Nat Commun︱Xing Dajun's research group reveals the brain mechanism of brightness information processing: the visual system's strategy for encoding the brightness of object surfaces

Written by Yang Yi, edited by Zhong Luyan and Wang Sizhen It is well known that our perception of the brightness of an object depends not only on the surface brightness of the object, but also on the boundary contrast of the object (the difference in brightness between the surface of the object and the surrounding background)
.

The simple question of how the visual cortex processes the surface brightness of objects seems to have been adequately answered in textbooks; in fact, it is not
.

 According to traditional textbooks, before visual information enters the cerebral cortex, neurons in the retina and lateral geniculate nucleus (LGN) have been greatly weakened by the receptive field structure such as center-peripheral antagonism [1].
neural responses directly evoked by brightness
.

It is generally believed that neurons in the primary visual cortex (V1), which receive input from the lateral geniculate body, respond strongly to boundary contrast and poorly to uniform/low spatial frequency visual input [2]
.

However, in recent years in the study of electrophysiology and functional magnetic resonance imaging, researchers have successively found that V1 neurons have a strong response to the surface brightness of objects [3-5]
.

 We propose two different hypotheses about how the surface brightness response observed in V1 is formed: a fill-in mechanism and a feed-forward mechanism
.

Filling-in hypotheses believe that the surface brightness response in V1 is formed based on boundary responses [6, 7]: V1 neurons whose receptive fields are located at the boundary are activated first, and then through horizontal or feedback links between neurons, The signal is transmitted to neurons whose receptive fields are located on the surface of the object
.

Feedforward hypotheses suggest that the surface brightness response in V1 is formed independently of the boundary response [4, 8, 9]: the response elicited by uniform brightness mainly comes from the feedforward drive of the LGN
.

Which of the two hypotheses is more plausible remains unclear
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 On January 12, 2022, Professor Xing Dajun's research group from the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University published a paper entitled "Coding strategy for surface luminance switches in the primary visual cortex of the awake" in Nature Communications.
monkey", which revealed the process and computational principles of the encoding of luminance information in the primary visual cortex (V1) of macaque monkeys
.

The first author is doctoral student Yang Yi, and the corresponding author is Professor Xing Dajun
.

This paper finds that the neural response directly activated by surface brightness gradually weakens during the process of signal transmission across layers; the brightness information undergoes a transition from surface encoding to boundary encoding
.

The paper further highlights the important role of extensive cortical inhibition in the output layer in mediating such strategic shifts
.

 In order to understand how luminance information is processed in V1, the authors of the paper measured group responses elicited by visual stimuli with uniform surface luminance in macaque V1, and for the first time fully described the cross-layer transmission of surface luminance information in V1 (Fig.
1)
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The authors found that the surface and boundary of visual stimuli elicited distinct differences in responses in V1
.

In the V1 input layer (L4C), neurons responded strongly to both surfaces and boundaries, but in the V1 output layer (L2/3), surface responses were significantly attenuated
.

This weakening becomes stronger and stronger along the direction of transmission across layers, so that V1 outputs mainly boundary responses to the next-level brain regions
.

Figure 1 Cross-layer representation of surface brightness information in V1 (Source: Yang Yi, et al.
, Nat Commun, 2022) In the further analysis of the dynamic response of neurons, the author observed a surface response similar to the "filling mechanism" formed
.

The implementation of the “filling mechanism” requires long-range links between neurons as a structural basis, such links are common in the output layer and rare in the input layer [10]
.

But what is interesting is that the "filling"-like reaction exists not only in the V1 output layer, but also in the V1 input layer (Fig.
2A), and the filling degree of the surface reaction of the output layer is strongly related to the filling degree of the surface reaction of the input layer correlation (Fig.
2B)
.

This result suggests that the persistent surface responses in the V1 output layer are likely to be inherited from the surface of the input layer, induced directly by the surface brightness (as described by the feedforward mechanism), rather than by boundary responses transmitted to the surface through horizontal connections (as filling mechanism said)
.

Figure 2.
There are surface reactions with 'fill' features in both the input and output layers of V1 (Source: Yang Yi, et al.
, Nat Commun, 2022) The ability of neuronal responses to encode luminance information
.

The results show that the encoding strategies of luminance information are significantly different in the input and output layers
.

In the input layer of V1 (L4C), the surface response is more dominant than the boundary response in the encoding of luminance information (Fig.
3A, B); in the output layer of V1 (L2/3), the encoding advantage of the luminance information is derived from the surface The reaction switches to the boundary reaction (Fig.
3A,B)
.

In addition, the authors also found that the "fill"-like surface reaction did not carry more luminance information, either at the input or output layers (Fig.
3C).

.

Figure 3 The encoding strategy of luminance information is significantly different in the input and output layers (Source: Yang Yi, et al.
, Nat Commun, 2022) In the last part of the paper, the author further explains the neural mechanism of the above phenomenon
.

