Odour-plume dynamics influence the brain's olfactory code

The neural computations used to represent olfactory information in the brain have long been investigated. Recent studies in the insect antennal lobe suggest that precise temporal and/or spatial patterns of activity underlie the recognition and discrimination of different odours, and that these patterns may be strengthened by associative learning. It remains unknown, however, whether these activity patterns persist when odour intensity varies rapidly and unpredictably, as often occurs in nature. Here we show that with naturally intermittent odour stimulation, spike patterns recorded from moth antennal-lobe output neurons varied predictably with the fine-scale temporal dynamics and intensity of the odour. These data support the hypothesis that olfactory circuits compensate for contextual variations in the stimulus pattern with high temporal precision. The timing of output neuron activity is constantly modulated to reflect ongoing changes in stimulus intensity and dynamics that occur on a millisecond timescale.     

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.     

An ultra-sparse code underlies the generation of neural sequences in a songbird

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the 'grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.     

Neuronal filtering of multiplexed odour representations

Neuronal activity patterns contain information in their temporal structure, indicating that information transfer between neurons may be optimized by temporal filtering. In the zebrafish olfactory bulb, subsets of output neurons (mitral cells) engage in synchronized oscillations during odour responses, but information about odour identity is contained mostly in non-oscillatory firing rate patterns. Using optogenetic manipulations and odour stimulation, we found that firing rate responses of neurons in the posterior zone of the dorsal telencephalon (Dp), a target area homologous to olfactory cortex, were largely insensitive to oscillatory synchrony of mitral cells because passive membrane properties and synaptic currents act as low-pass filters. Nevertheless, synchrony influenced spike timing. Moreover, Dp neurons responded primarily during the decorrelated steady state of mitral cell activity patterns. Temporal filtering therefore tunes Dp neurons to components of mitral cell activity patterns that are particularly informative about precise odour identity. These results demonstrate how temporal filtering can extract specific information from multiplexed neuronal codes.     

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.     

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.     

Spike-timing-dependent synaptic modification induced by natural spike trains

The strength of the connection between two neurons can be modified by activity, in a way that depends on the timing of neuronal firing on either side of the synapse. This spike-timing-dependent plasticity (STDP) has been studied by systematically varying the intervals between pre- and postsynaptic spikes. Here we studied how STDP operates in the context of more natural spike trains. We found that in visual cortical slices the contribution of each pre-/postsynaptic spike pair to synaptic modification depends not only on the interval between the pair, but also on the timing of preceding spikes. The efficacy of each spike in synaptic modification was suppressed by the preceding spike in the same neuron, occurring within several tens of milliseconds. The direction and magnitude of synaptic modifications induced by spike patterns recorded in vivo in response to natural visual stimuli were well predicted by incorporating the suppressive inter-spike interaction within each neuron. Thus, activity-induced synaptic modification depends not only on the relative spike timing between the neurons, but also on the spiking pattern within each neuron. For natural spike trains, the timing of the first spike in each burst is dominant in synaptic modification.     

Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells

According to the temporal coding hypothesis, neurons encode information by the exact timing of spikes. An example of temporal coding is the hippocampal phase precession phenomenon, in which the timing of pyramidal cell spikes relative to the theta rhythm shows a unidirectional forward precession during spatial behaviour. Here we show that phase precession occurs in both spatial and non-spatial behaviours. We found that spike phase correlated with instantaneous discharge rate, and processed unidirectionally at high rates, regardless of behaviour. The spatial phase precession phenomenon is therefore a manifestation of a more fundamental principle governing the timing of pyramidal cell discharge. We suggest that intrinsic properties of pyramidal cells have a key role in determining spike times, and that the interplay between the magnitude of dendritic excitation and rhythmic inhibition of the somatic region is responsible for the phase assignment of spikes.     

