Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the pr...Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.展开更多
Background:The primary visual cortex(V1)is a key component of the visual system that builds some of the first levels of coherent visual representations from sparse visual inputs.While the study of its dynamics has bee...Background:The primary visual cortex(V1)is a key component of the visual system that builds some of the first levels of coherent visual representations from sparse visual inputs.While the study of its dynamics has been the focus of many computational models for the past years,there is still relatively few research works that put an emphasis on both synaptic plasticity in V1 and biorealism in the context of learning visual inputs.Here,we present a recurrent spiking neural network that is capable of spike timing dependent plasticity(STDP)and we demonstrate its capacity to discriminate spatio-temporal orientation patterns in noisy natural images.Methods:A two stage model was developed.First,natural images flux(be it videos/gratings/camera)were converted into spikes,using a difference of gaussians(DOG)approach.This transformation approximates the retina-lateral geniculate nucleus(LGN)organization.Secondly,a spiking neural network was build using PyNN simulator,mimicking cortical neurons dynamics and plasticity,as well as V1 topology.This network was then fed with spikes generated by the first model and its ability to build visual representations was assessed using control gratings inputs.Results:The neural network exhibited several interesting properties.After a short period of learning,it was capable of learning multiples orientations and reducing noise in such learned feature,compared to the inputs.These learned features were stable even after increasing the noise in inputs and were found to not only encoding the spatial properties of the input,but also its temporal aspects(i.e.,the time of each grating presentation Conclusions:Our work shows that topological structuring of the cortical neural networks,combined with simple plasticity rules,are sufficient to drive strong learning dynamics of natural images properties.This computational model fits many properties found in the literature and provides some theoritical explanations for the shape of tuning curve of certain layers of V1.Further investigations are now conducted to validate its properties against the neuronal responses of rodents,using identical visual stimuli.展开更多
基金supported by the STI 2030-Major Projects 2022ZD0208500(to DY)the National Natural Science Foundation of China,Nos.82072011(to YX),82121003(to DY),82271120(to YS)+2 种基金Sichuan Science and Technology Program,No.2022ZYD0066(to YS)a grant from Chinese Academy of Medical Science,No.2019-12M-5-032(to YS)the Fundamental Research Funds for the Central Universities,No.ZYGX2021YGLH219(to KC)。
文摘Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.
文摘Background:The primary visual cortex(V1)is a key component of the visual system that builds some of the first levels of coherent visual representations from sparse visual inputs.While the study of its dynamics has been the focus of many computational models for the past years,there is still relatively few research works that put an emphasis on both synaptic plasticity in V1 and biorealism in the context of learning visual inputs.Here,we present a recurrent spiking neural network that is capable of spike timing dependent plasticity(STDP)and we demonstrate its capacity to discriminate spatio-temporal orientation patterns in noisy natural images.Methods:A two stage model was developed.First,natural images flux(be it videos/gratings/camera)were converted into spikes,using a difference of gaussians(DOG)approach.This transformation approximates the retina-lateral geniculate nucleus(LGN)organization.Secondly,a spiking neural network was build using PyNN simulator,mimicking cortical neurons dynamics and plasticity,as well as V1 topology.This network was then fed with spikes generated by the first model and its ability to build visual representations was assessed using control gratings inputs.Results:The neural network exhibited several interesting properties.After a short period of learning,it was capable of learning multiples orientations and reducing noise in such learned feature,compared to the inputs.These learned features were stable even after increasing the noise in inputs and were found to not only encoding the spatial properties of the input,but also its temporal aspects(i.e.,the time of each grating presentation Conclusions:Our work shows that topological structuring of the cortical neural networks,combined with simple plasticity rules,are sufficient to drive strong learning dynamics of natural images properties.This computational model fits many properties found in the literature and provides some theoritical explanations for the shape of tuning curve of certain layers of V1.Further investigations are now conducted to validate its properties against the neuronal responses of rodents,using identical visual stimuli.
