初级视皮层神经元集群对拓扑特征响应的可视化表征

神经元集群响应的高维特性是脑机制研究面临的主要困难之一.拓扑特征是图像的基本特征之一,为了有效表征高维的神经元集群响应的拓扑特征特性,提出了一种基于三维自组织映射网络采用RGB颜色特征表征神经元集群响应的动态可视化方法,分析多通道微电极阵列采集的大鼠初级视觉皮层(V1区)神经元集群信号,进而研究了V1区神经元集群对图形拓扑特征的响应特性.通过与主成分分析(PCA)方法进行对比发现:该方法能够有效表征V1区神经元集群对拓扑结构的时序动态响应特征,表征方式形象直观,具有一定的优越性.     

初级视皮层神经元对瞬态刺激响应的时间性质

初级视皮层神经元对周期性运动光栅刺激具有周期性持续响应,而对没有周期性变化的静止恒定刺激的响应主要集中在刺激呈现的初期,为瞬时性响应.我们研究了在5～50 ms短时长静止刺激下神经元的响应性质,对响应时程的分析表明,猫初级视皮层神经元对瞬态刺激的响应曲线呈波峰形式.随着刺激的出现,在经过一段延迟后,响应幅度上升,出现一个或多个响应峰,随刺激的消失,响应幅度下降.神经元表现出随刺激时长的增长,响应峰主峰(第一个峰)的峰值时间和峰宽都有增加的趋势,但峰值时间在30～50 ms趋于饱和,除5 ms刺激时长诱发的峰高明显降低以外,其他刺激时长诱发的峰高较恒定.刺激消失变为灰屏时,神经元会产生撤反应峰(刺激后发放,offset responses),其强度由前面的刺激时长决定.最小响应时长约为39 ms,主峰与撤峰的最小时间差为36 ms,二者十分相近,提示了视初级视皮层神经元的基本响应时间约为35～40 ms.这可能是"视觉暂留"现象在初级视皮层水平的生理基础.     

A Nonlinear Model of the Behavior of Simple Cells in Visual Cortex

Despite their structured receptive fields (RFs) and the strong linear components in their responses, most simple cells in mammalian visual cortex exhibit nonlinear behaviors. Besides the contrast-response function, nonlinearities are evident in various types of failure at superposition tasks, in the disagreement between direction indices computed from drifting and counterphase flickering gratings, in various forms of response suppression (including end- and side-stopping, spatial-frequency-specific inhibition and cross-orientation inhibition), in the advance of phase with increasing contrast, and in phase-insensitive and frequency-doubled responses to counterphase flickering gratings. These behaviors suggest that nonlinearities are involved in the operation of simple cells, but current models fail to explain them. A quantitative model is presented here that purports to describe basic and common principles of operation for all visual cortical cells. Simple cells are described as receiving afferents from multiple subunits that differ in their individual RFs and temporal impulse responses (TIRs). Subunits are independent and perform a spatial integration across their RFs followed by halfwave rectification and temporal convolution with their TIRs. This parallel operation yields a set of temporal functions representing each subunit's contribution to the membrane potential of the host cell, whose final form is given by the weighted sum of all subunits' contributions. By varying the number of subunits and their particular characteristics, different instances of the model are obtained each of which displays a different set of behaviors. Extensive simulation results are presented that illustrate how all of the reported nonlinear behaviors of simple cells arise from these multi-subunit organizations.     