Support for a synaptic chain model of neuronal sequence generation

In songbirds, the remarkable temporal precision of song is generated by a sparse sequence of bursts in the premotor nucleus HVC. To distinguish between two possible classes of models of neural sequence generation, we carried out intracellular recordings of HVC neurons in singing zebra finches (Taeniopygia guttata). We found that the subthreshold membrane potential is characterized by a large, rapid depolarization 5-10 ms before burst onset, consistent with a synaptically connected chain of neurons in HVC. We found no evidence for the slow membrane potential modulation predicted by models in which burst timing is controlled by subthreshold dynamics. Furthermore, bursts ride on an underlying depolarization of ～10-ms duration, probably the result of a regenerative calcium spike within HVC neurons that could facilitate the propagation of activity through a chain network with high temporal precision. Our results provide insight into the fundamental mechanisms by which neural circuits can generate complex sequential behaviours.     

Conditional modulation of spike-timing-dependent plasticity for olfactory learning

Mushroom bodies are a well-known site for associative learning in insects. Yet the precise mechanisms that underlie plasticity there and ensure their specificity remain elusive. In locusts, the synapses between the intrinsic mushroom body neurons and their postsynaptic targets obey a Hebbian spike-timing-dependent plasticity (STDP) rule. Although this property homeostatically regulates the timing of mushroom body output, its potential role in associative learning is unknown. Here we show in vivo that pre-post pairing causing STDP can, when followed by the local delivery of a reinforcement-mediating neuromodulator, specify the synapses that will undergo an associative change. At these synapses, and there only, the change is a transformation of the STDP rule itself. These results illustrate the multiple actions of STDP, including a role in associative learning, despite potential temporal dissociation between the pairings that specify synaptic modification and the delivery of reinforcement-mediating neuromodulator signals.     

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.     

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.     

Transformation from temporal to rate coding in a somatosensory thalamocortical pathway

The anatomical connections from the whiskers to the rodent somatosensory (barrel) cortex form two parallel (lemniscal and paralemniscal) pathways. It is unclear whether the paralemniscal pathway is directly involved in tactile processing, because paralemniscal neuronal responses show poor spatial resolution, labile latencies and strong dependence on cortical feedback. Here we show that the paralemniscal system can transform temporally encoded vibrissal information into a rate code. We recorded the representations of the frequency of whisker movement along the two pathways in anaesthetized rats. In response to varying stimulus frequencies, the lemniscal neurons exhibited amplitude modulations and constant latencies. In contrast, paralemniscal neurons in both thalamus and cortex coded the input frequency as changes in latency. Because the onset latencies increased and the offset latencies remained constant, the latency increments were translated into a rate code: increasing onset latencies led to lower spike counts. A thalamocortical loop that includes cortical oscillations and thalamic gating can account for these results. Thus, variable latencies and effective cortical feedback in the paralemniscal system can serve the processing of temporal sensory cues, such as those that encode object location during whisking. In contrast, fixed time locking in the lemniscal system is crucial for reliable spatial processing.     

Perception of sniff phase in mouse olfaction

Olfactory systems encode odours by which neurons respond and by when they respond. In mammals, every sniff evokes a precise, odour-specific sequence of activity across olfactory neurons. Likewise, in a variety of neural systems, ranging from sensory periphery to cognitive centres, neuronal activity is timed relative to sampling behaviour and/or internally generated oscillations. As in these neural systems, relative timing of activity may represent information in the olfactory system. However, there is no evidence that mammalian olfactory systems read such cues. To test whether mice perceive the timing of olfactory activation relative to the sniff cycle ('sniff phase'), we used optogenetics in gene-targeted mice to generate spatially constant, temporally controllable olfactory input. Here we show that mice can behaviourally report the sniff phase of optogenetically driven activation of olfactory sensory neurons. Furthermore, mice can discriminate between light-evoked inputs that are shifted in the sniff cycle by as little as 10 milliseconds, which is similar to the temporal precision of olfactory bulb odour responses. Electrophysiological recordings in the olfactory bulb of awake mice show that individual cells encode the timing of photoactivation in relation to the sniff in both the timing and the amplitude of their responses. Our work provides evidence that the mammalian olfactory system can read temporal patterns, and suggests that timing of activity relative to sampling behaviour is a potent cue that may enable accurate olfactory percepts to form quickly.     

Distributed synaptic modification in neural networks induced by patterned stimulation.

