初级视皮层神经元响应反应与撤反应的朝向选择性

朝向选择性是初级视皮层(17区或V1)神经元的基本性质,在图形感知中起着关键作用.同时这些神经元对于持续时间大于100 ms的视觉刺激具有清晰的响应反应(Onset responses)和撤反应(Offset responses).以往的研究只关注响应反应的朝向选择性,而忽视了对撤反应的朝向选择性研究.我们比较了响应与撤反应的朝向调谐性质,大多数细胞的撤反应与响应反应基本上具有相似的最优朝向,而撤反应的朝向调谐宽度有窄于响应反应的趋势,撤反应的最优延迟普遍滞后于响应反应的最优延迟.撤反应的朝向选择性略强于响应反应和具有显著长的反应延迟提示,皮层内的反馈输入可能在形成撤反应的朝向选择性中起着作用.本研究揭示了撤反应的朝向选择性在刺激朝向的连续表征和主体在形状知觉的后期对朝向的精细区分中起着作用.     

初级视觉皮层“简单”与“复杂”神经元动力学的计算建模

本文回顾了我们在哺乳动物视觉皮层的建模工作.利用初级视觉皮层的大规模神经元网络模型,我们解释了初级视觉皮层里“简单”与“复杂”神经元现象的网络机制.所谓的“简单”细胞对视觉刺激的反应近似线性,而“复杂”细胞对视觉刺激是非线性的.我们的模型成功地再现了简单和复杂细胞分布的实验数据.     

Cell-Type-Specific Thalamocortical Inputs Constrain Direction Map Formation in Visual Cortex

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Directional tunings independent of orientation in the primary visual cortex of the cat

A family of moving ‘random-line’ patterns was developed and used to study the directional tuning of 91 single units in cat primary visual cortex (V1). The results suggest that, in addition to the well-known orientation-dependent mechanism, there is also some kind of orientationindependent mechanism underlying the direction selectivity. The directional tuning of the neurons varies in accordance with the increase of orientation or non-orientation element in the stimulus.     

Coarse-grained reduction and analysis of a network model of cortical response: I. Drifting grating stimuli

We present a reduction of a large-scale network model of visual cortex developed by McLaughlin, Shapley, Shelley, and Wielaard. The reduction is from many integrate-and-fire neurons to a spatially coarse-grained system for firing rates of neuronal subpopulations. It accounts explicitly for spatially varying architecture, ordered cortical maps (such as orientation preference) that vary regularly across the cortical layer, and disordered cortical maps (such as spatial phase preference or stochastic input conductances) that may vary widely from cortical neuron to cortical neuron. The result of the reduction is a set of nonlinear spatiotemporal integral equations for phase-averaged firing rates of neuronal subpopulations across the model cortex, derived asymptotically from the full model without the addition of any extra phenomological constants. This reduced system is used to study the response of the model to drifting grating stimuli—where it is shown to be useful for numerical investigations that reproduce, at far less computational cost, the salient features of the point-neuron network and for analytical investigations that unveil cortical mechanisms behind the responses observed in the simulations of the large-scale computational model. For example, the reduced equations clearly show (1) phase averaging as the source of the time-invariance of cortico-cortical conductances, (2) the mechanisms in the model for higher firing rates and better orientation selectivity of simple cells which are near pinwheel centers, (3) the effects of the length-scales of cortico-cortical coupling, and (4) the role of noise in improving the contrast invariance of orientation selectivity.     

