decide to tackle the BN concentration you should discuss your plans with your undergraduate advisor and your BN advisor.

Just as every psychology must do, each student in the BN concentration must complete an empirical research project during their senior year. The topic of this thesis should be related to a topic in behavioral neuroscience and must be approved in advance by the BN advisor. The BN concentration has been designed to equip you with all the skills you should require to complete the thesis. Very often your thesis project may develop from collaborative projects that you began with one of the faculty. Many students presented their thesis at conferences and some have even published their thesis. Here are just a few of the recent BN thesis projects that have been completed by seniors:

  • Children’s Event Related Potentials and their Relationship with Computer Abilities.
  • Acute Aluminum Toxicity in Advanced Aged Mice
  • The Effects of Lead Exposure on Rat performance in an Eight-arm Radial Maze and Possible Implications for ADHD in Humans.
  • Anxiety and EEG: Using a Brain Atlas to Document Anxiety in the Temporal Lobes.
  • The Effects of Sodium Salicylate on Sexual Arousal in Adult Male Mice (Mus domesticus).
  • The Effects of Anandamide on the Ultrasonic Vocalizations of 10 Day-old Infant Mice.
  • The Effects of Medication on ERP Characteristics for Attention Deficit Disorder Individuals.
  • The Effects of Prenatal Cocaine on Physical and Social Development of Infant Mice.
  • The Effects of Antioxidants on Recovery from Excitotoxin Induced Brain Injury: Hippocampal Lesions and Spatial Learning in Mice.

The answer is absolutely yes!

While the concentration requires careful planning and course sequencing, there is ample opportunity to complete all the course requirement for graduation and for the BN concentration in four years. Some students take summer courses to accelerate their progress through the major or allow them greater flexibility in course selection during the regular school year. To insure that you are on track you should consult with your faculty advisor and with the faculty BN advisor sometime prior to the spring semester of your second year.

The BN concentration is offered through the psychology department, so you must major in psychology to obtain the B.S. in BN. However, if you decide to major in another discipline, you may still enroll in BN courses. In fact, just as we encourage students in the BN concentration to sample from a wide selection of course electives, we encourage other majors to sample from the BN courses.

Students in the concentration are encourage to spend at least one summer either in an internship or conducting research. There is a small summer research program at WC and similar opportunities for our students at other institutions are available. These opportunities are usually very competative, but the rewards can be exceptional. In just the last three years, students have obtained summer internship and/or research positions at the National Institutes of Mental Health (NIMH), the National Institute of Drug Abuse (NIDA), the University of Maryland, and the University of South Carolina.

Three faculty in the psychology department teach the majority of required psychology courses in the BN concentration. These faculty are Dr. Gibson (BN Advisor) and Dr. Weil. You can learn more about them and more about their research interests by visiting the faculty page .

Clinical Neuropsychology, Medicine, Pharmacology, Animal Science & Veterinary Science, Neuroscience, and many others.

Careers in most of the fields listed above require a post-graduate (M.S., Ph.D., Psy.D., or M.D.) degree. However, many many entry level positions in medical and or pharmaceutical research are available to individuals with a B.S. degree. With your liberal arts background you may also be interested in a career as a science journalist!

Follow these links to learn more about the field of neuroscience:

Follow this link for more information about WC and the admission process.


Relationship between Cognitive Learning Psychological Classification and Neural Network Design Elements

This article first analyzes the research background of the design elements of cognitive psychology and neural networks at home and abroad, roughly understands the research status and research background of these two courses at home and abroad, and discusses the application of cognitive psychology to neural networks. The design method has not yet formed a systematic theoretical system. Then, a systematic theoretical analysis of the research in this article is carried out to analyze the relationship between the various characteristics of cognitive psychology and the design elements of the neural network, and it uses these relationships to guide the design practice. Second, it analyzes the relationship between the influence and interaction of cognitive psychology on neural network design and connects cognitive psychology with neural network design. Finally, according to the theoretical analysis and research of the system, the application of cognitive psychology in neural network design, design practice, and the relationship between the two are systematically reviewed. Through the exploratory research on cognitive psychology in neural network design, we can see that the combination of neural network design and psychology, art aesthetics, and other cross-disciplinary and multidisciplinary research is necessary, which can promote the scientific and technological progress of neural network design in the context of the information age and the improvement of public mental health. Under the background of the era in which the neural network design becomes the link between people's emotions and culture, we must fully understand the essential role of each element in neural network design and build a design concept based on cognitive psychology and emotional experience. It is hoped that the content of this topic can provide a certain reference value for the future development of neural network design and cognitive psychology and clarify the new development direction.

