Information

What are the definitions of 'multi-channel coding' and 'opponent channel coding'?

What are the definitions of 'multi-channel coding' and 'opponent channel coding'?



We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I am looking for the definitions of

  • Multi-channel coding
  • Opponent-channel coding

And specifically in the context of visual adaptation. I have searched for information on the web and in books, but I am unable to find accurate definitions.


Short answer

  • Multi-channel coding in color vision refers to the different photoreceptors in the retina.
  • Opponent-channel coding refers to the opposing color pairs: the red-green and yellow-blue axes.

Background

I think you are referring to terminologies often used in color vision.

  • Multi-channel coding generally refers to different types of photoreceptors in the retina, namely rods and cones. In terms of color vision, it is the cones doing the work. In trichromatic species, such as humans, there are three types of cones in normally sighted people, namely red, green and blue cones. So humans have a 3-channel color system, the same as your computer screen you are looking at now, namely R, G, and B channels.
  • Opponent-channel coding refers to the opponent theory of color vision as proposed originally by Hering. It basically states that our RGB color system defines color space by 3 opponent processes, namely a black-white, a red-green and a blue-yellow opponent system (Fig. 1). Basically it is theorized that red opposes green, yellow opposes blue and white opposes black (and vice versa). This means that colors are mixed between these channels (e.g. a reddish blue equals purple), but that colors within channels are mutually exclusive (e.g. there exists no such thing as a yellowish blue).


Fig. 1. Hering model of color vivion. source: University of Calgary

Reference
- Gouras, Color Vision. In: Kolb et al (eds), Webvision. The Organization of the Retina and Visual System. Utah University


1 Introduction

The efficient coding hypothesis is an important proposal of how neural systems may represent (sensory) information (Barlow, 1961 Attneave, 1954 Linsker, 1988). Common formulations of efficient coding are based on the assumption that a neural system is adapted to the statistical structure of the environment in a way that the mutual information (Shannon & Weaver, 1949) between the stimulus variable and its neural representation (e.g., as reflected in the firing activity of a neural population) is maximized subject to certain resource constraints. However, the test of this prominent hypothesis is impeded by the fact that mutual information is analytically tractable only for simple coding problems (Laughlin, 1981 Atick, 1992). One way to work around this difficulty is to relate mutual information to Fisher information (Fisher, 1922). For many neural population coding models, Fisher information is relatively easy to compute and interpret with regard to neurophysiological parameters (e.g., neural response gain and dynamic range), as well as psychophysical behavior (e.g., discrimination threshold Seung & Sompolinsky, 1993 Seriès, Stocker, & Simoncelli, 2009).

In a seminal paper, Brunel and Nadal (1998) argued that Fisher information provides a lower bound on mutual information. This result has been widely applied in various studies aimed at testing the efficient coding hypothesis (Harper & McAlpine, 2004 McDonnell & Stocks, 2008 Wang, Stocker, & Lee, 2012 Ganguli & Simoncelli, 2010, 2014). These studies have derived efficient coding solutions by maximizing the proposed lower bound (in terms of Fisher information) rather than directly maximizing mutual information. This approach, however, can be problematic because recent theoretical and numerical analyses suggest that Fisher information can be an imprecise measure of coding accuracy (Bethge, Rotermund, & Pawelzik, 2002) and may actually represent an upper rather than a lower bound on mutual information (Yarrow, Challis, & Seriès, 2012). What is currently missing is a clear understanding of the conditions under which Fisher information serves as a (lower or upper) bound on mutual information and when this bound is reasonably tight (i.e., Fisher information provides a good proxy for mutual information).

