基于多元线性逆滤波器的视顶盖神经元集群亮度信息解码研究

通过解码鸽子视顶盖(the optic tectum,OT)神经元集群亮度信息的方法,研究了OT区神经元集群编码亮度信息的神经机制。首先设计了具有瞬态闪变特性的亮度视觉刺激模式,采用微电极阵列记录了多通道的锋电位(spike)发放序列;然后提取了神经元集群的spike发放率特征,构造多元线性逆滤波器解码视觉刺激亮度;最后采用互相关和信息论的方法对解码结果进行了分析。结果表明:采用的多元线性逆滤波器有效解码了OT区神经元集群编码的亮度信息。通过对解码参数变化与解码精度的分析可知神经元集群数目13个、bin的宽度5 ms、刺激后15 ms、刺激后持续时间35 ms具有最高的解码精度。通过神经元集群中逆滤波器形态的分析发现,单神经元在亮度信息的编码过程中受周围神经元的动态调制。通过对解码精度与刺激闪变频率关系的分析,发现在刺激闪变频率19 Hz到53 Hz的范围内,重建质量较好,在刺激闪变频率为33.5 Hz时,重建质量达到最优。     

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.     

Retinal ganglion cells act largely as independent encoders

Correlated firing among neurons is widespread in the visual system. Neighbouring neurons, in areas from retina to cortex, tend to fire together more often than would be expected by chance. The importance of this correlated firing for encoding visual information is unclear and controversial. Here we examine its importance in the retina. We present the retina with natural stimuli and record the responses of its output cells, the ganglion cells. We then use information theoretic techniques to measure the amount of information about the stimuli that can be obtained from the cells under two conditions: when their correlated firing is taken into account, and when their correlated firing is ignored. We find that more than 90% of the information about the stimuli can be obtained from the cells when their correlated firing is ignored. This indicates that ganglion cells act largely independently to encode information, which greatly simplifies the problem of decoding their activity.     

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。     

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.     

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.     

A new perceptual illusion reveals mechanisms of sensory decoding

Perceptual illusions are usually thought to arise from the way sensory signals are encoded by the brain, and indeed are often used to infer the mechanisms of sensory encoding. But perceptual illusions might also result from the way the brain decodes sensory information, reflecting the strategies that optimize performance in particular tasks. In a fine discrimination task, the most accurate information comes from neurons tuned away from the discrimination boundary, and observers seem to use signals from these 'displaced' neurons to optimize their performance. We wondered whether using signals from these neurons might also bias perception. In a fine direction discrimination task using moving random-dot stimuli, we found that observers' perception of the direction of motion is indeed biased away from the boundary. This misperception can be accurately described by a decoding model that preferentially weights signals from neurons whose responses best discriminate those directions. In a coarse discrimination task, to which a different decoding rule applies, the same stimulus is not misperceived, suggesting that the illusion is a direct consequence of the decoding strategy that observers use to make fine perceptual judgments. The subjective experience of motion is therefore not mediated directly by the responses of sensory neurons, but is only developed after the responses of these neurons are decoded.     

Gaze direction controls response gain in primary visual-cortex neurons

To localize objects in space, the brain needs to combine information about the position of the stimulus on the retinae with information about the location of the eyes in their orbits. Interaction between these two types of information occurs in several cortical areas, but the role of the primary visual cortex (area V1) in this process has remained unclear. Here we show that, for half the cells recorded in area V1 of behaving monkeys, the classically described visual responses are strongly modulated by gaze direction. Specifically, we find that selectivity for horizontal retinal disparity-the difference in the position of a stimulus on each retina which relates to relative object distance-and for stimulus orientation may be present at a given gaze direction, but be absent or poorly expressed at another direction. Shifts in preferred disparity also occurred in several neurons. These neural changes were most often present at the beginning of the visual response, suggesting a feedforward gain control by eye position signals. Cortical neural processes for encoding information about the three-dimensional position of a stimulus in space therefore start as early as area V1.     

