视顶盖神经元在不同颜色背景下的亮度解码

研究了鸽子视顶盖(the optic tectum,OT)神经元在不同颜色背景下对亮度的编码机制。选取色相环(Hue)中六等分处对应的颜色(H0、H16、H33、H49、H66、H82),设计了6种颜色背景下具有瞬态闪变特性的亮度刺激模式,采集了OT区神经元的峰电位发放序列(Spike),提取了Spike序列的特征,构造了多元线性逆滤波器进行解码。根据互相关性和正交试验的方法对重建亮度和实际亮度进行了分析。结果表明,神经元对色相环中相邻颜色的编码机制相近,例如H16(黄)和H33(绿)、H0(红)和H82(洋红)的相关性曲线十分靠近;色相环中H66(蓝)的背景非常削弱对于亮度的解码。正交试验的结果发现,参与神经元解码的四个参数(T、bin、chan_nums、delay)之间存在很强的交互性,彼此同等,共同作用参与神经元的解码。     

基于信鸽视顶盖神经集群响应的目标类别解码研究

通过提取信鸽视顶盖(The Optic Tectum, OT)的中间层神经元集群高频峰电位(spike)响应信号,利用小波和信息论的方法提取并筛选不同时间尺度上的响应特征,构建集群响应的特征向量,从而实现对目标类别的解码。首先,根据感受野特征筛选有效响应通道;然后通过特定目标对信鸽进行刺激实验,采用微电极阵列记录多通道的spike发放序列;其次设定时间窗口(bin)长度,采用小波和信息论的方法提取并筛选响应特征;最后利用朴素贝叶斯分类算法实现目标类别解码。结果表明,一方面本文方法所提取的响应特征能够实现对目标类别的解码和识别;另一方面,通过对bin取1ms的响应信号所提取特征进行统计分析,发现提取的包含目标类型信息高的小波系数大都属于较大分解层次上的系数,表明鸽子OT区可能利用这些尺度上的时间模式对目标的信息进行了编码,这为研究OT中间神经元的时间响应结构奠定了基础。     

基于信鸽视顶盖神经集群响应的目标类别解码

通过提取信鸽视顶盖(the optic tectum,OT)的中间层神经元集群高频峰电位(spike)响应信号,利用小波和信息论的方法提取并筛选不同时间尺度上的响应特征,构建集群响应的特征向量,从而实现对目标类别的解码。首先,根据感受野特征筛选有效响应通道;然后通过特定目标对信鸽进行刺激实验,采用微电极阵列记录多通道的spike发放序列;其次设定时间窗口(bin)长度,采用小波和信息论的方法提取并筛选响应特征;最后利用朴素贝叶斯分类算法实现目标类别解码。结果表明,一方面所提取的响应特征能够实现对目标类别的解码和识别;另一方面,通过对bin取1 ms的响应信号所提取特征进行统计分析,发现提取的包含目标类型信息高的小波系数大都属于较大分解层次上的系数,表明鸽子OT区可能利用这些尺度上的时间模式对目标的信息进行了编码,这为研究OT中间神经元的时间响应结构奠定了基础。     

信鸽视顶盖中间层ON-OFF神经元在空间统一白噪声刺激下的编码

感知系统神经科学的一个基本目标是刻画刺激和神经元响应之间的功能性关系。空间统一白噪声（spatial uniform white noise,SUN）刺激包含了丰富的时间变化信息,SUN 刺激下的神经响应揭示了其时间编码属性。基于视网膜神经节细胞-视顶盖（retinal ganglion cells - optic tectum,RGC-OT）神经回路的信息整合假设,本文提出了信鸽视顶盖浅中层 ON-OFF 神经元 SUN 刺激响应的双滤波器半波整流模型,用于 SUN 刺激响应的预报。为解决凸优化问题,利用二次曲线对半波整流进行近似,将双滤波器模型转换为广义二次模型（generalized quadratic model,GQM）,然后进行优化,预报结果表明二次模型相比较传统的线性-非线性-泊松分布（linear nolinear possion,LNP）模型,获得了更好的预报效果。该结果也验证了 OT 神经元以不同的通路进行时变的亮度增强和降低的信息处理的假设。     