1. Introduction

Today, in the development of psychology, cognitive psychology is the main direction of research. The difference between cognitive psychology and other disciplines is that cognitive psychology is not to explore people’s various projections by studying the phenomena that people manifest on the surface but to study the process of information transmitted by objective things to people's brains [1]. Among the many branches of psychology, cognitive psychology studies the complex thinking patterns of people and the process of psychological activity that combines the storage and extraction of information in memory with new knowledge information to produce its own empirical knowledge information. All this constitutes the essential difference between humans and animals. The entire cognitive psychological process greatly affects the behaviors people take in the process of obtaining information. However, compared to other “hard” sciences like physics, which has measurable body stimuli and measures responses to stimuli, the research process of cognitive psychology is obviously more unknown and uncertain [2]. However, cognitive psychology is indeed the core area of many scientific studies. In the past research on information acquisition behavior, most of the research results are from the perspective of external environment and scenarios to analyze information acquisition behavior. In the field of scientific research, nowadays, we have learned more from the perspective of people’s fundamental needs, people-oriented, and the psychological process of internal information cognition to understand the implementation of the entire information acquisition behavior more fully.

The main figures abroad on cognitive learning models and learning mechanisms are Bzdok, Danilo, and Andreas Meyer-Lindenberg. They proposed a multimedia learning cognitive model combining Baddeley’s working memory model, Paivio’s double-coding theory, and Sweller’s cognitive load theory [3]. On the basis of Mayer’s multimedia learning model, Wen, Guihua, and others proposed a comprehensive model of multimedia learning effect, which summarizes the process of multimedia learning into four types of elements: multimedia information input, cognitive processing, learning motivation, and knowledge and learning, among which learning motivation elements included learning style and cognitive participation [4]. American psychologist Kasabov puts people’s response factors as a function of variables. This theory also greatly promotes the development speed of cognitive psychology. This subject is gaining more and more attention in society [5]. Kanchanatawan believes that cognition does not occur independently of the human environment, psychological activities, and behavior but is based on various behavioral activities that people engage in. John and Keane of the United States carried out detailed analysis and systematic conclusions on cognitive psychology and further studied people’s psychological, physiological, memory, feeling, and other factors. We can see that the research should be more systematic and detailed, with a special emphasis on the factors of human perception [6]. This not only greatly promoted the development of psychology but also provided a powerful theoretical support for later scholars to study psychology.

Domestic researchers also have some unique understanding of the cognitive learning model: Zeng, Hong, and others explored the cognitive learning model through a lot of analysis and argumentation to a large extent determines the quality of online learning outcomes [7] Zhang, Tong, and others summarized the learning model by establishing scientific experiments. Good or bad has a significant impact on learning results [8]. Based on the information processing learning model, Gao, Zhongke, and others established an interactive cognitive complexity learning model and applied the model to teach design and proposed a teaching model with personal characteristics [9]. Lu et al. conducted a detailed analysis of the cognitive psychological process for users’ information services [10]. It proposes a form of knowledge management based on cognitive psychology and analyzes the information service model in detail from the influencing factors such as perception and attention in human psychological process and the guiding role of these psychological processes on information service [11]. These achievements all explain how to use cognitive psychology to encourage users to effectively use information resources, avoiding the waste and idleness of extensive information resources that have evolved with time [12].

In general, both domestic and foreign researches on the design of neural network have already achieved certain results from the perspective of cognitive psychology. Relatively speaking, the two still play a leading role under the scope of theoretical research abroad. The research is more profound and has a theoretical basis for practice. In this paper, through the research of neural network design based on cognitive psychology, the psychological mechanism behind the design of neural network elements is explored to improve the efficiency of neural network element design and resource utilization. Cognitive mental model is based on fuzzy comprehensive evaluation [13]. Based on the traditional cognitive psychology model, an expanded cognitive psychology model is constructed. Based on controlling the untrue data through the repeated answer rate of the test questions and the vector recording method, the logical composition structure of the cognitive psychological model is designed. At the same time, the improved AHP and partial correlation analysis method are used to determine the weight of the factors of fuzzy comprehensive evaluation, and the unit cognitive evaluation model and the disciplinary comprehensive evaluation model are constructed, respectively.

