Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

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An integrative theory of prefrontal cortex function

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

genome are dramatically reshaping the research and development pathways for drugs, vaccines, and diagnostics. The growth in the number of molecular entities entering the drug development pipeline has accelerated as a consequence of powerful discovery and screening technologies such as combinatorial chemistry, mass spectrometry, high throughput screening, celland tissue-based DNA microarrays, and proteomic approaches.1 As a consequence, there is an escalating number of therapeutic candidates, which has caused the need for new technologies and strategies to streamline the process to make safe and effective therapies available to patients. One approach to the achievement of more expeditious and informative therapeutic research is the use of precise clinical measurement tools to determine disease progression and the effects of interventions (drugs, surgery, and vaccines). For example, gene-based approaches such as single nucleotide polymorphism maps are now being developed to distinguish the molecular and cellular basis for variations in clinical response to therapy.2 Another approach is the use of a wide array of analytical tools to assess biological parameters, which are referred to as biomarkers. Biomarker measurements can help explain empirical results of clinical trials by relating the effects of interventions on molecular and cellular pathways to clinical responses. In doing so, biomarkers provide an avenue for researchers to gain a mechanistic understanding of the differences in clinical response that may be influenced by uncontrolled variables (for example, drug metabolism). There are a variety of ways that biomarker measurements can aid in the development and evaluation of COMMENTARY

Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework*

We present a computer method that assists in the analysis and interpretation of metabolic functional patterns occurring in 3-Dimensional human brain images produced by PET and SPECT cameras. We introduce the concept of the Metabolic Spectral Signature, a continuous curve across the metabolic spectrum that objectively generates optimum descriptors of asymmetric metabolic patterns and automatically selects the associated anatomical brain structures.We applied it to the study of asymmetries in PET brain images of normal and schizophrenic subjects and found three narrow bands in the metabolic spectrum, a=[2,3.51, p=[5.5,7.5] and y=[ 12,12.51 (mg glucose/lOO gr/min), where schizophrenic brains have statistically significant asymmetric metabolic patterns with respect to the normal brains.The anatomical structures identified with these spectral bands utilize 14%,35% and 4% of the total metabolic activity in a normal brain.These results suggest that the Metabolic Spectral Signature is a useful approach to classify clinical groups and increase our understanding of the relationship between functional and anatomical systems in the brain and their disruption by mental illness or drug abuse.

The Spectral Analysis Of Brain Glucose Metabolism

Comment: It's time for addiction science to supersede stigma

We read with great interest the article “Roux en Y Gastric Bypass Increases Ethanol Intake in the Rat” [1] from Jon Davis and colleagues, published on line in the Obesity Surgery on February 26, 2013. This study reports an impressive data set showing increased alcohol consumption in an outbred rat strain after Roux-en Y gastric bypass surgery (RYGB), a finding that has potential important clinical implications. This study supports recent clinical investigations showing increased susceptibility to alcohol abuse following RYGB [2-5] and corroborate our recent studies that reported increased alcohol taking, and increased motivation to work for alcohol reward in dietary obese rats that received RYGB [6, 7].

However we were surprised that the authors did not refer to prior publications showing similar outcomes [6, 7], which would have strengthen the conclusion of their paper. The authors also failed to cite their prior paper [8], which reported decreases in alcohol intake after RYGB in alcohol-preferring rats as well as decreased alcohol intake after RYGB in patients who regularly drank alcohol prior to undergoing surgery. The differences between their current results and their prior findings suggest that the effects of RYGB may be modulated by genetic background and should have been discussed rather than ignored.

There are increasing concerns over the lack of reproducibility of findings from preclinical research [9], yet study of Davis et al. show similar findings to the ones we previously reported using a similar animal model and surgical procedure but done by an independent laboratory.

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Notes on “Roux en Y Gastric Bypass Increases Ethanol Intake in the Rat” by Davis et al.

/pdf/correction-chronic-methamphetamine-effects-on-brain-1lv5zjy606.pdf

Correction: Chronic Methamphetamine Effects on Brain Structure and Function in Rats.

As one of the nine subtypes of adrenergic receptors (ARs) in the brain, α2C-ARs play essential roles in emotion and memory, and are implicated in neuropsychiatric disorders, including depression, Alzheimer's disease, substance use disorder, and schizophrenia. A recently developed α2C-AR specific positron emission tomography (PET) radiotracer, [11C]ORM-13070, showed promise for imaging α2C-ARs in the brain. Herein, we prepared highly potent C-11 labeled benzo-1,4-dioxane derivatives and compared them with [11C]ORM-13070, aiming to improve the specific binding signal, as evaluated by in vivo rodent brain PET imaging.© 2022 Elsevier Inc. All rights reserved. 

Nora D. Volkow

Papers

The Spectral Analysis Of Brain Glucose Metabolism

Comment: It's time for addiction science to supersede stigma

Notes on “Roux en Y Gastric Bypass Increases Ethanol Intake in the Rat” by Davis et al.

Correction: Chronic Methamphetamine Effects on Brain Structure and Function in Rats.

Preclinical evaluation of new C-11 labeled benzo-1,4-dioxane PET radiotracers for brain α2C adrenergic receptors.