Invariant odor recognition with ON–OFF neural ensembles

Invariant stimulus recognition is a challenging pattern-recognition problem that must be dealt with by all sensory systems. Since neural responses evoked by a stimulus are perturbed in a multitude of ways, how can this computational capability be achieved? We examine this issue in the locust olfactory system. We find that locusts trained in an appetitive-conditioning assay robustly recognize the trained odorant independent of variations in stimulus durations, dynamics, or history, or changes in background and ambient conditions. However, individual- and population-level neural responses vary unpredictably with many of these variations. Our results indicate that linear statistical decoding schemes, which assign positive weights to ON neurons and negative weights to OFF neurons, resolve this apparent confound between neural variability and behavioral stability. Furthermore, simplification of the decoder using only ternary weights ({+1, 0, −1}) (i.e., an “ON-minus-OFF” approach) does not compromise performance, thereby striking a fine balance between simplicity and robustness.

Rapid Prediction of Seismic Incident Angle's Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

The angle of seismic excitation is a significant factor of the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses using in each one of them different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerograms for the evaluation of seismic response. Thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on Multilayered Feedforward Perceptron Neural Networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class which arises from the application of the seismic motion along the buildings’ structural axes. To this end, the problem is expressed and solved as Pattern Recognition problem. As inputs of networks the ratios of seismic parameters’ values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.

Nonparametric pattern recognition algorithm for testing a hypothesis of the independence of random variables

A new method for testing a hypothesis of the independence of multidimensional random variables is proposed. The technique under consideration is based on the use of a nonparametric pattern recognition algorithm that meets a maximum likelihood criterion. In contrast to the traditional formulation of the pattern recognition problem, there is no a priori training sample. The initial information is represented by statistical data, which are made up of the values of a multivariate random variable. The distribution laws of random variables in the classes are estimated according to the initial statistical data for the conditions of their dependence and independence. When selecting optimal bandwidths for nonparametric kernel-type probability density estimates, the minimum standard deviation is used as a criterion. Estimates of the probability of pattern recognition error in the classes are calculated. Based on the minimum value of the estimates of the probabilities of pattern recognition errors, a decision is made on the independence or dependence of the random variables. The technique developed is used in the spectral analysis of remote sensing data.

Comparison of Feature Extraction Techniques for Pattern Classification

Pattern recognition is a challenging task in research field for the last few decades. Many researchers have worked on areas such as computer vision, speech recognition, document classification, and computational biology to tackle complex research problems. In this article, a pattern recognition problem for handwritten Malayalam character is presented. This system goes through two different stages of HCR namely, feature extraction and classification. Three feature extraction techniques – wavelet transform, zoning, division point – are used in this study. Among these, division is point is able to show best discriminative power using SVM classifier. All the experiments are conducted on size normalized and binarized images of isolated Malayalam characters.

Vive la difference! Celebrating and supporting autistic psychiatrists with autistic doctors international

AimsWe aim to raise awareness of the existence and value of autistic doctors in psychiatry and to also signpost psychiatrists who are or suspect they might be autistic towards peer support.MethodAutism refers to a lifelong difference in how people communicate and interact with the world. These differences lead to strengths and challenges with individual profiles which include special interests, hyper-focus, and often sensory differences and anxiety. Autism has an estimated prevalence of 1-2%, which is likely an underestimate. It was noted that there was little in the way of advocacy for autistic doctors around the world. Anecdotal evidence also suggested possible issues of misunderstanding and stigmatisation of autistic doctors. As such, there was a need to tackle this to promote positive change. MD founded the group Autistic Doctors International (ADI) in 2019 to foster camaraderie, advocacy and support. ADI has flourished with 250+ members currently. In a recent member poll, 24 of 180 respondents identified themselves as psychiatrists – second only to general practice (n = 54). Several other consultant psychiatrists are known to self-identify as autistic but have not formally joined due to the fear of disclosure. The group has additionally supported multiple doctors to tackle prejudice and discrimination in the workplace / training environment. It has also brought together autistic doctors with academic interests and has generated multiple academic outputs in the form of publications, research grants and conference posters/papers regarding autism.ResultPsychiatrists, and doctors in general, are a self-selecting group for many autistic strengths such as hyper-focus, curiosity, self-motivation, a desire to study social communication, attention to detail, pattern recognition, problem solving and empathy, which, contrary to prevailing stereotypes, can be marked in autism. The increasing numbers of doctors joining ADI supports the assumption that autistic individuals are safe and effective clinicians. It is worth noting that many members are not ‘doctors in difficulty’. Those who have been able to achieve suitable accommodations, often without realising why they were needed, have flourished. Such accommodations and outcomes are in line with the neurodiversity movement, which promotes a view of autism as difference, rather than pure disability or disorder. This aims to challenge stereotypes and the tragedy narrative surrounding autism.ConclusionAutism awareness is increasing amongst doctors but more open discussion is still needed in order to facilitate appropriate peer and workplace support. This is likely to improve mental wellbeing and resilience for autistic psychiatrists.

The Current Status of EMG Signals on Kinematics Needed for Precise Online Myoelectric Control and the Development Direction

Widely accepted idea is the prosthetic control problem could be regarded as the pattern recognition problem. The prosthetic control means there are several differences such as distinguishable electric signals between different activation of muscle. However, this conventional method could not provide proper control of the artificial limbs. Kinematics behavior is continuous and needs the coordination of multiple physiological degrees of freedom (DOF) among various joints. Currently, a huge challenge is achieving precise, coherent and elegant coordination protheses which needs many DOFs to rehabilitation of patients with limb deficiency. This article analyzed the principles of control of bionic limbs from the aspect of EMG and traditional pattern recognition. According to the research results, the following conclusions can be given. Since the quantum amplitudes are complex numbers generally, different parameter should be included and analyzed together during the quantum information processing. Besides, the quantum control scheme could be combined with the classic one. What is more, other sensor modes should be applied for robust control instead of the EMG signal only.

Analytical method for selection an informative set of features with limited resources in the pattern recognition problem

Feature selection is one of the most important issues in Data Mining and Pattern Recognition. Correctly selected features or a set of features in the final report determines the success of further work, in particular, the solution of the classification and forecasting problem. This work is devoted to the development and study of an analytical method for determining informative attribute sets (IAS) taking into account the resource for criteria based on the use of the scattering measure of classified objects. The areas of existence of the solution are determined. Statements and properties are proved for the Fisher type informativeness criterion, using which the proposed analytical method for determining IAS guarantees the optimality of results in the sense of maximizing the selected functional. The relevance of choosing this type of informativeness criterion is substantiated. The universality of the method with respect to the type of features is shown. An algorithm for implementing this method is presented. In addition, the paper discussed the dynamics of the growth of information in the world, problems associated with big data, as well as problems and tasks of data preprocessing. The relevance of reducing the dimension of the attribute space for the implementation of data processing and visualization without unnecessary difficulties is substantiated. The disadvantages of existing methods and algorithms for choosing an informative set of attributes are shown.