States of high conductance in a large-scale model of the visual cortex

This paper reports on the consequences of large, activity dependent, synaptic conductances for neurons in a large-scale neuronal network model of the input layer 4C of the Macaque primary visual cortex (Area V1). This high conductance state accounts for experimental observations about orientation selectivity, dynamics, and response magnitude (D. McLaughlin et al. (2000) Proc. Natl. Acad. Sci. USA 97: 8087–8092), and the linear dependence of Simple cells on visual stimuli (J. Wielaard et al. (2001) J. Neuroscience 21: 5203–5211). The source of large conductances in the model can be traced to inhibitory corticocortical synapses, and the model's predictions of large conductance changes are consistent with recent intracellular measurements (L. Borg-Graham et al. (1998) Nature 393: 369–373; J. Hirsch et al. (1998) J. Neuroscience 15: 9517–9528; J.S. Anderson et al. (2000) J. Neurophysiol. 84: 909–926). During visual stimulation, these conductances are large enough that their associated time-scales become the shortest in the model cortex, even below that of synaptic interactions. One consequence of this activity driven separation of time-scales is that a neuron responds very quickly to temporal changes in its synaptic drive, with its intracellular membrane potential tracking closely an effective reversal potential composed of the instantaneous synaptic inputs. From the effective potential and large synaptic conductance, the spiking activity of a cell can be expressed in an interesting and simplified manner, with the result suggesting how accurate and smoothly graded responses are achieved in the model network. Further, since neurons in this high-conductance state respond quickly, they are also good candidates as coincidence detectors and burst transmitters.     

Direction Selectivity and Spatiotemporal Separability in Simple Cortical Cells

Simple cells in mammalian visual cortex are quasi-linear mechanisms whose behavior departs from true linearity in a very consistent manner. Empirical research on direction selectivity (DS) clearly illustrates these characteristics. A linear DS cell will be DS for all stimuli, whereas a linear non-DS cell will not be DS for any stimuli. However, many simple cells have opposite preferred directions for stimuli of reversed polarity, and some cells are DS for some stimuli (e.g., moving bars) but not for others (e.g., drifting gratings). Also, linear non-DS cells must have separable spatiotemporal receptive fields (RFs), and linear DS cells must have inseparable RFs. Yet many actual DS cells have separable RFs. Here we present a nonlinear model of simple-cell behavior that reproduces all of these empirical behaviors. The model is a variant of the current linear model, amended to include an interleaved nonlinearity (half-wave rectification) that allows it to mimic the (im)balance of push-pull mechanisms. We present simulation results showing that balanced push-pull mechanisms result in linear behavior, while imbalanced push-pull arrangements produce all of the incongruent DS-related behaviors that have been reported for simple cells.     

Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex

In the primate visual pathway, orientation tuning of neurons is first observed in the primary visual cortex. The LGN cells that comprise the thalamic input to V1 are not orientation tuned, but some V1 neurons are quite selective. Two main classes of theoretical models have been offered to explain orientation selectivity: feedforward models, in which inputs from spatially aligned LGN cells are summed together by one cortical neuron; and feedback models, in which an initial weak orientation bias due to convergent LGN input is sharpened and amplified by intracortical feedback. Recent data on the dynamics of orientation tuning, obtained by a cross-correlation technique, may help to distinguish between these classes of models. To test this possibility, we simulated the measurement of orientation tuning dynamics on various receptive field models, including a simple Hubel-Wiesel type feedforward model: a linear spatiotemporal filter followed by an integrate-and-fire spike generator. The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells. A simple feedback model of interacting cells is also considered. This model is successful in explaining the appearance of Mexican-hat orientation profiles, but other features of the data continue to be unexplained.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.     

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.     

Early Cortical Orientation Selectivity: How Fast Inhibition Decodes the Order of Spike Latencies

Following a flashed stimulus, I show that a simple neurophysiological mechanism in the primary visual system can generate orientation selectivity based on the first incoming spikes. A biological model of the lateral geniculate nucleus generates an asynchronous wave of spikes, with the most strongly activated neurons firing first. Geniculate activation leads to both the direct excitation of a cortical pyramidal cell and disynaptic feed-forward inhibition. The mechanism provides automatic gain control, so the cortical neurons respond over a wide range of stimulus contrasts. It also demonstrates the biological plausibility of a new computationally efficient neural code: latency rank order coding.     