Small modulation of ongoing cortical dynamics by sensory input during natural vision

During vision, it is believed that neural activity in the primary visual cortex is predominantly driven by sensory input from the environment. However, visual cortical neurons respond to repeated presentations of the same stimulus with a high degree of variability. Although this variability has been considered to be noise owing to random spontaneous activity within the cortex, recent studies show that spontaneous activity has a highly coherent spatio-temporal structure. This raises the possibility that the pattern of this spontaneous activity may shape neural responses during natural viewing conditions to a larger extent than previously thought. Here, we examine the relationship between spontaneous activity and the response of primary visual cortical neurons to dynamic natural-scene and random-noise film images in awake, freely viewing ferrets from the time of eye opening to maturity. The correspondence between evoked neural activity and the structure of the input signal was weak in young animals, but systematically improved with age. This improvement was linked to a shift in the dynamics of spontaneous activity. At all ages including the mature animal, correlations in spontaneous neural firing were only slightly modified by visual stimulation, irrespective of the sensory input. These results suggest that in both the developing and mature visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing circuit dynamics by input signals, rather than directly reflecting the structure of the input signal itself.     

Functional specificity of local synaptic connections in neocortical networks

Neuronal connectivity is fundamental to information processing in the brain. Therefore, understanding the mechanisms of sensory processing requires uncovering how connection patterns between neurons relate to their function. On a coarse scale, long-range projections can preferentially link cortical regions with similar responses to sensory stimuli. But on the local scale, where dendrites and axons overlap substantially, the functional specificity of connections remains unknown. Here we determine synaptic connectivity between nearby layer 2/3 pyramidal neurons in vitro, the response properties of which were first characterized in mouse visual cortex in vivo. We found that connection probability was related to the similarity of visually driven neuronal activity. Neurons with the same preference for oriented stimuli connected at twice the rate of neurons with orthogonal orientation preferences. Neurons responding similarly to naturalistic stimuli formed connections at much higher rates than those with uncorrelated responses. Bidirectional synaptic connections were found more frequently between neuronal pairs with strongly correlated visual responses. Our results reveal the degree of functional specificity of local synaptic connections in the visual cortex, and point to the existence of fine-scale subnetworks dedicated to processing related sensory information.     

Short-term memory in olfactory network dynamics

Neural assemblies in a number of animal species display self-organized, synchronized oscillations in response to sensory stimuli in a variety of brain areas. In the olfactory system of insects, odour-evoked oscillatory synchronization of antennal lobe projection neurons (PNs) is superimposed on slower and stimulus-specific temporal activity patterns. Hence, each odour activates a specific and dynamic projection neuron assembly whose evolution during a stimulus is locked to the oscillation clock. Here we examine, using locusts, the changes in population dynamics of projection-neuron assemblies over repeated odour stimulations, as would occur when an animal first encounters and then repeatedly samples an odour for identification or localization. We find that the responses of these assemblies rapidly decrease in intensity, while they show a marked increase in spike time precision and inter-neuronal oscillatory coherence. Once established, this enhanced precision in the representation endures for several minutes. This change is stimulus-specific, and depends on events within the antennal lobe circuits, independent of olfactory receptor adaptation: it may thus constitute a form of sensory memory. Our results suggest that this progressive change in olfactory network dynamics serves to converge, over repeated odour samplings, on a more precise and readily classifiable odour representation, using relational information contained across neural assemblies.     

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.     

Stable propagation of synchronous spiking in cortical neural networks

The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion--presumably reflecting fluctuations in synaptic input and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.     

Feedback inhibition controls spike transfer in hybrid thalamic circuits

Sensory information reaches the cerebral cortex through the thalamus, which differentially relays this input depending on the state of arousal. Such 'gating' involves inhibition of the thalamocortical relay neurons by the reticular nucleus of the thalamus, but the underlying mechanisms are poorly understood. We reconstructed the thalamocortical circuit as an artificial and biological hybrid network in vitro. With visual input simulated as retinal cell activity, we show here that when the gain in the thalamic inhibitory feedback loop is greater than a critical value, the circuit tends towards oscillations -- and thus imposes a temporal decorrelation of retinal cell input and thalamic relay output. This results in the functional disconnection of the cortex from the sensory drive, a feature typical of sleep states. Conversely, low gain in the feedback inhibition and the action of noradrenaline, a known modulator of arousal, converge to increase input output correlation in relay neurons. Combining gain control of feedback inhibition and modulation of membrane excitability thus enables thalamic circuits to finely tune the gating of spike transmission from sensory organs to the cortex.     