2. Cognitive Psychological Neural Network Model

2.1. Cognitive Learning Psychological Impact Classification

The cognitive process itself is a mental process for the brain to process information (Figure 1). It refers to how to find and explain the world around you in your own way of understanding, create new knowledge and information through your own understanding and experience to solve difficult problems in life, seek out rules, and adjust and manipulate psychological processes in order to make information be obtained easier. The whole cognitive process includes four parts [14]: (1) Perception, which is a collective term for feeling and perception, is actually a kind of reception of the information transmitted by the external world and the beginning of all cognitive processes. Information is transmitted to the human brain in the actual form of words, language, the human visual and auditory senses, and the signal of sensory information [15]. (2) Attention is to draw people’s attention to it with a vivid image to focus on the ideology and achieve the role occupied by the human heart. Only when the ideology is optimized can people achieve the best state of perception and organize the sensory information in the brain. (3) Memory is defined as a physiological phenomenon that stores and extracts information in the natural world. The memory process is to encode, store, and retrieve information. Coding is actually the acquisition of information, which is the first process of processing information, thus forming a memory representation. Storage is to keep the coded information. The extraction is to keep the stored information for recovery and reuse. (4) Use thinking and reasoning to solve problems. Thinking is also an internal cognitive process, which is to reprocess the internally stored long-term memory, that is, the so-called reorganization of long-term memory information, finely retell it, extract clues, compare information, and classify it. It is a deeper processing of long-term memory information [16].

Emotions are the driving force of all behaviors. It is precisely because of the triggering of emotions that the motivation to complete the behavior is generated. It is the guidance of emotions that enables the individual to maintain the completion of the established target behavior [17]. Therefore, it also plays a decisive role in information acquisition behavior. In the process of implementing information acquisition behavior, the difference in emotion directly affects the outcome of the behavior. Positive emotions directly stimulate the progress of the actors, so that the implementers of the behavior can continue to advance toward the goal of information acquisition. The information behavior is also more efficient, so that the behavior task can be performed more satisfactorily. However, under negative emotions, it will hinder information acquisition behavior, so that the behavior may not be completed, or it is difficult to achieve the desired effect during the entire information acquisition process.

From the perspective of information acquisition, although attitude is a good predictor, it is not an accurate indicator. Attitude will also elicit three responses. For example, I watched a movie that was vulgar (cognitive) disliked the movie (emotion) and unwilling to spend money to watch this bad movie (behavior). Sometimes, the information user is not interested in some information but still obtains this kind of information in a large amount however, sometimes the information acquisition behavior is still closely followed by the attitude for the information that matches the interest, the acquirer will spend more energy, and the information obtained is also more abundant and efficient.

2.2. Model Buildings

Human’s understanding of the objective world often has a certain degree of ambiguity. The analysis of many problems cannot ignore the ambiguity attached to humans’ view of the problem. Online learning evaluation cannot avoid the participation of human subjective consciousness activities, so it must be affected by human’s own character, preferences, experience, knowledge, and technical level.

The neural network evaluation method obtains the weight of each node by training the sample set, thereby establishing a neural network evaluation model for online learning quality evaluation. Although this method avoids subjective factors in the process of obtaining weight, the entire process is a “dark box” operation, and the complete data processing process is difficult to be understood and accepted [18]. Fuzzy comprehensive evaluation analyzes complex objects from a hierarchical perspective and is not limited to linear models, which satisfy the requirements for evaluating complex objects. The processing of fuzzy comprehensive evaluation can be given by mathematical expressions, and the evaluation results are also expressed in the form of vectors, and the evaluation information is rich and clear at a glance. The evaluation level is shown in Table 1.

By establishing a fuzzy mapping relationship between the influencing factor set E and the rating evaluation level J, a fuzzy matrix representation is established:


A New Map Of The Brain That Is Unlike Anything You Have Seen Before

The cerebral cortex is widely believed to be the area of the brain where thought, cognition and the processing of complex sensory information takes place. Recently, an international team of researchers used several different types of MRI (

Map of the United States created in 1803. Credit: Free Stock Photos

Maps evolve over time as mapping technology improves and more is learned about the place being mapped. Back in the day, the best maps people had of North America were like the one shown above. It's accurate after a fashion and if you used it to guide your path from New York City to San Francisco you'd probably get there eventually, but you'd almost certainly get lost more than once along the way. Google Earth is a big improvement.