In this letter, we revisit the formal link between Fisher and mutual information. We first reexamine the conditions for which the lower bound proposed by Brunel & Nadal (1998) holds. We show that the derivation of the bound is based on assumptions that make it automatically tight, thus defying the meaning of a bound. We then formally derive the relation between Fisher and mutual information in a standard input-output model under more general conditions. We discuss the possible interpretations of our derivation in terms of both upper and lower bounds on mutual information. We further derive the conditions under which Fisher information provides a good approximation of mutual information. Finally, we discuss the implications of our results in the context of efficient coding. Our results provide an important step toward a more detailed and rigorous understanding of Fisher information as a characteristic measure of neural codes and their efficiency.


Matching CPT Codes to Services

Your interest in these codes is usually related to your doctors' and insurance billings. CPT codes are copyrighted by the AMA.   The organization charges fees for the use of the codes and access to full listings, which means you won't find a comprehensive list online for free.

To make them more accessible to patients, the AMA provides a means for looking up the individual CPT codes you might encounter in medical paperwork. If you have paperwork that has a CPT code on it and you want to figure out what that code represents, you can do so in a number of ways:

  • Do a CPT code search on the American Medical Association website. You will have to register (for free) and you are limited to five searches per day. You can search by a CPT code or use a keyword to see what the associated CPT code for a service might be.
  • Contact your doctor's office and ask them to help you match CPT codes and services.
  • Contact your payer's billing personnel and ask them to help you.
  • Remember that some codes may be bundled but can be looked up in the same way.

What are the definitions of 'multi-channel coding' and 'opponent channel coding'? - Psychology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


The ethics of data collection

There are two main ethical issues about using observational data collection. The first is pretty obvious — do you have permission to collect and use the data? The debacle over Facebook and Cambridge Analytica and Europe’s GDPR requirements show the importance of ensuring that you have permission to process individual data, including observational data.

The second problem relates to information you might uncover and the actions you should take. This ethical problem is clearest in social and medical research. Two different treatments or plans are put into use – one group receive option A and the other group option B. However, if the results of the trial show that one of the treatments is less good (or actually harmful) then it may not be ethical to keep the trial going, which means the information may be compromised.

In a commercial situation, in many countries, if the analysis of observational data shows that people are on the wrong mobile phone plan, if they are paying too much for their energy, or if they could have bought a cheaper ticket, then it may be unethical not to intervene (indeed some governments/courts may also decide it is illegal mis-selling).

Observational research should be reviewed for its ethical implications – and the nature of what is ethical is likely to continuously evolve for the foreseeable future.


6 Control Experiments

In this section, we perform a series of targeted experiments to assess how well our results extend to different settings. These experiments are intended to improve our understanding of the conditions under which the various forms of opponency emerge, supporting a comprehensive discussion.

6.1 Random Weights

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of our models with gaussian weights (mean and variance from filters of the same depth in a reference pretrained model with N B N = 32 and D V V S = 4 ⁠ ) as a function of bottleneck width. Some opponency is explained by simple statistics of the filters. Functional organization emerges only as a result of training.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of our models with gaussian weights (mean and variance from filters of the same depth in a reference pretrained model with N B N = 32 and D V V S = 4 ⁠ ) as a function of bottleneck width. Some opponency is explained by simple statistics of the filters. Functional organization emerges only as a result of training.

6.2 Greyscale

(a) Distribution of spatially opponent, nonopponent, and unresponsive cells in different layers of our model as a function of bottleneck width, for models trained with grayscale images showing that the known spatial opponency from Lindsey et al. (2019) is detected by our method. (b) Distribution of excitatory and inhibitory hues for cells in different layers of networks trained on images with distorted color (hue rotation of 90 ∘ ⁠ ). The most prevalent excitatory and inhibitory colors are aligned with the RGB extremes closest to a 90 ∘ rotation of the peaks in Figure 7.

(a) Distribution of spatially opponent, nonopponent, and unresponsive cells in different layers of our model as a function of bottleneck width, for models trained with grayscale images showing that the known spatial opponency from Lindsey et al. (2019) is detected by our method. (b) Distribution of excitatory and inhibitory hues for cells in different layers of networks trained on images with distorted color (hue rotation of 90 ∘ ⁠ ). The most prevalent excitatory and inhibitory colors are aligned with the RGB extremes closest to a 90 ∘ rotation of the peaks in Figure 7.