鸽子视顶盖颜色信息的解码

研究了鸽子视顶盖神经元对颜色信息的编码机制并解码了三种颜色刺激。首先,针对鸽子视网膜四种类型敏感细胞设计了红、绿、蓝三种色块刺激模式。然后,对视觉实验采集到的神经元响应信号绘制其刺激后时间直方图(peri-stimulus time histogram,PSTH)。确定出神经元有效响应区间后发现,视顶盖神经元以集群的方式对颜色信息进行编码,对于不同的顶盖神经元,三种色块刺激的PSTH曲线存在不同的响应规律;对于同一个顶盖神经元,多次实验下三种色块刺激的PSTH曲线有着近乎一致的规律。最后,分别采用主成分分析法(primary component analysis,PCA)和等度规映射法(isometric mapping,ISOMAP)对集群特征进行降维,采用概率型神经网络(probabilistic neural network,PNN)对三种色块进行解码,结果表明,不同的特征降维方法,都具有较高的识别率,再次验证了鸽子视顶盖神经元是以集群的方式对颜色信息进行编码。     

大鼠基底外侧杏仁核对恐惧视觉信息的编码分析

研究大鼠在条件性恐惧视觉建立过程中基底外侧杏仁核(basal lateral amygdala, BLA)如何对恐惧视觉信息进行神经元响应特征编码分析。通过对实验Long-Evans(LE)大鼠进行电极植入手术,采用巴普洛夫原理设计基于自然图像的恐惧训练实验范式;用多通道信号采集系统采集训练前、后BLA脑区3种不同属性图片刺激时的锋电位(Spike)神经信号,并且分别根据发放率编码和信息熵编码方法对所提取Spike信号进行编码分析;最后,采用朴素贝叶斯分类器对图片属性进行分类解码判断,研究大鼠BLA区对恐惧视觉信息进行神经编码的机制。结果表明,恐惧训练后Spike发放率和信息熵均有明显增加,解码正确率为恐惧94%、非恐惧93%。说明恐惧视觉信息能够激活大鼠BLA脑区的神经元响应,朴素贝叶斯分类器可以实现对神经元集群响应的分类解码,且对恐惧信息解码效果更好。     

基于稀疏-小世界网络的初级视皮层神经元发放模型

通过构建神经元发放模型实现对视觉系统刺激编码方式的解析是计算神经生理学领域的研究热点。在传统线性-非线性-泊松(LNP)模型的基础上,采用稀疏编码模型训练的基函数作为模型的刺激滤波器,进一步利用小世界网络优化神经元集群的连接结构,构建了一种新型初级视皮层(V1区)神经元发放模型,用于预测神经元在特定刺激模式下的响应活动。利用在LE大鼠V1区采集的多通道发放数据拟合模型参数,进一步验证模型的有效性。实验结果表明,与传统未选择基函数作为刺激滤波器以及未经过小世界网络优化的对照模型相比,该模型能更准确地预测大鼠V1区神经元在不同朝向光栅刺激下的响应。该研究表明,经小世界网络优化后,模型中神经元的连接结构具有更强的生物相似性,能更真实地反映初级视觉皮层神经元群的响应机制。     

基于神经元突触可塑性机制图像边缘检测方法

针对图像边缘信息的有效提取问题,提出了基于脉冲时间相关突触可塑性(STDP)机制的边缘检测新方法.首先通过亮度和色度编码实现颜色拮抗特性;利用Log-Gabor滤波器提取符合人类视觉特性的特定方向图像信息;接着建立了一种具有突触STDP特性的神经元网络模型,利用神经元之间非同步放电与视觉轮廓的关联性强化边缘信息;最后通过首次放电时间解码获取边缘信息.以微生物显微图像为例进行实验研究,结果表明:所提方法获取的图像边缘信息清晰完整,并且保留了更多的微弱细节;为突触可塑性机制在图像处理中的应用提供新思路.     