Maps are generally enhanced when more detail is added and previously existing details are represented more accurately. However, sometimes these improvements can lead to confusion when names for well-known places are changed. Think of all of the Martin Luther King Drives that didn't exist 60 years ago and the confusion that resulted when drivers saw road signs that didn't match the street name on the their maps.

It's not a major problem w hen only one road has its name changed . Imagine what it would be like, however, if all of the roads had their names changed several times. The proliferation of multiple names for the same thing on road maps is analogous to what has happened with brain maps as our understanding of how the brain is structured and how it functions has changed over time.

The area of the brain where visual information is processed provides a good example. Visual information is processed in the occipital lobe which is an area of the cerebral cortex in the lower rear of the brain behind the base of the skull. Sections of the occipital lobe have several different names that reflect different approaches to understanding the brain. One approach divides the occipital lobe into the striate and extrastriate cortices. The striate cortex is also know as the primary visual cortex, Broadman area 17 and area V1. The extrastriate cortex is sometimes called the peristriate cortex and includes areas that are sometimes called Broadman areas 18 and 19, or visual areas V2, V3, V4 and V5.

People who have training in neuroanatomy or neuroscience soon learn to sort all of this out, but for someone without that training it can get confusing. It's hard to understand what's going on when different people use different names to refer to the same area of the brain. A set of universally agreed-on terms based on a sophisticated map of the cerebral cortex would make things easier for everybody.

A myelin map of the left hemisphere. Credit: Nature Video/YouTube

A new map of the brain

An international team of researchers used a combination of state-of-the-art imaging techniques, algorithmic mapping and manual identification by neuroanatomists to construct a map of the cerebral cortex that takes a large step toward building the Google Earth of brain maps.

Maps of the cerebral cortex are usually based on a single neurobiological feature such as large-scale structures or task-related functions. The research team based their map on four neurobiological features and the cortical areas they identified had to be picked out by at least two of the four. The features they used were:

  • Microstructural architecture such as the thickness of the cortex or the amount of myelin in an area. Myelin is a fatty substance that sheaths and insulates the axons of some neurons. It's white in color and is often referred to as the "white matter" in the brain. Myelin density is shown in the image above.
  • Functional specialization refers to groups of neurons in areas of the brain that operate together to carry out a specific function. Brain maps based solely on fMRI data usually contrast brightly colored active areas with dark inactive areas to identify functionally specialized areas of the cerebral cortex.
  • Connectivity relationships between areas is just what it sounds like. Mapping the connections between areas is analogous to constructing a wiring diagram of the brain. It is one of the most sought-after goals of contemporary neuroscience.
  • Within-area topographic organization refers to how the various subsections or parts of an area are arranged with respect to each other. An example would be how the map of visual space captured by the retina is represented in an area of the cortex that processes visual information.

The brain map was built and then tested with imaging data from the Human Connectome Project (HCP) which is funded by the National Institutes of Health The HCP is a five-year project dedicated to building a complete map of the brain's structure combined with a wiring diagram of its functional connections. The project collects data using state-of-the-art imaging technologies, creates tools for processing the data, and makes both the data and the tools available for research in a variety of fields.

Structural MRI (T1 and T2 weighted), task-related fMRI, and resting-state fMRI images from 210 HCP subjects were used to build the map. Brain areas based on architecture, function, connectivity and topology were initially identified by an algorithm and then refined by neuroanatomists. When the map was complete, it was used to train a machine learning program to identify brain areas. The program was then tested on data from 210 different HCP subjects and was able to successfully identify 96.6% of the areas delineated on the map.

Credit: Nature Video/YouTube

The image above shows the areas identified in the lateral (outside) view of the left hemisphere. The colors show the degree to which areas are associated with somatosensory (green), auditory (red), or visual (blue) processing in resting-state fMRI data. The white-black continuum shows areas that are task-positive (white) and negative (black) during resting-state tasks.

For each hemisphere, the map contains 83 areas that had been previously identified and 97 areas that had not. In other words, almost 54% of the 180 areas that were mapped in each hemisphere had not been previously identified. This is a huge increase in our knowledge about how the cerebral cortex is organized.