6.3 Distorted Color

To further explore the idea that the opponency in our networks derives from the statistics of the data, we trained a batch of models on images with distorted color. Specifically, we convert the images into HSV space and offset the hue channel by 90 ∘ ⁠ , before converting back into RGB and forwarding to the network. Our interest here is not in whether opponency emerges, but in the effect this distortion has on it. Figure 11b shows the distribution of excitatory and inhibitory colors in networks trained with distorted inputs. Here, the most prevalent excitatory and inhibitory colors are aligned with the RGB extremes closest to a 90 ∘ rotation of the peaks in Figure 7. This is consistent with our observation that the vast majority of color opponent neurons are channel opponent. In contrast, the additional excitation peak has been rotated by exactly 90 ∘ from orange/red to green. This demonstrates that the cells that are excited by specific hues emerge as a result of the statistics of the data, not of the input color space.

6.4 CIELAB Space

(a) Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on images in LAB space as a function of bottleneck width, showing that functional organization is not unique to RGB. (b) Excitatory/inhibitory hues in LAB space for random and trained networks. Training increases prevalence of blue/green and excitation by orange/red and cyan/blue.

(a) Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on images in LAB space as a function of bottleneck width, showing that functional organization is not unique to RGB. (b) Excitatory/inhibitory hues in LAB space for random and trained networks. Training increases prevalence of blue/green and excitation by orange/red and cyan/blue.

6.5 Street View House Numbers

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on Street View House Numbers (SVHN) (Netzer et al., 2011) as a function of bottleneck width. Spatial opponency is present, with a similar distribution to the networks trained on CIFAR-10. Color opponency is generally lower, increasing only slightly for networks with narrow bottlenecks.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on Street View House Numbers (SVHN) (Netzer et al., 2011) as a function of bottleneck width. Spatial opponency is present, with a similar distribution to the networks trained on CIFAR-10. Color opponency is generally lower, increasing only slightly for networks with narrow bottlenecks.

6.6 ImageNet

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on ImageNet (Russakovsky et al., 2015) as a function of bottleneck width, showing how our findings transfer to a higher resolution setting. There is an increase in opponency for narrow bottlenecks, which decays rapidly. Emergent organization is observed only partially in the networks with the tightest bottlenecks.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on ImageNet (Russakovsky et al., 2015) as a function of bottleneck width, showing how our findings transfer to a higher resolution setting. There is an increase in opponency for narrow bottlenecks, which decays rapidly. Emergent organization is observed only partially in the networks with the tightest bottlenecks.

6.7 Intel Scene Classification

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on the Intel scene classification challenge data set (Intel, 2018) as a function of bottleneck width. With fewer classes (six in this case), the number of opponent cells is much higher. The distribution of opponent cells in Retina 2 bares strong similarity with the results from CIFAR-10. This does not extend to the ventral layers, which have near-identical cell distributions.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on the Intel scene classification challenge data set (Intel, 2018) as a function of bottleneck width. With fewer classes (six in this case), the number of opponent cells is much higher. The distribution of opponent cells in Retina 2 bares strong similarity with the results from CIFAR-10. This does not extend to the ventral layers, which have near-identical cell distributions.

6.8 Classifying Mosaics

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on mosaic images as a function of bottleneck width with example mosaic images. These results show that when the spatial structure of the input is removed, some spatial opponency, particularly in Retina 2, is removed also. Color opponency is similarly affected, suggesting a complex dependence between spatial and color processing.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on mosaic images as a function of bottleneck width with example mosaic images. These results show that when the spatial structure of the input is removed, some spatial opponency, particularly in Retina 2, is removed also. Color opponency is similarly affected, suggesting a complex dependence between spatial and color processing.

