Olfactory bulbThe olfactory bulb (Latin: bulbus olfactorius) is a neural structure of the vertebrate forebrain involved in olfaction, the sense of smell. It sends olfactory information to be further processed in the amygdala, the orbitofrontal cortex (OFC) and the hippocampus where it plays a role in emotion, memory and learning. The bulb is divided into two distinct structures: the main olfactory bulb and the accessory olfactory bulb.
WaveletA wavelet is a wave-like oscillation with an amplitude that begins at zero, increases or decreases, and then returns to zero one or more times. Wavelets are termed a "brief oscillation". A taxonomy of wavelets has been established, based on the number and direction of its pulses. Wavelets are imbued with specific properties that make them useful for signal processing. For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly one tenth of a second.
Discrete wavelet transformIn numerical analysis and functional analysis, a discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information (location in time). Haar wavelet The first DWT was invented by Hungarian mathematician Alfréd Haar. For an input represented by a list of numbers, the Haar wavelet transform may be considered to pair up input values, storing the difference and passing the sum.
Statistical significanceIn statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is statistically significant, by the standards of the study, when .
Statistical inferenceStatistical inference is the process of using data analysis to infer properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.
Sense of smellThe sense of smell, or olfaction, is the special sense through which smells (or odors) are perceived. The sense of smell has many functions, including detecting desirable foods, hazards, and pheromones, and plays a role in taste. In humans, it occurs when an odor binds to a receptor within the nasal cavity, transmitting a signal through the olfactory system. Glomeruli aggregate signals from these receptors and transmit them to the olfactory bulb, where the sensory input will start to interact with parts of the brain responsible for smell identification, memory, and emotion.
Wavelet transformIn mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform. A function is called an orthonormal wavelet if it can be used to define a Hilbert basis, that is a complete orthonormal system, for the Hilbert space of square integrable functions.
Body odorBody odor or body odour (BO) is present in all animals and its intensity can be influenced by many factors (behavioral patterns, survival strategies). Body odor has a strong genetic basis, but can also be strongly influenced by various factors, such as gender, diet, health, and medication. The body odor of human males plays an important role in human sexual attraction, as a powerful indicator of MHC/HLA heterozygosity.
OdorAn odor (American English) or odour (Commonwealth English; see spelling differences) is caused by one or more volatilized chemical compounds that are generally found in low concentrations that humans and many animals can perceive via their sense of smell. An odor is also called a "smell" or a "scent", which can refer to either a pleasant or an unpleasant odor. While "odor" and "smell" can refer to pleasant and unpleasant odors, the terms "scent", "aroma", and "fragrance" are usually reserved for pleasant-smelling odors and are frequently used in the food and cosmetic industry to describe floral scents or to refer to perfumes.
Statistical hypothesis testingA statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .
Olfactory nerveThe olfactory nerve, also known as the first cranial nerve, cranial nerve I, or simply CN I, is a cranial nerve that contains sensory nerve fibers relating to the sense of smell. The afferent nerve fibers of the olfactory receptor neurons transmit nerve impulses about odors to the central nervous system (olfaction). Derived from the embryonic nasal placode, the olfactory nerve is somewhat unusual among cranial nerves because it is capable of some regeneration if damaged.
Olfactory systemThe olfactory system, or sense of smell, is the sensory system used for smelling (olfaction). Olfaction is one of the special senses, that have directly associated specific organs. Most mammals and reptiles have a main olfactory system and an accessory olfactory system. The main olfactory system detects airborne substances, while the accessory system senses fluid-phase stimuli. The senses of smell and taste (gustatory system) are often referred to together as the chemosensory system, because they both give the brain information about the chemical composition of objects through a process called transduction.
Olfactory tubercleThe olfactory tubercle (OT), also known as the tuberculum olfactorium, is a multi-sensory processing center that is contained within the olfactory cortex and ventral striatum and plays a role in reward cognition. The OT has also been shown to play a role in locomotor and attentional behaviors, particularly in relation to social and sensory responsiveness, and it may be necessary for behavioral flexibility.
Olfactory epitheliumThe olfactory epithelium is a specialized epithelial tissue inside the nasal cavity that is involved in smell. In humans, it measures and lies on the roof of the nasal cavity about above and behind the nostrils. The olfactory epithelium is the part of the olfactory system directly responsible for detecting odors. Olfactory epithelium consists of four distinct cell types: Olfactory sensory neurons Supporting cells Basal cells Brush cells Olfactory receptor neuron The olfactory receptor neurons are sensory neurons of the olfactory epithelium.
Olfactory receptor neuronAn olfactory receptor neuron (ORN), also called an olfactory sensory neuron (OSN), is a sensory neuron within the olfactory system. Humans have between 10 and 20 million olfactory receptor neurons (ORNs). In vertebrates, ORNs are bipolar neurons with dendrites facing the external surface of the cribriform plate with axons that pass through the cribriform foramina with terminal end at olfactory bulbs. The ORNs are located in the olfactory epithelium in the nasal cavity.
Linear modelIn statistics, the term linear model is used in different ways according to the context. The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. However, the term is also used in time series analysis with a different meaning. In each case, the designation "linear" is used to identify a subclass of models for which substantial reduction in the complexity of the related statistical theory is possible.
Generalized linear modelIn statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.
Olfactory ensheathing cellOlfactory ensheathing cells (OECs), also known as olfactory ensheathing glia or olfactory ensheathing glial cells, are a type of macroglia (radial glia) found in the nervous system. They are also known as olfactory Schwann cells, because they ensheath the non-myelinated axons of olfactory neurons in a similar way to which Schwann cells ensheath non-myelinated peripheral neurons. They also share the property of assisting axonal regeneration. OECs are capable of phagocytosing axonal debris in vivo, and in vitro they phagocytose bacteria.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Power of a testIn statistics, the power of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis () when a specific alternative hypothesis () is true. It is commonly denoted by , and represents the chances of a true positive detection conditional on the actual existence of an effect to detect. Statistical power ranges from 0 to 1, and as the power of a test increases, the probability of making a type II error by wrongly failing to reject the null hypothesis decreases.