大鼠杏仁核在条件性恐惧视觉建立过程中的集群编码研究

研究了大鼠在条件性恐惧视觉建立过程中杏仁核对恐惧视觉信息的编码。首先,设计两种不同拓扑结构("十"和"O")图形,利用巴普洛夫条件反射原理建立大鼠条件性恐惧视觉联结,采用多通道神经信号采集系统采集恐惧视觉建立过程中的杏仁核神经元集群响应信号。然后,对神经元响应信号进行有效响应区间的自适应选取,分别采用神经元集群发放频率和集群熵研究条件性恐惧视觉建立的不同阶段杏仁核的集群编码,发现神经元集群在条件性恐惧建立后发放率、熵均有显著增加。最后,采用支持向量机构建条件性恐惧建立过程中不同恐惧水平的分类模型,验证两种编码的效果。结果表明集群熵编码包含更多的非线性信息和时空整合信息,能更有效地实现恐惧视觉建立过程中视觉信息的"恐惧"水平的表征,由此推测大鼠杏仁核神经核团是以集群的方式对恐惧信息进行编码的。     

基于spike-LFP同步的神经元朝向选择性分析

局部场电位(local field potential,LFP)与神经元发放的锋电位(spike)之间的同步关系揭示了重要的神经编码信息。为了研究不同对比度的光栅刺激下猕猴视皮层V1区和V4区神经元的朝向选择性,本文将锋电位触发的相关矩阵同步方法应用于在猕猴视皮层V1区和V4区记录的微电极信号,发现spike与gamma频段LFP之间的同步与光栅朝向之间存在较强的相关性,反映了视觉刺激的基本特征信息。另外,神经元spike的发放速率虽然会受到光栅朝向的影响,但是在不同对比度下发放速率并无明显区别,而spike-LFP同步会随着对比度的增加而增加,能够反映出同一朝向下对比度的变化。本文还利用圆方差来量化spike-LFP同步的朝向调谐曲线,发现对比度越大,V1区和V4区神经元的朝向选择性越强,V1区神经元比V4区神经元具有更强的朝向选择性。总之,spike-LFP同步能够更深入地揭示视皮层神经元的朝向调谐特性,为研究朝向选择性的形成机制提供新的方向。     

Spatio-temporal correlations and visual signalling in a complete neuronal population

Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.     

Adaptive filtering enhances information transmission in visual cortex

Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain's coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we compare responses to natural inputs with those to noise inputs matched for luminance and contrast. We find that neural filters adaptively change with the input ensemble so as to increase the information carried by the neural response about the filtered stimulus. Adaptation affects the spatial frequency composition of the filter, enhancing sensitivity to under-represented frequencies in agreement with optimal encoding arguments. Adaptation occurs over 40 s to many minutes, longer than most previously reported forms of adaptation.     

Mapping multiple features in the population response of visual cortex

Stimulus features such as edge orientation, motion direction and spatial frequency are thought to be encoded in the primary visual cortex by overlapping feature maps arranged so that the location of neurons activated by a particular combination of stimulus features can be predicted from the intersections of these maps. This view is based on the use of grating stimuli, which limit the range of stimulus combinations that can be examined. We used optical imaging of intrinsic signals in ferrets to assess patterns of population activity evoked by the motion of a texture (a field of iso-oriented bars). Here we show that the same neural population can be activated by multiple combinations of orientation, length, motion axis and speed. Rather than reflecting the intersection of multiple maps, our results indicate that population activity in primary visual cortex is better described as a single map of spatiotemporal energy.     

Adaptive coding of visual information in neural populations

Our perception of the environment relies on the capacity of neural networks to adapt rapidly to changes in incoming stimuli. It is increasingly being realized that the neural code is adaptive, that is, sensory neurons change their responses and selectivity in a dynamic manner to match the changes in input stimuli. Understanding how rapid exposure, or adaptation, to a stimulus of fixed structure changes information processing by cortical networks is essential for understanding the relationship between sensory coding and behaviour. Physiological investigations of adaptation have contributed greatly to our understanding of how individual sensory neurons change their responses to influence stimulus coding, yet whether and how adaptation affects information coding in neural populations is unknown. Here we examine how brief adaptation (on the timescale of visual fixation) influences the structure of interneuronal correlations and the accuracy of population coding in the macaque (Macaca mulatta) primary visual cortex (V1). We find that brief adaptation to a stimulus of fixed structure reorganizes the distribution of correlations across the entire network by selectively reducing their mean and variability. The post-adaptation changes in neuronal correlations are associated with specific, stimulus-dependent changes in the efficiency of the population code, and are consistent with changes in perceptual performance after adaptation. Our results have implications beyond the predictions of current theories of sensory coding, suggesting that brief adaptation improves the accuracy of population coding to optimize neuronal performance during natural viewing.     