The new map is such a large advance over what was available before that it may become the standard map of the cerebral cortex. If that happens, the names given to each area on the map should eventually supplant the multitude of names that are currently in use. Confusion over names isn't the only benefit that can be derived from a better map, however. The map's more refined and accurate delineation of the areas in the cerebral cortex should also contribute to a greater understanding of what these areas do and how they are connected to each other.

The map itself is not the only benefit that comes from this research. The methods the investigators used to create the map are also valuable. It was recently reported that tens of thousands of fMRI brain-mapping studies published over the past 20 years may be flawed because assumptions underlying the statistical methods that are commonly used to analyze fMRI data may not have been met. The methodology used to create the new brain map provides one possible solution to this problem.

Rather than rely on one type of MRI data to identify an area, the researchers used several and only included an area in the map if it was picked out by at least two types of MRI. Different types of data often require different analytical tools and methods. Thus, a problem with the analysis of one type of data is unlikely to be repeated with another, and f alse identifications based on flawed analyses are improbable if it is required that at least two types of data are needed to identify an area.

The investigators named their map the Human Connectome Project Multi-Modal Parcellation version 1.0. They call it "version 1.0" because they view it as a first step. It may be a first step, but it's an awfully big one.


Many maps of the brain

Your brain has at least four different senses of location -- and perhaps as many as 10. And each is different, according to new research from the Kavli Institute for Systems Neuroscience, at the Norwegian University of Science and Technology.

The findings, published in the 6 December 2012 issue of Nature, show that rather than just a single sense of location, the brain has a number of "modules" dedicated to self-location. Each module contains its own internal GPS-like mapping system that keeps track of movement, and has other characteristics that also distinguishes one from another.

"We have at least four senses of location," says Edvard Moser, director of the Kavli Institute. "Each has its own scale for representing the external environment, ranging from very fine to very coarse. The different modules react differently to changes in the environment. Some may scale the brain's inner map to the surroundings, others do not. And they operate independently of each other in several ways."

This is also the first time that researchers have been able to show that a part of the brain that does not directly respond to sensory input, called the association cortex, is organized into modules. The research was conducted using rats.

Technical breakthroughs

A rat's brain is the size of a grape, while the area that keeps track of the sense of location and memory is comparable in size to a small grape seed. This tiny area holds millions of nerve cells.

A research team of six people worked for more than four years to acquire extensive electrophysiological measurements in this seed-sized region of the brain. New measurement techniques and a technical breakthrough made it possible for Hanne Stensola and her colleagues to measure the activity in as many as 186 grid cells of the same rat brain. A grid cell is a specialized cell named for its characteristic of creating hexagonal grids in the brain's mental map of its surroundings.

"We knew that the 'grid maps' in this area of the brain had resolutions covering different scales, but we did not know how independent the scales were of each other," Stensola said. "We then discovered that the maps were organized in four to five modules with different scales, and that each of these modules reacted slightly differently to changes in their environment. This independence can be used by the brain to create new combinations -- many combinations -- which is a very useful tool for memory formation."

After analysing the activity of nearly 1000 grid cells, researchers were able to conclude that the brain has not just one way of making an internal map of its location, but several.

Perhaps 10 different senses of location

Institute director Moser says that while researchers are able to state with confidence that there are at least four different location modules, and have seen clear evidence of a fifth, there may be as many as 10 different modules.

He says, however, that researchers need to conduct more measurements before they will have covered the entire grid-cell area. "At this point we have measured less than half of the area," he says.

Aside from the time and challenges involved in making these kinds of measurements, there is another good reason why researchers have not yet completed this task. The lower region of the sense of location area, the entorhinal cortex, has a resolution that is so coarse or large that it is virtually impossible to measure it.

"The thinking is that the coordinate points for some of these maps are as much as ten metres apart," explains Moser. "To measure this we would need to have a lab that is quite a lot larger and we would need time to test activity over the entire area. We work with rats, which run around while we make measurements from their brain. Just think how long it would take to record the activity in a rat if it was running back and forth exploring every nook and cranny of a football field. So you can see that we have some challenges here in scaling up our experiments."

New way to organize

Part of what makes the discovery of the grid modules so special is that it completely changes our understanding of how the brain physically organizes abstract functions. Previously, researchers have shown that brain cells in sensory systems that are directly adjacent to each other tend to have the same response pattern. This is how they have been able to create detailed maps of which parts of the sensory brain do what.

