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*

Cocaine profoundly affects both cerebral blood vessels and neuronal activity in the brain. The vasoconstrictive effects of cocaine, concurrently with its effects on neuronal [Ca2+]i accumulation are likely to jeopardize neuronal tissue that in the prefrontal cortex (PFC) could contribute to impaired self-regulation and compulsive cocaine consumption. Here we used optical imaging to study the cerebrovascular and neuronal effects of acute cocaine (1 mg/kg i.v.) and to examine whether selective blockade of L-type Ca2+ channels by Nifedipine (NIF) (0.5 mg/kg i.v.) would alleviate cocaine's effects on hemodynamics (measured with cerebral blood volume, HbT), oxygenation (measured with oxygenated hemoglobin, HbO2) and neuronal [Ca2+]i, which were concomitantly measured in the PFC of naive rats. Our results show that in the PFC acute cocaine significantly reduced flow delivery (HbT), increased neuronal [Ca2+]i accumulation and profoundly reduced tissue oxygenation (HbO2) and these effects were significantly attenuated by NIF pretreatment. They also show that cocaine-induced vasoconstriction is distinct from its increase of neuronal [Ca2+]i accumulation though both of them contribute to hypoxemia and both effects were attenuated by NIF. These results provide evidence that blockade of voltage-gated L-type Ca2+ channels might be beneficial in preventing vasoconstriction and neurotoxic effects of cocaine and give support for further clinical investigations to determine their value in reducing cocaine's neurotoxicity in cocaine use disorders.

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Ca2+ channel blockade reduces cocaine's vasoconstriction and neurotoxicity in the prefrontal cortex.

Positron emission tomography (PET) is a new nuclear medicine technique that has recently entered the clinical realm of medicine. Although it is a technique that can be utilized for assessment of biochemical and physiological parameters of any organ in the body, it has particular utility in the investigation of the brain. PET poses unique advantages over previous imaging devices. For the first time, it is feasible to investigate directly various biological parameters of the brain in a noninvasive way. PET allows for investigating the functional, biochemical, physiological, and pharmacological characteristics of various areas within the brains of normal and psychiatric or neurological patients. Although it has already started to give promising results, it is too new a technique to obtain an accurate appraisal of its true potentials. This is a problem that seems always to surface when one tries to evaluate the utility of a new technique in a new area of research. The problem is accentuated in the case of PET where there is no other technique available with which to compare results. This paper will discuss the basic principles of PET, its relationship to other existing imaging devices, and the issues to be considered when making a technological assessment of positron emission tomography.

Nora D. Volkow

Papers

Ca2+ channel blockade reduces cocaine's vasoconstriction and neurotoxicity in the prefrontal cortex.

Positron emission tomography: a technology assessment.

Addiction should be treated, not penalized.

Effects of Cannabis Use on Human Behavior—Reply

Research on substance use disorders during the COVID-19 pandemic