There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Our understanding of the cellular implementation of systems-level neural processes like action, thought and emotion has been limited by the availability of tools to interrogate specific classes of neural cells within intact, living brain tissue. Here we identify and develop an archaeal light-driven chloride pump (NpHR) from Natronomonas pharaonis for temporally precise optical inhibition of neural activity. NpHR allows either knockout of single action potentials, or sustained blockade of spiking. NpHR is compatible with ChR2, the previous optical excitation technology we have described, in that the two opposing probes operate at similar light powers but with well-separated action spectra. NpHR, like ChR2, functions in mammals without exogenous cofactors, and the two probes can be integrated with calcium imaging in mammalian brain tissue for bidirectional optical modulation and readout of neural activity. Likewise, NpHR and ChR2 can be targeted together to Caenorhabditis elegans muscle and cholinergic motor neurons to control locomotion bidirectionally. NpHR and ChR2 form a complete system for multimodal, high-speed, genetically targeted, all-optical interrogation of living neural circuits.

Multimodal fast optical interrogation of neural circuitry

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Thank you very much for downloading introduction to robust estimation and hypothesis testing. As you may know, people have search numerous times for their favorite books like this introduction to robust estimation and hypothesis testing, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop.

Introduction To Robust Estimation And Hypothesis Testing

The conventional mode of health delivery can be profiled as follows: a patient, if he does not feel good, will visit his physician and report his symptoms (e.g., headache, bellyache, and nausea, etc); the physician strives to make a few empirical hypotheses based on the patient’s complaints and medical history; in general, the physician has to further examine the patient’s vital signs (e.g., body temperature, blood pressure, and pulse, etc.) so as to confirm or reject his initial hypotheses; if the physician is confident enough on his hypotheses, the final diagnosis decision can be made and the according treatments are hereby delivered; if not, the physician will resort to various medical instruments (e.g., electrocardiography, ultrasonography, and computed tomography, etc) and laboratory tests (e.g., blood test, urinalysis, and immunological analysis, etc); during this procedure, the initial hypotheses may be updated at any time; to those complicated diseases, the physicians will propose a few tentative treatments and modify them in accordance with clinical observations. It is noteworthy that above procedure is triggered and driven by patients’ symptoms. But most symptoms are subjective, and are late manifestations of a disease or a group of diseases. In other words, they often incur expensive and painful clinical treatments. Furthermore, once the patients perceive malign symptoms, they may have missed the best opportunity of medical treatments. Take cardiovascular diseases (CVDs) for an example. Nowadays it is possible to treat or relieve nearly all CVDs by medical interventions, such as controlling blood pressure and blood cholesterol. In the case of refractory or congenital CVDs, various sophisticated clinical instruments and operations, including pacemakers, prosthetic valves, coronary artery bypass and even whole heart transplantation, have been developed and practiced too. As a consequence, the World Health Organization (WHO) reports “... at least 80% of premature deaths from heart disease and stroke could be avoided

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Computerized Interpretation of Cardiovascular Physiological Signals 1

Emotion can be regarded as a special brain status and it can be captured by Electroencephalography (EEG) via deploying number of channels all over the scalp. To find out the characteristics of emotional responses, the scalp level connectivity is one of the interests that considered. Nevertheless, to distinguish the significant EEG channels, which can imply the special regions at scalp level, has not yet been studied well. Therefore, in this work, we aim to identify the representative channels towards different emotional status by considering the connectivity derived from the values of coherence and correlation. In this purpose, DEAP, an online database has been investigated. Certainly, the emotional status can be evaluated on the valence-arousal (V-A) space and here, four groups contain HVHA, HVLA, LVHA, and LVLA have been studied. The results from 16 subjects show that the EEG channels AF3, F7, P7 and O1 have higher percentages in term of the connectivity and they could be considered as the representative channels in this case. Such findings could be useful to understand the scalp responses in emotional status.

Scalp Level Connectivity for Representative Channels in Emotional Status

Spiking neural networks (SNNs) are more energy- and resource-efficient than artificial neural networks (ANNs). However, supervised SNN learning is a challenging task due to non-differentiability of spikes and computation of complex terms. Moreover, the design of SNN learning engines is not an easy task due to limited hardware resources and tight energy constraints. In this article, a novel hardware-efficient SNN back-propagation scheme that offers fast convergence is proposed. The learning scheme does not require any complex operation such as error normalization and weight-threshold balancing, and can achieve an accuracy of around 97.5% on MNIST dataset using only 158,800 synapses. The multiplier-less inference engine trained using the proposed hard sigmoid SNN training (HaSiST) scheme can operate at a frequency of 135 MHz and consumes only 1.03 slice registers per synapse, 2.8 slice look-up tables, and can infer about 0.03[Formula: see text] features in a second, equivalent to 9.44 giga synaptic operations per second (GSOPS). The article also presents a high-speed, cost-efficient SNN training engine that consumes only 2.63 slice registers per synapse, 37.84 slice look-up tables per synapse, and can operate at a maximum computational frequency of around 50 MHz on a Virtex 6 FPGA. 

/pdf/a-low-cost-neuromorphic-learning-engine-based-on-a-high-3smlmc3v.pdf

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm.

Human alpha oscillation (7–13 Hz) has been extensively studied over the years for its connection with cognition. The individual alpha frequency (IAF), defined as the frequency that provides the highest power in the alpha band, shows a positive correlation with cognitive processes. The modulation of alpha activities has been accomplished through various approaches aimed at improving cognitive performance. However, very few studies focused on the direct modulation of IAF by shifting the peak frequency, and the understanding of IAF modulation remains highly limited. In this study, IAFs of healthy young adults were up-regulated through short-term neurofeedback training using haptic feedback. The results suggest that IAFs have good trainability and are up-regulated, also that IAFs are correlated with the enhanced cognitive performance in mental rotation and n-back tests compared to sham-neurofeedback control. This study demonstrates the feasibility of self-regulating IAF for cognition enhancement and provides potential therapeutic benefits for cognitive-impaired patients.

Mang I Vai

Papers

Computerized Interpretation of Cardiovascular Physiological Signals 1

Scalp Level Connectivity for Representative Channels in Emotional Status

A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm.

Modulating Individual Alpha Frequency through Short-Term Neurofeedback for Cognitive Enhancement in Healthy Young Adults

A low-cost, high-throughput neuromorphic computer for online SNN learning