The new research shows that a modular organization is also found in the highest parts of the cortex, far away from areas devoted to senses or motor outputs. But these maps are different in the sense that they overlap or infiltrate other. It is thus not possible to locate the different modules with a microscope, because the cells that work together are intermingled with other modules in the same area.

"The various components of the grid map are not organized side by side," explains Moser. "The various components overlap. This is the first time a brain function has been shown to be organized in this way at separate scales. We have uncovered a new way for neural network function to be distributed."

A map and a constant

The researchers were surprised, however, when they started calculating the difference between the scales. They may have discovered an ingenious mathematical coding system, along with a number, a constant. (Anyone who has read or seen "The Hitchhiker's Guide to the Galaxy" may enjoy this.) The scale for each sense of location is actually 42% larger than the previous one.

"We may not be able to say with certainty that we have found a mathematical constant for the way the brain calculates the scales for each sense of location, but it's very funny that we have to multiply each measurement by 1.42 to get the next one. That is approximately equal to the square root of the number two," says Moser.

Maps are genetically encoded

Moser thinks it is striking that the relationship between the various functional modules is so orderly. He believes this orderliness shows that the way the grid map is organized is genetically built in, and not primarily the result of experience and interaction with the environment.

So why has evolution equipped us with four or more senses of location?

Moser believes the ability to make a mental map of the environment arose very early in evolution. He explains that all species need to navigate, and that some types of memory may have arisen from brain systems that were actually developed for the brain's sense of location.

"We see that the grid cells that are in each of the modules send signals to the same cells in the hippocampus, which is a very important component of memory," explains Moser. "This is, in a way, the next step in the line of signals in the brain. In practice this means that the location cells send a different code into the hippocampus at the slightest change in the environment in the form of a new pattern of activity. So every tiny change results in a new combination of activity that can be used to encode a new memory, and, with input from the environment, becomes what we call memories.

The article is a part of doctoral research conducted by Hanne and Tor Stensola, and has been funded through an Advanced Investigator Grant that Edvard Moser was awarded by the European Research Council (ERC).


We reconstruct memories when we retrieve them

The film shows memories as stable and complete representations of actual events – something we know is not the case. The events of Riley’s day are automatically “encoded” into a single globe. Each memory globe is “stored” somewhere on a shelf in a vast long-term storage library. Memories are “retrieved” and sent intact and exact, back to Headquarters and, therefore, to consciousness.

That might be a handy visual metaphor for memory, but it’s not actually how memory works. We do encode events from our daily life without a deliberate intention to learn or remember them. For instance, you remember what you had for breakfast today even though you did not have to try to remember that information. But, our brain doesn’t store each memory as an individual whole unit.

Instead scholars believe that the components of events are processed by individual neural modules. Our brain has separate systems for basic cognitive functions: vision, hearing, language, emotion and so on. Visual components are processed by the visual system, auditory components by the auditory system, emotional components by the limbic system. Memories are stored in bits and pieces all over your brain. There is no globe sitting on a shelf that can be retrieved and used to reproduce the event exactly as it happened.

When we retrieve a memory, we reconstruct it from those component pieces. We use the same neural systems that encoded the components to see the event in our mind’s eye, hear it in our mind’s ear and re-experience the emotions associated with the event. That reconstructive process is influenced by what we know about the world around us, our current thoughts and beliefs, and our ongoing goals. So our memories can change over time, just as we do through the years.

In fact, each time we remember an event, we are simultaneously re-encoding that event, making it less likely to be forgotten.


Topographic maps of multisensory attention

The intraparietal sulcus (IPS) region is uniquely situated at the intersection of visual, somatosensory, and auditory association cortices, ideally located for processing of multisensory attention. We examined the internal architecture of the IPS region and its connectivity to other regions in the dorsal attention and cinguloinsular networks using maximal connectivity clustering. We show with resting state fMRI data from 58 healthy adolescent and young adult volunteers that points of maximal connectivity between the IPS and other regions in the dorsal attention and cinguloinsular networks are topographically organized, with at least seven maps of the IPS region in each hemisphere. Distinct clusters of the IPS exhibited differential connectivity to auditory, visual, somatosensory, and default mode networks, suggesting local specialization within the IPS region for different sensory modalities. In an independent task activation paradigm with 16 subjects, attention to different sensory modalities showed similar functional specialization within the left intraparietal sulcus region. The default mode network, in contrast, did not show a topographical relationship between regions in the network, but rather maximal connectivity in each region to a single central cluster of the other regions. The topographical architecture of multisensory attention may represent a mechanism for specificity in top-down control of attention from dorsolateral prefrontal and lateral orbitofrontal cortex and may represent an organizational unit for multisensory representations in the brain.

