scholarly journals What the odor is not: Estimation by elimination

2019 ◽  
Author(s):  
Vijay Singh ◽  
Martin Tchernookov ◽  
Vijay Balasubramanian
Keyword(s):  
Olfactory Receptor ◽  
Olfactory System ◽  
Accurate Determination ◽  
Piriform Cortex ◽  
Experimental Tests ◽  
Response Functions ◽  
Vast Number ◽  
Small Factor ◽  
The Brain

AbstractThe olfactory system uses a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose that the brain decodes this distributed representation by exploiting a statistical fact: receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not, rather than what the odor is. For biologically realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. A neural network realization of our algorithm resembles circuit architecture in the piriform cortex. We propose several experimental tests and show an excellent match to data from olfactory discrimination experiments. Our theory also suggests why animals from the fly to the elephant have a few hundred olfactory receptor types, give or take a small factor.

Accurate determination of total CO2 in the brain

1963 ◽  
Vol 19 (6) ◽  
pp. 312-313 ◽  
Author(s):  
U. Pontén ◽  
B. K. Siesjö
Keyword(s):  
Accurate Determination ◽  
The Brain

scholarly journals Coding of odor stimulus features among secondary olfactory structures

2015 ◽  
Vol 114 (1) ◽  
pp. 736-745 ◽  
Author(s):  
Christina Z. Xia ◽  
Stacey Adjei ◽  
Daniel W. Wesson
Keyword(s):  
Olfactory System ◽  
Piriform Cortex ◽  
Odor Stimulus ◽  
Odor Perception ◽  
Odor Coding ◽  
Network Function ◽  
Complex Functions ◽  
Odor Exposure ◽  
Stimulus Features ◽  
The Brain

Sensory systems must represent stimuli in manners dependent upon a wealth of factors, including stimulus intensity and duration. One way the brain might handle these complex functions is to assign the tasks throughout distributed nodes, each contributing to information processing. We sought to explore this important aspect of sensory network function in the mammalian olfactory system, wherein the intensity and duration of odor exposure are critical contributors to odor perception. This is a quintessential model for exploring processing schemes given the distribution of odor information by olfactory bulb mitral and tufted cells into several anatomically distinct secondary processing stages, including the piriform cortex (PCX) and olfactory tubercle (OT), whose unique contributions to odor coding are unresolved. We explored the coding of PCX and OT neuron responses to odor intensity and duration. We found that both structures similarly partake in representing descending intensities of odors by reduced recruitment and modulation of neurons. Additionally, while neurons in the OT adapt to odor exposure, they display reduced capacity to adapt to either repeated presentations of odor or a single prolonged odor presentation compared with neurons in the PCX. These results provide insights into manners whereby secondary olfactory structures may, at least in some cases, uniquely represent stimulus features.

A quantitative approach to inner shell losses

1978 ◽  
Vol 36 (1) ◽  
pp. 526-527 ◽  
Author(s):  
R.D. Leapman ◽  
P. Rez ◽  
D.F. Mayers
Keyword(s):  
Energy Transfer ◽  
Cross Section ◽  
Cross Sections ◽  
Accurate Determination ◽  
Background Intensity ◽  
Core Excitation ◽  
Quantitative Basis ◽  
Cross Section Differential ◽  
Bound Electrons

Microanalysis by EELS has been developing rapidly and though the general form of the spectrum is now understood there is a need to put the technique on a more quantitative basis (1,2). Certain aspects important for microanalysis include: (i) accurate determination of the partial cross sections, σx(α,ΔE) for core excitation when scattering lies inside collection angle a and energy range ΔE above the edge, (ii) behavior of the background intensity due to excitation of less strongly bound electrons, necessary for extrapolation beneath the signal of interest, (iii) departures from the simple hydrogenic K-edge seen in L and M losses, effecting σx and complicating microanalysis. Such problems might be approached empirically but here we describe how computation can elucidate the spectrum shape.The inelastic cross section differential with respect to energy transfer E and momentum transfer q for electrons of energy E0 and velocity v can be written as

Fast calibration of CBED patterns for quantitative analysis

1993 ◽  
Vol 51 ◽  
pp. 674-675
Author(s):  
M.A. Gribelyuk ◽  
M. Rühle
Keyword(s):  
Reciprocal Lattice ◽  
Accurate Determination ◽  
Incident Beam ◽  
Crystal Thickness ◽  
Structure Model ◽  
Beam Direction ◽  
Transmitted Beam ◽  
Reciprocal Vector ◽  
On Line

A new method is suggested for the accurate determination of the incident beam direction K, crystal thickness t and the coordinates of the basic reciprocal lattice vectors V1 and V2 (Fig. 1) of the ZOLZ plans in pixels of the digitized 2-D CBED pattern. For a given structure model and some estimated values Vest and Kest of some point O in the CBED pattern a set of line scans AkBk is chosen so that all the scans are located within CBED disks.The points on line scans AkBk are conjugate to those on A0B0 since they are shifted by the reciprocal vector gk with respect to each other. As many conjugate scans are considered as CBED disks fall into the energy filtered region of the experimental pattern. Electron intensities of the transmitted beam I0 and diffracted beams Igk for all points on conjugate scans are found as a function of crystal thickness t on the basis of the full dynamical calculation.

Computer-Assisted High-Re Solution TEM

1990 ◽  
Vol 48 (1) ◽  
pp. 162-163
Author(s):  
F.A. Ponce ◽  
H. Hikashi
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Determination ◽  
Computer Software ◽  
Video Camera ◽  
Image Contrast ◽  
Objective Lens ◽  
Computer Assisted ◽  
Optical Parameters ◽  
Astigmatism Correction

The determination of the atomic positions from HRTEM micrographs is only possible if the optical parameters are known to a certain accuracy, and reliable through-focus series are available to match the experimental images with calculated images of possible atomic models. The main limitation in interpreting images at the atomic level is the knowledge of the optical parameters such as beam alignment, astigmatism correction and defocus value. Under ordinary conditions, the uncertainty in these values is sufficiently large to prevent the accurate determination of the atomic positions. Therefore, in order to achieve the resolution power of the microscope (under 0.2nm) it is necessary to take extraordinary measures. The use of on line computers has been proposed [e.g.: 2-5] and used with certain amount of success.We have built a system that can perform operations in the range of one frame stored and analyzed per second. A schematic diagram of the system is shown in figure 1. A JEOL 4000EX microscope equipped with an external computer interface is directly linked to a SUN-3 computer. All electrical parameters in the microscope can be changed via this interface by the use of a set of commands. The image is received from a video camera. A commercial image processor improves the signal-to-noise ratio by recursively averaging with a time constant, usually set at 0.25 sec. The computer software is based on a multi-window system and is entirely mouse-driven. All operations can be performed by clicking the mouse on the appropiate windows and buttons. This capability leads to extreme friendliness, ease of operation, and high operator speeds. Image analysis can be done in various ways. Here, we have measured the image contrast and used it to optimize certain parameters. The system is designed to have instant access to: (a) x- and y- alignment coils, (b) x- and y- astigmatism correction coils, and (c) objective lens current. The algorithm is shown in figure 2. Figure 3 shows an example taken from a thin CdTe crystal. The image contrast is displayed for changing objective lens current (defocus value). The display is calibrated in angstroms. Images are stored on the disk and are accessible by clicking the data points in the graph. Some of the frame-store images are displayed in Fig. 4.

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