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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Functional neuroimaging studies have started unravelling unexpected functional attributes for the posteromedial portion of the parietal lobe, the precuneus. This cortical area has traditionally received little attention, mainly because of its hidden location and the virtual absence of focal lesion studies. However, recent functional imaging findings in healthy subjects suggest a central role for the precuneus in a wide spectrum of highly integrated tasks, including visuo-spatial imagery, episodic memory retrieval and self-processing operations, namely first-person perspective taking and an experience of agency. Furthermore, precuneus and surrounding posteromedial areas are amongst the brain structures displaying the highest resting metabolic rates (hot spots) and are characterized by transient decreases in the tonic activity during engagement in non-self-referential goal-directed actions (default mode of brain function). Therefore, it has recently been proposed that precuneus is involved in the interwoven network of the neural correlates of self-consciousness, engaged in self-related mental representations during rest. This hypothesis is consistent with the selective hypometabolism in the posteromedial cortex reported in a wide range of altered conscious states, such as sleep, drug-induced anaesthesia and vegetative states. This review summarizes the current knowledge about the macroscopic and microscopic anatomy of precuneus, together with its wide-spread connectivity with both cortical and subcortical structures, as shown by connectional and neurophysiological findings in non-human primates, and links these notions with the multifaceted spectrum of its behavioural correlates. By means of a critical analysis of precuneus activation patterns in response to different mental tasks, this paper provides a useful conceptual framework for matching the functional imaging findings with the specific role(s) played by this structure in the higher-order cognitive functions in which it has been implicated. Specifically, activation patterns appear to converge with anatomical and connectivity data in providing preliminary evidence for a functional subdivision within the precuneus into an anterior region, involved in self-centred mental imagery strategies, and a posterior region, subserving successful episodic memory retrieval.

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The precuneus: a review of its functional anatomy and behavioural correlates.

Schultz, Wolfram. Predictive reward signal of dopamine neurons. J. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest t...

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Predictive Reward Signal of Dopamine Neurons

The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.

/pdf/the-human-connectome-a-structural-description-of-the-human-31vln7dys2.pdf

The Human Connectome: A Structural Description of the Human Brain

Modeling parietal-premotor interactions in primate control of grasping

The functional anatomy of reaching and grasping simple objects was determined in nine healthy subjects with positron emission tomography imaging of regional cerebral blood flow (rCBF). In a prehension (grasping) task, subjects reached and grasped illuminated cylindrical objects with their right hand. In a pointing task, subjects reached and pointed over the same targets. In a control condition subjects looked at the targets. Both movement tasks increased activity in a distributed set of cortical and subcortical sites: contralateral motor, premotor, ventral supplementary motor area (SMA), cingulate, superior parietal, and dorsal occipital cortex. Cortical areas including cuneate and dorsal occipital cortex were more extensively activated than ventral occipital or temporal pathways. The left parietal operculum (putative Sll) was recruited during grasping but not pointing. Blood flow changes were individually localized with respect to local cortical anatomy using sulcal landmarks. Consistent anatomic landmarks from MRI scans could be identified to locate sensorimotor, ventral SMA, and Sll blood flow increases. The time required to complete individual movements and the amount of movement made during imaging correlated positively with the magnitude of rCBF increases during grasping in the contralateral inferior sensorimotor, cingulate, and ipsilateral inferior temporal cortex, and bilateral anterior cerebellum. This functionalanatomic study defines a cortical system for "pragmatic" manipulation of simple neutral objects.

Functional Anatomy of Pointing and Grasping in Humans

The decreasing size and cost of wearable computers and mobile sensors is presenting new challenges and opportunities for deploying networks. Existing network routing protocols provide reliable communication between nodes and allow for mobility and even ad-hoc deployment. They rely, however on the assumption of a dense scattering of nodes and end-to-end connectivity in the network. In this paper we address routing support for ad-hoc, wireless networks under conditions of sporadic connectivity and ever-present network partitions. This work proposes a general framework of agent movement and communication in which mobile computers physically carry packets across network partitions. We then propose algorithms that exploit the relative position of stationary devices and non-randonmess in the movement of mobile agents in the network. The learned structure of the network is used to inform an adaptive routing strategy With a simulation, we evaluate these algorithms and their ability to route packets efficiently through a highly-partitioned network.

Wearable computers as packet transport mechanisms in highly-partitioned ad-hoc networks

Grafton, S. T., A. H. Fagg, and M. A. Arbib. Dorsal premotor cortex and conditional movement selection: a PET functional mapping study. J. Neurophysiol. 79: 1092–1097, 1998. Positron emission tomog...

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Dorsal Premotor Cortex and Conditional Movement Selection: A PET Functional Mapping Study

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life Brain-machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI) Using an exoskeletal robot, the monkey's arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

https://www.jneurosci.org/content/jneuro/30/50/16777.full.pdf

Andrew H. Fagg

Papers

Modeling parietal-premotor interactions in primate control of grasping

Functional Anatomy of Pointing and Grasping in Humans

Wearable computers as packet transport mechanisms in highly-partitioned ad-hoc networks

Dorsal Premotor Cortex and Conditional Movement Selection: A PET Functional Mapping Study

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control