Brain regions with related function and anatomic connectivity show synchrony of slow (<0.08 Hz) fluctuations in functional MRI (fMRI) signal (1–3). A network of brain regions known to be active during states of high attention to sensory stimuli or performance of attention-demanding tasks, the attention control network, or task positive network (4–6), reproducibly shows high functional connectivity between regions in the network. A separate interconnected network, the default mode, or task negative network (7–9), is comprised of brain regions more active during rest or attention to internal stimuli or narrative (10). We use here the nomenclature “attention control” and “default mode” networks rather than “task positive” or “task negative” networks because the positive or negative activation of each of these networks depends entirely on whether the task measures internal mentalization or attention to external stimuli (11), and both may be coactivated or have similar behavioral associations (12).

The attention control network consists of two primary subnetworks. The dorsal attention network is composed of bilateral intraparietal sulcus (IPS), frontal eye fields (FEF), and lateral prefrontal cortex (4, 5) and has also been termed the executive control network (13). This network frequently shows coactivation with the cinguloinsular, or salience detection network, consisting of bilateral anterior insula, dorsal anterior cingulate/supplementary motor area (SMA), and bilateral middle temporal (MT + ) regions (13). These networks both tend to be more active during tasks requiring higher attentional demands and may in aggregate be referred to as the attention control network (6). The attention control network may also be operationally defined as areas that are significantly correlated with IPS, MT + , and FEF regions (6).

The IPS region is perhaps the best understood region involved in multimodal sensory attention. Lesions in the IPS region may cause neglect of modality-specific or polymodal attention (14). Auditory and visual attentional areas are processed in overlapping but distinct inferolateral and posterior subregions of the IPS (15). Distinct human IPS subregions corresponding to macaque anterior intraparietal (AIP), ventral intraparietal (VIP), medial intraparietal (MIP), lateral intraparietal (LIP), and caudal intraparietal (CIP) regions show different spatial positions within the IPS likely corresponding to functional differences in attentional modality (16). Four visual attentional areas, IPS1–IPS4 show topographic maps of human visual cortex and are situated along the posterior medial aspect of the IPS region, adjacent to area V7 (17, 18). The IPS region is thought to represent a site for top-down control of attention (4).

The default mode network consists of bilateral posterior cingulate/precuneus, medial prefrontal, temporoparietal junction, superior frontal, parahippocampal gyri, cerebellar tonsils, and inferior temporal regions (6, 8, 19) and may be defined as areas significantly correlated with posterior cingulate/precuneus, medial prefrontal, and temporparietal junction regions (6). There is converging evidence that this network participates in attending to internal stimuli or narrative and constructing a multifaceted representation of “self.” Narrative-specific activation has been observed in the precuneus (20), medial prefrontal cortex (21, 22), and temporoparietal junction (22). Default mode regions show time variation with narrative stimuli (23). The default mode network has also shown greater activity during self-referential mental activity (24), including visuospatial imagery, episodic memory retrieval, and first-person perspective (10). The observation of default mode activity when subjects’ minds were allowed to wander also suggests this network may process internal narrative, mentalization, and self-dialogue (25).

Both networks consist of discrete brain regions separated in space, an organization which might represent redundancy, allowing distributed processing of important brain functions, and/or specialization, where different regions perform distinct operations or compare different input streams. Clues to the functional relationship between such regions in a network can be obtained by examining the pattern of connectivity between the regions. At least three distinct patterns of connectivity are possible in such a network: there may be diffuse interconnectivity between the regions, where all voxels in a region are similarly connected to all voxels in other regions voxels in one region may all show greatest connectivity to a central hub in other regions representing the “core” of the network or there may be point-to-point, topographic connectivity between two or more regions. Distinguishing between such possibilities would constrain what operations could be performed on information within the network and would inform choices for targets in studying brain connectivity in neuropathology and neurodevelopment.


A map of the brain can tell what you’re reading

Summary: fMRI brain scans reveal semantic tuning during both reading and listening to words are highly correlated in selective areas of the cerebral cortex. The new brain maps enabled researchers to accurately predict which words would active specific regions of the cortex.