Orientation Preference Maps in Microcebus murinus Reveal Size-Invariant Design Principles in Primate Visual Cortex

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综述: 初级视皮层的亮度信息加工

亮度(luminance)是最基本的视觉信息.与其他视觉特征相比,由于视神经元对亮度刺激的反应较弱,并且许多神经元对均匀亮度无反应,对亮度信息编码的神经机制知之甚少.初级视皮层部分神经元对亮度的反应要慢于对比度反应,被认为是由边界对比度诱导的亮度知觉(brightness)的神经基础.我们的研究表明,初级视皮层许多神经元的亮度反应要快于对比度反应,并且这些神经元偏好低的空间频率、高的时间频率和高的运动速度,提示皮层下具有低空间频率和高运动速度通路的信息输入对产生初级视皮层神经元的亮度反应有贡献.已经知道初级视皮层神经元对空间频率反应的时间过程是从低空间频率到高空间频率,我们发现的早期亮度反应是对极低空间频率的反应,与这一时间过程是一致的,是这一从粗到细的视觉信息加工过程的第一步,揭示了处理最早的粗的视觉信息的神经基础.另外,初级视皮层含有偏好亮度下降和高运动速度的神经元,这群神经元的活动有助于在光照差的环境中检测高速运动的低亮度物体.     

初级视皮层神经元非经典感受野研究的新进展

感觉皮层神经元的非经典感受野(简称"外周")对经典感受野(简称"中心")的调节作用广泛存在于哺乳动物中,被认为是感觉皮层神经元的基本特性.以初级视皮层神经元为例,刺激其外周能有效地调节刺激其中心引起的反应,这种作用主要是抑制性的.理解初级视皮层神经元的外周对中心的调节机制能够深入揭示哺乳动物的感觉皮层神经元信息处理的基本原则.本文综述了引起初级视皮层神经元非经典感受野对经典感受野调节作用的神经环路机制和计算模型研究的进展.     

Experience-Dependent Development and Maintenance of Binocular Neurons in the Mouse Visual Cortex

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Decoding Temporal Information Through Slow Lateral Excitation in the Olfactory System of Insects

Sensory information is represented in a spatio-temporal code in the antennal lobe, the first processing stage of the olfactory system of insects. We propose a novel mechanism for decoding this information in the next processing stage, the mushroom body. The Kenyon cells in the mushroom body of insects exhibit lateral excitatory connections at their axons. We demonstrate that slow lateral excitation between Kenyon cells allows one to decode sequences of activity in the antennal lobe. We are thus able to clarify the role of the existing connections as well as to demonstrate a novel mechanism for decoding temporal information in neuronal systems. This mechanism complements the variety of existing temporal decoding schemes. It seems that neuronal systems not only have a rich variety of code types but also quite a diversity of algorithms for transforming different codes into each other.     

Dopamine Is Signaled by Mid-frequency Oscillations and Boosts Output Layers Visual Information in Visual Cortex

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Integration of Visual Information in Auditory Cortex Promotes Auditory Scene Analysis through Multisensory Binding

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Neural Representation of Ambiguous Visual Objects in the Inferior Temporal Cortex

Inferior temporal (IT) cortex as the final stage of the ventral visual pathway is involved in visual object recognition. In our everyday life we need to recognize visual objects that are degraded by noise. Psychophysical studies have shown that the accuracy and speed of the object recognition decreases as the amount of visual noise increases. However, the neural representation of ambiguous visual objects and the underlying neural mechanisms of such changes in the behavior are not known. Here, by recording the neuronal spiking activity of macaque monkeys’ IT we explored the relationship between stimulus ambiguity and the IT neural activity. We found smaller amplitude, later onset, earlier offset and shorter duration of the response as visual ambiguity increased. All of these modulations were gradual and correlated with the level of stimulus ambiguity. We found that while category selectivity of IT neurons decreased with noise, it was preserved for a large extent of visual ambiguity. This noise tolerance for category selectivity in IT was lost at 60% noise level. Interestingly, while the response of the IT neurons to visual stimuli at 60% noise level was significantly larger than their baseline activity and full (100%) noise, it was not category selective anymore. The latter finding shows a neural representation that signals the presence of visual stimulus without signaling what it is. In general these findings, in the context of a drift diffusion model, explain the neural mechanisms of perceptual accuracy and speed changes in the process of recognizing ambiguous objects.