To measure whether surface and boundary responses interact during processing in V1, the authors compared responses elicited in V1 by two visual stimuli: a box with both a surface and a boundary and a box without a surface portion
.

The authors found that surface brightness elicited strong cortical inhibition in the output layer
.

This inhibitory component modulates not only the surface reaction but also the boundary reaction (Fig.
4A)
.

Based on the above results, the authors used the methods of systematic analysis and mathematical modeling to separate the excitatory and inhibitory components of the neuronal dynamic response, and analyzed the calculation principle of the brightness information of V1 (Fig.
4B)
.

The authors found that extensive cortical inhibition in the V1 output layer plays an important role in processing surface and boundary responses
.

The large-scale suppression of modulation of the local feedforward input explains both the cross-layer differences in surface and boundary responses (Fig.
4C) and also provides a reason for the transition of the luminance information encoding strategy at V1 (Fig.
4D)
.

 Figure 4 Cortical inhibition plays an important role in processing surface and boundary responses (Source: Yang Yi, et al.
, Nat Commun, 2022) Figure 5 The coding process and neural mechanism of visual stimulus surface brightness in V1 (Source: : Yang Yi, et al.
, Nat Commun, 2022) article conclusion and discussion, inspiration and prospect In conclusion, this study completely characterizes the encoding form of object surface brightness information in V1 and the underlying neural mechanism: feedforward between layers The connection drives both surface and boundary responses, and the local integration of feedforward signals improves the ability of boundary responses to encode luminance information
.

Inhibition within the visual cortex primarily suppressed surface responses and assigned more luminance information to boundary responses
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These two cortical processes combine to integrate surface brightness information into boundary responses, enabling boundary response-based encoding of efficiency (Fig.
5)
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The brightness information processing method disclosed in this paper is highly consistent with the cortical processing method of orientation information (object shape and texture correlation) revealed by Xing Dajun's research group in 2020 [11]
.

 The results of this work challenge the conventional view that surface brightness responses are filtered out by local central-peripheral antagonistic receptive field structures before entering V1, where the V1 output layer or higher visual cortex is responsible for 'filling in' surface information
.

The authors of this paper emphasize the important role of larger spatial-scale cortical mechanisms (central excitation + peripheral inhibition) in the encoding of brightness information; the new brightness processing mechanism proposed in this study has important implications for systems neuroscience, artificial intelligence, and human visual perception Interested readers have a very important reference value
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 Based on the results of this study, there are many related issues that deserve further exploration
.

One, exactly what circuits form the cortical inhibition caused by surface brightness
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In the model presented in this paper, inhibition is produced by pooling neural activity within a certain range of cortical space, and the process of pooling distinguishes whether the type of neural activity is boundary response or surface response
.

Whether the production of inhibition is pathway-specific is currently unclear
.

Secondly, this article uses simple visual stimuli, the original link: https://doi.
org/10.
1038/s41467-021-27892-3 The first author Yang Yi (1 row, right 2); the corresponding author Professor Xing Dajun ( 2 row left 1); and other main participants in the article Wang Tian (3 row right 3), Li Yang (1 row left 3), Dai Weifeng (3 row right 4), Han Chuanliang (3 row 1 left), Wu Yujie (1 row left) 2)
.

This work was supported by the National Natural Science Foundation of China (32171033, 32100831)
.

Family portrait of Professor Xing Dajun's laboratory (Photo courtesy of: Professor Xing Dajun's research group at the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University) Laboratory Introduction The group used electrophysiology, signal processing, mathematical calculation, and anatomy to study the primary visual cortex of higher mammals (rhesus monkeys) to systematically study single cells, groups of cells, and structurally differentiated cells in visual information processing.
Dynamics of cell population responses, mathematical models of their neural mechanisms, and brain concussions
.

The goal of the research group's work is to understand how the visual system processes visual perception information (such as brightness, color, motion, etc.
), and what is the relationship between concussions and visual perception (and other cognitive functions), the brain How the shocks in the brain are generated, and what these shocks can tell us about the brain
.

By answering these questions about perception and oscillations, the ultimate goal is to understand how the brain processes information from the most complex nonlinear dynamical systems
.

Recruitment information Postdoctoral research direction: Cognitive Neurology/Computational Neurology/Deep Learning/Visual Science/Neural Modeling/Visual Cognition Entry Requirements 1.
Possess a doctorate degree, excellent in character and study, in good health, and under the age of 35
.

  2.
Be able to work in the key laboratory full-time during the station, and not recruit on-the-job personnel
.

  3.
Applicants who have produced high-level scientific research achievements in the past five years can apply for corresponding postdoctoral fellows as required: A postdoctoral fellow must meet one of the following conditions: preside over scientific research projects at the provincial and ministerial level or above; natural sciences are ranked first Or the corresponding author has published 1 paper in SCI-Q1 area, or 2 papers in SCI-Q2 area, or 3 SCI papers
.

