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Linking behavioral deficits with underlying neural property changes in amblyopia

Neuropsychologia 2025年 214卷 109156页

作者： Jinli Zhu Yijin Han Xiaolin Huang Yufan Feng Xiaowei Ruan Wenman Lin Jiawei Zhou Fang Hou School of Ophthalmology & Optometry and Eye hospital Wenzhou Medical University Wenzhou 325027 China. School of Ophthalmology & Optometry and Eye hospital Wenzhou Medical University Wenzhou 325027 China Xi'an People's Hospital Xi'an 710004 Shaanxi China. National Engineering Research Center of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou 325027 China State Key Laboratory of Ophthalmology Optometry and Vision Science Eye Hospital Wenzhou Medical University Wenzhou 325027 China. State Key Laboratory of Ophthalmology Optometry and Vision Science Eye Hospital Wenzhou Medical University Wenzhou 325027 China. Electronic address: zhoujw@***. State Key Laboratory of Ophthalmology Optometry and Vision Science Eye Hospital Wenzhou Medical University Wenzhou 325027 China. Electronic address: houf@***.

While it is widely accepted that abnormal visual experience during critical period can lead to significant functional deficits and altered neural property, the quantitative link between behavioral visual losses and underlying neural changes remains elusive. To address this gap, we systematically varied stimulus orientation and contrast to measure 2D psychometric functions of amblyopic and normally sighted participants at two different spatial frequencies. A biologically-interpretable neural population model explicitly incorporated with neural contrast response function (CRF) and orientation tuning property accounted for the complex performance data for both groups. Our results revealed that the poor performance in the amblyopic group can be excellently explained by a rightward-shifted CRF at higher spatial frequency and reduced population Fisher information for coding orientation. Moreover, regression analysis revealed that the behavior contrast threshold from an independent measurement significantly depended on the neural properties estimated by the model. This study demonstrates the potential of biologically-interpretable models to quantitatively bridge the gap between behavioral deficits and underlying neural changes, offering a promising tool for understanding normal and abnormal visual systems.

关键词： Amblyopia Computational model Contrast response function Orientation tuning population coding

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Bio-Inspired Electromagnetic Protection Based on Neural Information Processing

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Journal of Bionic Engineering 2014年第1期11卷 151-157页

作者： Xiaolong Chang Shanghe Liu Menghua Man Weihua Han Jie Chu Liang Yuan Institute of Electrostatic and Electromagnetic Protection Mechanical Engineering College Shijiazhuang 050003 P R. China Institute of Semiconductors Chinese Academy of Science Beijing 100083 P. R. China Department of Information Engineering Mechanical Engineering College Shijiazhuang 050003 P. R. China

Electronic systems are vulnerable in electromagnetic interference environment. Although many solutions are adopted to solve this problem, for example shielding, filtering and grounding, noise is still introduced into the circuit inevitably. What impresses us is the biological nervous system with a vital property of robustness in noisy environment. Some mechanisms, such as neuron population coding, degeneracy and parallel distributed processing, are believed to partly explain how the nervous system counters the noise and component failure. This paper proposes a novel concept of bio-inspired electromagnetic protec- tion making reference to the characteristic of neural information processing. A bionic model is presented here to mimic neuron populations to transform the input signal into neural pulse signal. In the proposed model, neuron provides a dynamic feedback to the adjacent one according to the concept of synaptic plasticity. A simple neural circuitry is designed to verify the rationality of the bio-inspired model for electromagnetic protection. The experiment results display that bio-inspired electromagnetic pro- tection model has more power to counter the interference and component failure.

关键词： biological nervous system robustness population coding bio-inspired electromagnetic protection model neuralcircuitry

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Stimulus representation in rat primary visual cortex: multi-electrode recordings with micro-machined silicon probes and estimation theory

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NEUROCOMPUTING 2002年 44卷 407-416页

作者： Freiwald, WA Stemmann, H Wannig, A Kreiter, AK Hofmann, UG Hills, MD Kovacs, GTA Kewley, DT Bower, JM Etzold, A Wilke, SD Eurich, CW MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA Univ Bremen Inst Brain Res FB 2 D-28334 Bremen Germany Med Univ Lubeck Inst Signal Proc D-23569 Lubeck Germany Stanford Univ Ctr Integrated Syst CISX 202 Stanford CA 94305 USA CALTECH Pasadena CA 91125 USA Univ Texas Hlth Sci Ctr Res Image Ctr San Antonio TX 78229 USA Univ Texas Cajal Neurosci Res Ctr San Antonio TX 78249 USA Univ Bremen Dept Theoret neurophys FB 1 D-28334 Bremen Germany

The study of neural population codes relies on massively parallel recordings in combination with theoretically motivated analysis tools. We applied two multi-site recording techniques to record from cells throughout cortical depth in a minimally invasive way. The feasibility of such experiments in area 17 of the anesthetized rat is demonstrated, Bayesian reconstruction and the interpretative framework of Fisher information are introduced, We demonstrate applicability and usefulness of Bayesian stimulus reconstruction and show that even small numbers of neurons can yield a high degree of representational accuracy under favorable conditions. Results are discussed and future lines of research outlined. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： population coding Bayesian reconstruction Fisher information multi-site recording technology visual cortex

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Bio-inspired computer vision: Towards a synergistic approach of artificial and biological vision

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 150卷 1-30页

作者： Medathati, N. V. Kartheek Neumann, Heiko Masson, Guillaume S. Kornprobst, Pierre Inria Sophia Antipolis Mediterranee Biovis Team 2004 Route LuciolesBP 93 F-06902 Sophia Antipolis France Univ Ulm Inst Neural Informat Proc D-89081 Ulm Germany CNRS UMR7289 Inst Neurosci Timone 27 Blvd Jean Moulin F-13005 Marseille France Aix Marseille Univ 27 Blvd Jean Moulin F-13005 Marseille France

Studies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license.