6.9 Shuffled Color Channels

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on images with shuffled color channels as a function of bottleneck width with example shuffled images. When consistent color information is removed, most color opponency is also removed. Spatial opponency remains.

Distribution of spatially and color opponent, nonopponent, and unresponsive cells in different layers of models trained on images with shuffled color channels as a function of bottleneck width with example shuffled images. When consistent color information is removed, most color opponency is also removed. Spatial opponency remains.


Uses of Content Analysis

Content analysis can be applied to analyze any piece of content that is written or verbal. Content analysis is involved in a variety of fields such as politics, human behavior, marketing, literature, health, psychology, and much more.

Content analysis is also displaying a close relation between the linguistic factors and psychological aspects, thereby leading to the development of artificial intelligence.

Examples of the Uses of Content Analysis

For example, a brand can discover emerging trends with the use of content analysis. Content from online conversations is obtained from various sources such as news, feedbacks, blogs, tickets, online discussion, social media, and reviews.

Once the data is available, the data has to be sliced and diced using algorithms and proven mathematical models. Topics, relationships, and tone intensities are analyzed to identify patterns, correlations, and inferences at multiple levels.

As content analysis deals with text, numbers, comments, statistics, and much more measurable facts, it is used for forecasting, trend analysis, and drawing logical strategies. It is used widely to remove the ambiguity factor and to get rid of opinions and guesswork.

Content that you gather is subjective, and hence using it to analyze and define it more quantitatively helps to arrive at decisions. Therefore, content analysis is essential. It has the following benefits:

  • Establishes proof of the reliability of the data
  • Allows both quantitative and qualitative analysis
  • Offers valuable insights into history by analyzing information
  • Provides analytical insight into human thought and language
  • To Identify the trends and intentions of an individual or a group
  • Understands both human behavior and the use of language, and their relationship

The use of content analysis depends on how you use it. For example, when you release an article on your blog page, content analysis will help to understand the further journey.

How many people read it, how many liked it, how many shared it, how many people visited your website after reading your article, and how much sales increased post releasing this article.

When you look at the content analysis reports, you can identify several areas that are doing well and the specific regions where you will have to devote attention to its improvement. All this would not have happened without content analysis.


Frequently Asked Questions

What are the 'games' being played in game theory?

It is called game theory since the theory tries to understand the strategic actions of two or more "players" in a given situation containing set rules and outcomes. While used in a number of disciplines, game theory is most notably used as a tool within the study of business and economics. The "games" may thus involve how two competitor firms will react to price cuts by the other, if a firm should acquire another, or how traders in a stock market may react to price changes.

In theoretic terms, these games may be categorized as similar to prisoner's dilemmas, the dictator game, the hawk-and-dove, and Bach or Stravinsky, among several other variations.

What are some of the assumptions about these games?

Like many economic models, game theory also contains a set of strict assumptions that must hold for the theory to make good predictions in practice. First, all players are utility-maximizing rational actors that have full information about the game, the rules, and the consequences. Players are not allowed to communicate or interact with one another. Possible outcomes are not only known in advance but also cannot be changed. The number of players in a game can theoretically be infinite, but most games will be put into the context of only two players.

What is a Nash equilibrium?

The Nash equilibrium is an important concept referring to a stable state in a game where no player can gain an advantage by unilaterally changing a strategy, assuming the other participants also do not change their strategies. The Nash equilibrium provides the solution concept in a non-cooperative (adversarial) game. It is named after John Nash who received the Nobel in 1994 for his work

Who came up with game theory?

Game theory is largely attributed to the work of mathematician John von Neumann and economist Oskar Morgenstern in the 1940s, and was developed extensively by many other researchers and scholars in the 1950s. It remains an area of active research and applied science to this day.


Healthcare Common Procedural Coding System (HCPCS)

HCPCS is divided into two subsystems: Level I (comprised of the CPT code set) and Level II. Level II of the HCPCS is a standardized coding system (a single alphabetical letter followed by 4 numeric digits) that is primarily used to identify products, supplies and services not included in the CPT code set. HCPCS Level II codes include ambulance services and durable medical equipment, prosthetics, orthotics and supplies (DMEPOS) when used outside a physician’s office.