Dynamic Processing of Nociception in Cortical Network in Conscious Rats: A Laser-evoked Field Potential Study

(1) Field potential study in conscious rats provides a convenient and effective animal model for pain mechanism and pharmacological research. However, the spatial-temporal character of nociception processing in cortex revealed by field potential technique in conscious rats remains unclear. (2) In the present study, multi-channel field potentials evoked by noxious laser stimulation applied to the hind paw of conscious rats were recorded through 12 chronically implanted skull electrodes. Independent component analysis (ICA) was used to remove possible artifacts and to extract the specific nociception-related component. (3) Two fast sharp responses and one slow blunt response were evoked by noxious laser stimulation. Systemic morphine (5 mg/kg, i.p.) preferentially attenuated the amplitude of the slow blunt response while had no significant effect on the first two sharp responses. ICA revealed that those responses came from activities of contralateral anterior parietal area, medial frontal area and posterior parietal area. A movement artifact was also detected in this study. Partial directed coherence (PDC) analysis showed that there were changes of information flows from medial frontal and posterior parietal area to anterior parietal area after noxious laser stimulation. (4) Characterization of the spatio-temporal responses to noxious laser stimulation may be a valuable model for the study of pain mechanisms and for the assessment of analgesia.     

Comparative study on direction selectivity and functional organization of the primary visual cortical cells in monkeys and cats

Although the directionally selective cells in many visual cortical areas are organized in columnar manner, the functional organization of direction selectivity of area Vl in the monkey still remains unclear. We quantitatively studied the proportion of directionally selective cells, direction selectivity and the functional organization of the striate cortical cells in the monkey and compared those with the cat. The results show that the direction selectivity and directional organization of striate cortical cells in the monkey are significantly weaker than those in the cat, suggesting that the species difference between the two kinds of animal is related to their different anatomic pathways.     

The Possible Role of Spike Patterns in Cortical Information Processing

When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.     

Emergent Oscillations in a Realistic Network: The Role of Inhibition and the Effect of the Spatiotemporal Distribution of the Input

We have simulated a network of 10,000 two-compartment cells, spatially distributed on a two-dimensional sheet; 15% of the cells were inhibitory. The input to the network was spatially delimited. Global oscillations frequently were achieved with a simple set of connectivity rules. The inhibitory neurons paced the network, whereas the excitatory neurons amplified the input, permitting oscillations at low-input intensities. Inhibitory neurons were active over a greater area than excitatory ones, forming a ring of inhibition. The oscillation frequency was modulated to some extent by the input intensity, as has been shown experimentally in the striate cortex, but predominantly by the properties of the inhibitory neurons and their connections: the membrane and synaptic time constants and the distribution of delays.In networks that showed oscillations and in those that did not, widely distributed inputs could lead to the specific recruitment of the inhibitory neurons and to near zero activity of the excitatory cells. Hence the spatial distribution of excitatory inputs could provide a means of selectively exciting or inhibiting a target network. Finally, neither the presence of oscillations nor the global spike activity provided any reliable indication of the level of excitatory output from the network.     

Nucleofection and Primary Culture of Embryonic Mouse Hippocampal and Cortical Neurons

Hippocampal and cortical neurons have been used extensively to study central nervous system (CNS) neuronal polarization, axon/dendrite outgrowth, and synapse formation and function. An advantage of culturing these neurons is that they readily polarize, forming distinctive axons and dendrites, on a two dimensional substrate at very low densities. This property has made them extremely useful for determining many aspects of neuronal development. Furthermore, by providing glial conditioning for these neurons they will continue to develop, forming functional synaptic connections and surviving for several months in culture. In this protocol we outline a technique to dissect, culture and transfect embryonic mouse hippocampal and cortical neurons. Transfection is accomplished by electroporating DNA into the neurons before plating via nucleofection. This protocol has the advantage of expressing fluorescently-tagged fusion proteins early in development (~4-8hrs after plating) to study the dynamics and function of proteins during polarization, axon outgrowth and branching. We have also discovered that this single transfection before plating maintains fluorescently-tagged fusion protein expression at levels appropriate for imaging throughout the lifetime of the neuron (> 2 months in culture). Thus, this methodology is useful for studying protein localization and function throughout CNS development with little or no disruption of neuronal function.