关键词： Canonical computations Event based processing Dynamic sensors Multiplexed representation population coding Soft selectivity Feedback Lateral interactions Form-motion interactions

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A Neurophysiologically Plausible population Code Model for Feature Integration Explains Visual Crowding

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PLOS COMPUTATIONAL BIOLOGY 2010年第1期6卷 1-11页

作者： van den Berg, Ronald Roerdink, Jos B. T. M. Cornelissen, Frans W. Univ Groningen Inst Math & Comp Sci Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Lab Expt Ophthalmol Groningen Netherlands Univ Groningen Sch Behav & Cognit Neurosci Groningen Netherlands

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

关键词： .population Code Spatial Integration Feature Integration Visual Crowding population coding

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Suprathreshold stochastic resonance induced by ion channel fluctuation

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PHYSICA D-NONLINEAR PHENOMENA 2010年第6期239卷 327-334页

作者： Ashida, Go Kubo, Masayoshi Univ Maryland Dept Biol College Pk MD 20742 USA Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

In this study, we examine the signal detection ability of an array of neurons with intrinsic channel fluctuation. Numerical simulations show that estimation of the input signal from the output spiking activity of the neuronal array is most accurate if a proper amount of channel noise exists. Theoretical calculation of the mutual and Fisher information confirms the effect of the noise-aided information transfer in the array, or the presence of suprathreshold stochastic resonance. These results indicate that the channel noise, which induces response variability, may play an essential role in population coding. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Noise Stochastic resonance Neuronal modeling Ion channel population coding Information theory

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Asymptotic behaviors of population codes

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NEUROCOMPUTING 2002年 44卷 697-702页

作者： Wu, S Amari, S Nakahara, H Univ Sheffield Dept Comp Sci Sheffield S10 2TN S Yorkshire England RIKEN Brain Sci Inst Wako Saitama Japan

The present study investigates asymptotic behaviors of population codes, i.e., their performances when the number of neurons goes to infinity. Since the number of neurons involved in population coding is often large, asymptotic behaviors are the properties of biologic relevance, This study is based on a neural field model for the encoding process, which has a simple form. whereas, covers a broad range of important correlation cases. The asymptotic behaviors of both Fisher information and maximum likelihood type of decoding methods are clarified. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： population coding asymptotic behavior Fisher information maximum likelihood center of mass

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Path Integration Mechanism with Coarse coding of Neurons

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NEURAL PROCESSING LETTERS 2011年第3期34卷 277-291页

作者： Kim, DaeEun Lee, Jiwon Yonsei Univ Biol Cybernet Lab Sch Elect & Elect Engn Seoul 120749 South Korea

Many animals can return home accurately after exploring for food using their own homing navigation algorithm. Path integration plays a critical role in homing navigation. It is believed that animals are able to recognize their relative location from the nest by accumulating both distance and direction experienced during their travel. We tested possible patterns of neuronal organization for a path integration mechanism. The neural networks consisted of a circular array of neurons, following population coding. We describe here a neural model of path integration involving a relatively small number of neurons and discuss how well the model operates for homing navigation. Robotic simulations suggest that a neural structure with only a few sensor neurons can successfully handle the path integration needed for homing navigation.

关键词： Path integration Homing navigation Coarse coding population coding Circular array neurons

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Designing neural networks that process mean values of random variables

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PHYSICS LETTERS A 2014年第30-31期378卷 2163-2167页

作者： Barber, Michael J. Clark, John W. AIT Innovat Syst Dept A-1220 Vienna Austria Washington Univ Dept Phys St Louis MO 63130 USA Washington Univ McDonnell Ctr Space Sci St Louis MO 63130 USA Univ Madeira Ctr Ciencias Matemat P-9000390 Funchal Portugal

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Neural networks Probabilistic models Bayesian networks Bayesian inference Neural information processing population coding

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A BIOLOGICALLY INSPIRED NEURAL MODEL OF VISION-LANGUAGE INTEGRATION

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NEURAL NETWORK WORLD 2011年第3期21卷 227-249页

作者： Plebe, Alessio Mazzone, Marco De La Cruz, Vivian M. Dept Cognit Sci I-98121 Messina Italy Lab Cognit Sci I-95125 Catania Italy

One crucial step in the construction of the human representation of the world is found at the boundary between two basic stimuli: visual experience and the sounds of language. In the developmental stage when the ability of recognizing objects consolidates, and that of segmenting streams of sounds into familiar chunks emerges, the mind gradually grasps the idea that utterances are related to the visible entities of the world. The model presented here is an attempt to reproduce this process, in its basic form, simulating the visual and auditory pathways, and a portion of the prefrontal cortex putatively responsible for more abstract representations of object classes. Simulations have been performed with the model, using a set of images of 100 real world objects seen from many different viewpoints and waveforms of labels of various classes of objects. Subsequently, categorization processes with and without language are also compared.

关键词： Categorization self-organization visual cortex population coding

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