A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons

Neurons in area 7a of the posterior parietal cortex of monkeys respond to both the retinal location of a visual stimulus and the position of the eyes and by combining these signals represent the spatial location of external objects. A neural network model, programmed using back-propagation learning, can decode this spatial information from area 7a neurons and accounts for their observed response properties.     

Top-down gain control of the auditory space map by gaze control circuitry in the barn owl

High-level circuits in the brain that control the direction of gaze are intimately linked with the control of visual spatial attention. Immediately before an animal directs its gaze towards a stimulus, both psychophysical sensitivity to that visual stimulus and the responsiveness of high-order neurons in the cerebral cortex that represent the stimulus increase dramatically. Equivalent effects on behavioural sensitivity and neuronal responsiveness to visual stimuli result from focal electrical microstimulation of gaze control centres in monkeys. Whether the gaze control system modulates neuronal responsiveness in sensory modalities other than vision is unknown. Here we show that electrical microstimulation applied to gaze control circuitry in the forebrain of barn owls regulates the gain of midbrain auditory responses in an attention-like manner. When the forebrain circuit was activated, midbrain responses to auditory stimuli at the location encoded by the forebrain site were enhanced and spatial selectivity was sharpened. The same stimulation suppressed responses to auditory stimuli represented at other locations in the midbrain map. Such space-specific, top-down regulation of auditory responses by gaze control circuitry in the barn owl suggests that the central nervous system uses a common strategy for dynamically regulating sensory gain that applies across modalities, brain areas and classes of vertebrate species. This approach provides a path for discovering mechanisms that underlie top-down gain control in the central nervous system.     

Efficient auditory coding

The auditory neural code must serve a wide range of auditory tasks that require great sensitivity in time and frequency and be effective over the diverse array of sounds present in natural acoustic environments. It has been suggested that sensory systems might have evolved highly efficient coding strategies to maximize the information conveyed to the brain while minimizing the required energy and neural resources. Here we show that, for natural sounds, the complete acoustic waveform can be represented efficiently with a nonlinear model based on a population spike code. In this model, idealized spikes encode the precise temporal positions and magnitudes of underlying acoustic features. We find that when the features are optimized for coding either natural sounds or speech, they show striking similarities to time-domain cochlear filter estimates, have a frequency-bandwidth dependence similar to that of auditory nerve fibres, and yield significantly greater coding efficiency than conventional signal representations. These results indicate that the auditory code might approach an information theoretic optimum and that the acoustic structure of speech might be adapted to the coding capacity of the mammalian auditory system.     

Retrospective and prospective coding for predicted reward in the sensory thalamus

Reward is important for shaping goal-directed behaviour. After stimulus-reward associative learning, an organism can assess the motivational value of the incoming stimuli on the basis of past experience (retrospective processing), and predict forthcoming rewarding events (prospective processing). The traditional role of the sensory thalamus is to relay current sensory information to cortex. Here we find that non-primary thalamic neurons respond to reward-related events in two ways. The early, phasic responses occurred shortly after the onset of the stimuli and depended on the sensory modality. Their magnitudes resisted extinction and correlated with the learning experience. The late responses gradually increased during the cue and delay periods, and peaked just before delivery of the reward. These responses were independent of sensory modality and were modulated by the value and timing of the reward. These observations provide new evidence that single thalamic neurons can code for the acquired significance of sensory stimuli in the early responses (retrospective coding) and predict upcoming reward value in the late responses (prospective coding).     

Temporal precision in the neural code and the timescales of natural vision

The timing of action potentials relative to sensory stimuli can be precise down to milliseconds in the visual system, even though the relevant timescales of natural vision are much slower. The existence of such precision contributes to a fundamental debate over the basis of the neural code and, specifically, what timescales are important for neural computation. Using recordings in the lateral geniculate nucleus, here we demonstrate that the relevant timescale of neuronal spike trains depends on the frequency content of the visual stimulus, and that 'relative', not absolute, precision is maintained both during spatially uniform white-noise visual stimuli and naturalistic movies. Using information-theoretic techniques, we demonstrate a clear role of relative precision, and show that the experimentally observed temporal structure in the neuronal response is necessary to represent accurately the more slowly changing visual world. By establishing a functional role of precision, we link visual neuron function on slow timescales to temporal structure in the response at faster timescales, and uncover a straightforward purpose of fine-timescale features of neuronal spike trains.     

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。