Source: UC Berkeley

Too busy or lazy to read Melville’s Moby Dick or Tolstoy’s Anna Karenina? That’s OK. Whether you read the classics, or listen to them instead, the same cognitive and emotional parts of the brain are likely to be stimulated. And now, there’s a map to prove it.

Neuroscientists at the University of California, Berkeley, have created interactive maps that can predict where different categories of words activate the brain. Their latest map is focused on what happens in the brain when you read stories.

The findings, to appear Aug. 19 in the Journal of Neuroscience, provide further evidence that different people share similar semantic — or word-meaning — topography, opening yet another door to our inner thoughts and narratives. They also have practical implications for learning and for speech disorders, from dyslexia to aphasia.

“At a time when more people are absorbing information via audiobooks, podcasts and even audio texts, our study shows that, whether they’re listening to or reading the same materials, they are processing semantic information similarly,” said study lead author Fatma Deniz, a postdoctoral researcher in neuroscience in the Gallant Lab at UC Berkeley and former data science fellow with the Berkeley Institute for Data Science.

For the study, people listened to stories from “The Moth Radio Hour,” a popular podcast series, and then read those same stories. Using functional MRI, researchers scanned their brains in both the listening and reading conditions, compared their listening-versus-reading brain activity data, and found the maps they created from both datasets were virtually identical.

The results can be viewed in an interactive, 3D, color-coded map, where words — grouped in such categories as visual, tactile, numeric, locational, violent, mental, emotional and social — are presented like vibrant butterflies on flattened cortices. The cortex is the coiled surface layer of gray matter of the cerebrum that coordinates sensory and motor information.

The interactive 3D brain viewer is scheduled to go online this month.

As for clinical applications, the maps could be used to compare language processing in healthy people and in those with stroke, epilepsy and brain injuries that impair speech. Understanding such differences can aid recovery efforts, Deniz said.

The semantic maps can also inform interventions for dyslexia, a widespread, neurodevelopmental language-processing disorder that impairs reading.

“If, in the future, we find that the dyslexic brain has rich semantic language representation when listening to an audiobook or other recording, that could bring more audio materials into the classroom,” Deniz said.

And the same goes for auditory processing disorders, in which people cannot distinguish the sounds or “phonemes” that make up words. “It would be very helpful to be able to compare the listening and reading semantic maps for people with auditory processing disorder,” she said.

Nine volunteers each spent a couple of hours inside functional MRI scanners, listening and then reading stories from “The Moth Radio Hour” as researchers measured their cerebral blood flow.

Their brain activity data, in both conditions, were then matched against time-coded transcriptions of the stories, the results of which were fed into a computer program that scores words according to their relationship to one another.

These color-coded maps of the brain show the semantic similarities during listening (top) and reading (bottom).The image is credited to Fatma Deniz.

Using statistical modeling, researchers arranged thousands of words on maps according to their semantic relationships. Under the animals category, for example, one can find the words “bear,” “cat” and “fish.”

The maps, which covered at least one-third of the cerebral cortex, enabled the researchers to predict with accuracy which words would activate which parts of the brain.

The results of the reading experiment came as a surprise to Deniz, who had anticipated some changes in the way readers versus listeners would process semantic information.

“We knew that a few brain regions were activated similarly when you hear a word and read the same word, but I was not expecting such strong similarities in the meaning representation across a large network of brain regions in both these sensory modalities,” Deniz said.

Her study is a follow-up to a 2016 Gallant Lab study that recorded the brain activity of seven study subjects as they listened to stories from “The Moth Radio Hour.”


Affiliations

Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK, Amsterdam, The Netherlands

Shir Hofstetter, Yuxuan Cai & Serge O. Dumoulin

Experimental and Applied Psychology, Vrije University Amsterdam, Van der Boechorststraat 7, 1181 BT, Amsterdam, The Netherlands

Yuxuan Cai & Serge O. Dumoulin

Experimental Psychology, Helmholtz Institute, Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, The Netherlands

Ben M. Harvey & Serge O. Dumoulin

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Contributions

S.H. and S.O.D. designed the study S.H. and Y.C. collected the data S.H. performed the analysis S.H., B.M.H., and S.O.D wrote the paper. We wish to thank Prof. Astrid Kappers for their valuable input.

Corresponding author


Watch the video: Sensory Memory - VCE Unit 3 Psychology (January 2022).