Category B postdoctoral fellows must meet one of the following conditions: Publish 1 SCI paper as the first or corresponding author in the natural science category
.

Li Yun postdoctoral fellows: (1) Full-time doctoral graduates who have obtained a doctorate degree from a foreign (overseas) world university comprehensive ranking or top 100 subject ranking universities or a doctorate degree obtained from an A+ subject in a domestic double-first-class construction school and a comparable level of scientific research Excellent graduates who have obtained a doctoral degree from the institution; (2) The doctoral degree has been obtained for less than three years, and the scientific research achievements are rich, and they meet the requirements for entering a class A postdoctoral fellow
.

 In-station treatment (detailed interview): refer to the postdoctoral standards of the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
.

Li Yun postdoctoral fellow: The salary is not less than 300,000 yuan per year, and social insurance and provident fund are paid in accordance with the regulations of the state and the school; the rental subsidy is 50,000 yuan per year, and the selected Liyun postdoctoral fellow can rent a postdoctoral apartment at the market price according to the housing situation; During the normal stay in the station, their eligible children can apply for admission to the kindergarten (Shahe) of the new campus of the experimental kindergarten of Beijing Normal University or the primary school (Shahe) of the Changping Affiliated School of Beijing Normal University (yuan)
.

Class A postdoctoral fellows: The salary is not less than 250,000 yuan per year, and social insurance and provident funds are paid in accordance with the regulations of the state and the school
.

Category B postdoctoral fellows: The salary is not less than 120,000 yuan per year, and social insurance and provident funds are paid in accordance with the regulations of the state and the school
.

   How to apply: Interested candidates please send your Chinese and English resume, past research content, future research plans and research interests, published articles, and other relevant materials that can prove your ability level to the cooperative tutor's mailbox, dajun_xing@bnu.
edu.
cn, please indicate "postdoctoral application + applicant's name" in the email
.

After we receive the resume, we will contact the applicant for an interview if interested, and keep the application materials confidential
.

 The main work of the research assistant: (1) Training experimental animals (rhesus monkeys), sorting and analyzing behavioral and electrophysiological data; (2) Data collection and literature sorting; (3) Mathematical modeling of electrophysiological experimental data; ( 4) Participate in other supportive work for the research activities of the research group
.

Application requirements: (1) Biology, psychology, science and engineering majors are acceptable, bachelor degree or above, and under 30 years old; (2) The work is extremely conscientious and responsible; (3) Those who have a strong interest in scientific research are preferred, and those who are motivated are preferred , with relevant work experience is preferred
.

(4) Good health, good dedication and teamwork spirit
.

 Salary: This position is employed by the labor contract system of Beijing Normal University, and the five insurances and one housing fund are covered according to the regulations.
The specific treatment is negotiable according to personal experience and ability
.

 If you are interested, please send your resume (including educational background, publications, other achievements and scientific research experience) to dajun_xing@bnu.
edu.
cn (please specify "Application for Research Assistant + Name" in the email)
.

Those who meet the requirements will be notified of the interview as soon as possible, and the application materials will be kept confidential
.

Selected articles from previous issues[1] STAR Protocols︱Min Zhao's research group proposes a new plan for physical intervention in the psychological craving of methamphetamine use disorder[2]Cereb Cortex︱Li Qiang/Mingdong research group jointly reveals the key to patients with mild cognitive impairment Structural lesions of white matter [3] Neuron︱“Eros and anger”: the neural substrate of female social behavior accompanied by changes in physiological state [4] Nat Neurosci︱ Jingjing Liu et al.
Reveal the effect of hypothalamic melanin aggregation hormone on the activity of the hippocampal-dorsolateral septal circuit Regulatory role【5】J Neurosci︱Merlin’s lab reveals the necessary signaling pathways for generating sharp ripples and spatial working memory: NRG1/ErbB4【6】Autophagy︱Li Xiaojiang’s team found that SQSTM1 in a non-human primate model has an effect on TDP-43 New progress in the clearance of cytoplasmic aggregates【7】Nat Neurosci︱Cao Peng’s lab discovered an important function of pain in the struggle for survival among species【8】Neurosci Bull︱Tongli’s research group revealed the role of dopaminergic neurons in the VTA-PrL neural pathway Rat sevoflurane exerts wake-promoting effect under general anesthesia【9】Nat Aging︱New discovery in Alzheimer's disease: a regulatory mechanism of bone marrow-derived macrophages independent of TREM2 pathway【10】Sci Transl Med︱mon New evidence for delayed onset of aminergic antidepressants: hippocampal cAMP modulates HCN channel function and affects behavior and memory in mice Bonus Book︱Introduction to the Hardcore Brain: Scientific References for Cognition and Self-Improvement (swipe up and down to view) 1.
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