鸽子运动意图的最大似然估计解码算法

神经信息解码是目前植入式脑机接口(brain-computer interface,BCI)神经信息处理研究中的难点和重点;解码效果的优劣以及解码算法的效率直接决定了脑机接口应用的有效性和实用性。为了解码十字迷宫内鸽子运动转向信息,利用高斯分布模型对神经元锋电位发放率的概率密度函数进行建模;并结合最大似然估计(maximum likelihood estimation,MLE)算法对鸽子的转向意图进行了预测;并将其结果与(support vector machine,SVM)法和群矢量(population vector,PV)法进行了比较。结果表明,MLE算法能够有效地解码鸽子的运动意图,解码正确率显著高于SVM法和PV法。这一结果也为进一步分析鸽子运动意图神经信息处理机制奠定了基础。     

视知觉训练与初级视皮层神经元信息及其表征容量

通过分析猕猴初级视皮层（V1）神经元的群体活动在轮廓线检测训练过程中的变化发现：知觉训练可以降低V1神经元响应在不同试次之间的变异性，促使V1神经元对相同刺激的响应更加稳定；训练还可降低V1神经元群体中神经元活动之间的相关性，使V1神经元之间的活动更加独立；增加了神经活动的维度，提高了神经元表征信息的容量，减少了冗余的神经活动，进而提升了感知能力.     

Motion streaks provide a spatial code for motion direction.

Although many neurons in the primary visual cortex (V1) of primates are direction selective, they provide ambiguous information about the direction of motion of a stimulus. There is evidence that one of the ways in which the visual system resolves this ambiguity is by computing, from the responses of V1 neurons, velocity components in two or more spatial orientations and then combining these velocity components. Here I consider another potential neural mechanism for determining motion direction. When a localized image feature moves fast enough, it should become smeared in space owing to temporal integration in the visual system, creating a spatial signal-a 'motion streak'-oriented in the direction of the motion. The orientation masking and adaptation experiments reported here show that these spatial signals for motion direction exist in the human visual system for feature speeds above about 1 feature width per 100 ms. Computer simulations show that this psychophysical finding is consistent with the known response properties of V1 neurons, and that these spatial signals, when appropriately processed, are sufficient to determine motion direction in natural images.     

Neural suppression of distractors surrounding the spotlight: Evidence from steady-state visual evoked potentials

The present study investigated the allocation of spatial attention using steady-state visual evoked potentials(SSVEPs).The SSVEP is elicited in visual cortical areas by a repetitive flicker having the same fundamental frequency as the driving stimulus.Two flickers were applied with the letter stream presented in the center of the monitor and the distractor presented on either the left or right side of the target.Participants were instructed to detect the target letter in the letter stream.The distance of the two flickers was manipulated.The results show that the amplitudes of the SSVEPs elicited by the distractor were enhanced when it was in the closest position and suppressed when it was at a farther distance.But the amplitudes rebounded at the farthest distance.Meanwhile,the SSVEP elicited by the target flicker remained stable independent of the distance of the distractor.Thus,the present study indicates that focused attention involves neural suppression surrounding the classic "spotlight",and the SSVEP paradigms open new avenues for studying the attentional suppression mechanism.     

Correlation between neural spike trains increases with firing rate

Populations of neurons in the retina, olfactory system, visual and somatosensory thalamus, and several cortical regions show temporal correlation between the discharge times of their action potentials (spike trains). Correlated firing has been linked to stimulus encoding, attention, stimulus discrimination, and motor behaviour. Nevertheless, the mechanisms underlying correlated spiking are poorly understood, and its coding implications are still debated. It is not clear, for instance, whether correlations between the discharges of two neurons are determined solely by the correlation between their afferent currents, or whether they also depend on the mean and variance of the input. We addressed this question by computing the spike train correlation coefficient of unconnected pairs of in vitro cortical neurons receiving correlated inputs. Notably, even when the input correlation remained fixed, the spike train output correlation increased with the firing rate, but was largely independent of spike train variability. With a combination of analytical techniques and numerical simulations using 'integrate-and-fire' neuron models we show that this relationship between output correlation and firing rate is robust to input heterogeneities. Finally, this overlooked relationship is replicated by a standard threshold-linear model, demonstrating the universality of the result. This connection between the rate and correlation of spiking activity links two fundamental features of the neural code.