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*

The future of career development awards : How will it influence mental health research? Authors' reply

The Need for Objective Measures of Nicotine Use and Cessation.

The effect on chronic crack users of a 3 month detoxification programme on lung clearance of inhaled 99 Tc m -diethylenetriamine pentaacetate ( 99 Tc m -DTPA) aerosol, spirometry and gas exchange was determined in a controlled in-patient clinical treatment setting. Imaging studies were carried out in eight chronic crack users (four crack-only and four crack plus tobacco) before and after the successful completion of the detoxification programme to measure the clearance of inhaled 99 Tc m -DTPA from the lungs, an index of lung epithelial permeability. 99 Tcm-DTPA lung clearance, expressed in terms of the biological half-time, T μ was determined from the slopes of the least-squares fit regression lines of the respective time-activity plots. The mean (± S.D.) global T 1/2 values of the crack-only (75±39 min) and crack plus tobacco users (22±10 min) were significantly shorter (P < 0.02 and P < 0.001, respectively) than from the lungs of the non-smoking controls (124±29 min). This was consistent with increased lung epithelial permeability secondary to crack-related lung injury. The mean global T 1/2 value of the crack plus tobacco users was significantly shorter (P <0.05) than that of the crack-only users. After detoxification, the abnormally rapid lung clearance became normal in two of the four crack-only users studied, improved in a third and remained unchanged in the fourth, a subject whose T 1/2 value was already normal initially. However, lung clearance improved in only one of the four crack plus tobacco users studied. Faster 99 Tc m -DTPA clearance was the only impairment found in seven of the eight crack users, the eighth having restrictive lung disease. Crack-related lung injury, reflected by abnormally rapid 99 Tc m -DTPA lung clearance, may be at least partially reversible after a 3 month period of abstinence from crack.

Does detoxification reverse the acute lung injury of crack smokers

The use of Positron Emission Tomography (PET) to examine drug pharmacokinetics and pharmacodynamics and the relationship of these properties to the behavioral, therapeutic and toxic properties of drugs is emerging as a powerful new scientific tool (Fowler et al., 1990). PET provides a new perspective on drug research by virtue of its ability to directly assess different parameters such as absolute uptake, regional distribution, kinetics, pharmacological profile and metabolic effects of drugs in human subjects. For example, the labeled drug itself can be used to measure the absolute uptake, regional distribution and kinetics at its site of action in the body. Additionally, the labeled drug and whole body PET can be used to determine the target organs for the drug and its labeled metabolites, thus providing information on potential toxic effects as well as tissue half-lives. On the other hand, different labeled tracers can be used to assess the effects of a drug on particular physiological or functional processes. For example, with appropriate radiotracers, the effects of a drug on metabolism, neurotransmitter activity, blood flow, enzyme activity or other processes can be probed. Because of the short half-life of the positron emitters, these parameters can be assessed directly in the human body both in normal controls and in patients and serial studies can be done where a subject serves as his own control.

Positron Emission Tomography, Enzymes and Drug Research and Development

Addiction is a chronic disease process that develops in susceptible individuals upon repeated consumption of drugs of abuse. Neuroimaging technology has allowed an in vivo view into the addicted human brain. This view expanded as functional neuroimaging research began to facilitate the modeling of complex behavioral and psychological phenomena into a temporally dynamic web of an interconnected neural circuitry. Here, we provide a review of drug addiction, highlighting the neurobiological mechanisms that underlie its core behavioral characteristics as revealed by imaging studies. Visualizing the structural, neurochemical, and functional impairments in the brains of drug-addicted individuals and integrating results with measures of behavior, cognition, and emotion offers an opportunity for an in-depth mechanistic understanding of human drug addiction. Such understanding is crucial for designing therapeutic and preventive behavioral and psychopharmacological interventions.

Nora D. Volkow

Papers

The future of career development awards : How will it influence mental health research? Authors' reply

The Need for Objective Measures of Nicotine Use and Cessation.

Does detoxification reverse the acute lung injury of crack smokers

Positron Emission Tomography, Enzymes and Drug Research and Development

Drug Addiction: Neuroimaging