G Codes (home sleep apnea testing)

The G codes (G0398, G0399 and G0400), which describe home sleep apnea testing (HSAT) services, were added to the Healthcare Common Procedure Coding System (HCPCS) Level II in 2008. Some insurers accept the G codes while others accept the CPT® codes for HSATs (95800, 95801 and 95806). An HSAT provider will need to contact each insurer they work with to identify which codes can be reported.

Code Description
G0398 Home sleep study test (HST) with type II portable monitor unattended minimum of 7 channels: EEG, EOG, EMG, ECG/heart rate, airflow, respiratory effort, and oxygen saturation
G0399 Home sleep test (HST) with type III portable monitor unattended minimum of 4 channels: 2 respiratory movement/airflow, 1 ECG/heart rate and 1 oxygen saturation
G0400 Home sleep test (HST) with type IV portable monitor unattended minimum of 3 channels

Durable Medical Equipment (DME) Sleep Medicine Codes

All E codes fall under the jurisdiction of the DME MAC unless otherwise noted.

Code Description
E0485 Oral device/appliance used to reduce upper airway collapsibility, adjustable or non-adjustable, prefabricated, includes fitting and adjustment
E0486 Oral device/appliance used to reduce upper airway collapsibility, adjustable or non-adjustable, custom fabricated, includes fitting and adjustment
E0601 Continuous positive airway pressure (CPAP) device
E0470 Respiratory assist device, bi-level pressure capability, without backup rate feature, used with noninvasive interface, e.g. nasal or facial mask (intermittent assist device with continuous positive airway pressure device)
E0471 Respiratory assist device, bi-level pressure capability, with back-up rate feature, used with noninvasive interface, e.g. nasal or facial mask (intermittent assist device with continuous positive airway pressure device)

Psychology/Psychiatry Codes

Psychiatric Diagnostic Procedures
Code Description
90791 Psychiatric diagnostic evaluation
90792 Psychiatric diagnostic evaluation with medical services
Psychotherapy
90832 Psychotherapy, 30 minutes with patient
90833 Psychotherapy, 30 minutes with patient when performed with an evaluation and management service (List separately in addition to the code for primary procedure)
90834 Psychotherapy, 45 minutes with patient
90836 Psychotherapy, 45 minutes with patient when performed with an evaluation and management service (List separately in addition to the code for primary procedure)
90837 Psychotherapy, 60 minutes with patient
90838 Psychotherapy, 60 minutes with patient when performed with an evaluation and management service (List separately in addition to the code for primary procedure)

Note: CPT Copyright 2021 American Medical Association. All rights reserved. CPT® is a registered trademark of the American Medical Association.


SMILE

Chris Nickson

Chris is an Intensivist and ECMO specialist at the Alfred ICU in Melbourne. He is also the Innovation Lead for the Australian Centre for Health Innovation at Alfred Health and Clinical Adjunct Associate Professor at Monash University. He is a co-founder of the Australia and New Zealand Clinician Educator Network (ANZCEN) and is the Lead for the ANZCEN Clinician Educator Incubator programme. He is on the Board of Directors for the Intensive Care Foundation and is a First Part Examiner for the College of Intensive Care Medicine. He is an internationally recognised Clinician Educator with a passion for helping clinicians learn and for improving the clinical performance of individuals and collectives.

After finishing his medical degree at the University of Auckland, he continued post-graduate training in New Zealand as well as Australia’s Northern Territory, Perth and Melbourne. He has completed fellowship training in both intensive care medicine and emergency medicine, as well as post-graduate training in biochemistry, clinical toxicology, clinical epidemiology, and health professional education.


Watch the video: Introduction to Channel Coding. Lecture 14. Information Theory u0026 Coding Technique. ITCCN (August 2022).