Activity-dependent changes in synaptic efficacy or connectivity are critical for the development, signal processing and learning and memory functions of the nervous system. Repetitive correlated spiking of pre- and postsynaptic neurons can induce a persistent increase or decrease in synaptic strength, depending on the timing of the pre- and postsynaptic excitation. Previous studies on such synaptic modifications have focused on synapses made by the stimulated neuron. Here we examine, in networks of cultured hippocampal neurons, whether and how localized stimulation can modify synapses that are remote from the stimulated neuron. We found that repetitive paired-pulse stimulation of a single neuron for brief periods induces persistent strengthening or weakening of specific polysynaptic pathways in a manner that depends on the interpulse interval. These changes can be accounted for by correlated pre- and postsynaptic excitation at distant synaptic sites, resulting from different transmission delays along separate pathways. Thus, through such a 'delay-line' mechanism, temporal information coded in the timing of individual spikes can be converted into and stored as spatially distributed patterns of persistent synaptic modifications in a neural network.     

基于CPLD的曼彻斯特码高速传输系统设计

介绍了一种基于复杂可编程逻辑器件 (CPLD)的曼彻斯特码高速数据传输系统设计方案。其特点是 :①将曼彻斯特编码、解码及其相关的控制逻辑集成于CPLD中 ,减小了传输系统的规模 ;②工作方式由微处理器 (MPU)编程控制 ,可以根据传输信道的特性选择不同的数据格式及传输速率 ,通用性强 ;③输入、输出采用并行方式与先进先出缓冲存储器接口 ,传输过程中无须干预 ,降低了传输系统对MPU的负载 ,有利于系统总体性能的提高。该方案特别适用于实时多任务下的高速数据采集与传输系统。     

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.     

基于伪码测距的弹射试验测试系统

提出了一套基于伪码测距的弹射试验测试系统设计方案.将GPS定位和精密授时与伪码测距相结合用于弹射试验的测试,在保证测试精度前提下,提高了数据更新率,适应了弹射试验高动态测试的要求.机载测站与地面多个基站构成测试数据链路,采用伪码测距获取原始数据,测试系统的时标和空间参考坐标由基站的GPS模块进行自动标定.事后解算处理获得定位数据.测距定位的精度受测试系统几何布局的影响,详细分析了在伪码测距误差既定的情况下,测试基站的布局对测试定位精度的影响,对测场范围内的测量误差分布进行了仿真计算,并根据误差分布给出了合理的测试基站布局.     

Asynchronous cooperative transmission scheme using linear dispersion code

The cooperative diversity schemes can effectively create a virtual antenna array for combating multipath fading in wireless channels. However, a lot of cooperative diversity schemes require perfect synchronization which is, in practice, difficult and even impossible to be realized. In this paper, we propose an asynchronous cooperative diversity scheme based on the linear dispersion code (LDC). By adding the zero padding (ZP) between linear dispersion codewords, our scheme mitigates the effect of asynchronism effectively. The length of ZP is decided by relative timing errors between different relays. Besides, an easy decoding method of our scheme is given in this paper by restructuring the stacked channel matrix.     

Organization of cell assemblies in the hippocampus

Neurons can produce action potentials with high temporal precision. A fundamental issue is whether, and how, this capability is used in information processing. According to the 'cell assembly' hypothesis, transient synchrony of anatomically distributed groups of neurons underlies processing of both external sensory input and internal cognitive mechanisms. Accordingly, neuron populations should be arranged into groups whose synchrony exceeds that predicted by common modulation by sensory input. Here we find that the spike times of hippocampal pyramidal cells can be predicted more accurately by using the spike times of simultaneously recorded neurons in addition to the animals location in space. This improvement remained when the spatial prediction was refined with a spatially dependent theta phase modulation. The time window in which spike times are best predicted from simultaneous peer activity is 10-30 ms, suggesting that cell assemblies are synchronized at this timescale. Because this temporal window matches the membrane time constant of pyramidal neurons, the period of the hippocampal gamma oscillation and the time window for synaptic plasticity, we propose that cooperative activity at this timescale is optimal for information transmission and storage in cortical circuits.