自发和诱发神经放电序列的内在时序特性

运用Fano因子分析法,考察豚鼠听神经单纤维的自发放电序列、小鼠海马CAl区神经元的自发放电序列以及蟾蜍缝匠肌肌梭传入神经的诱发放电序列的时序特性,结果显示自发和诱发放电时间序列均存在Fano因子随计算窗口时间的增大而持续增长的特点,而原始数据的随机重排替代数据则没有这一特性,说明这些神经放电时间序列与一般的随机点过程不同,存在长时程相关性,在时序上具有某种结构特征。进一步的研究表明,这一时序结构特征可以通过将随机产生的一维正态分布序列数据,与神经放电时间序列数据进行跟随排序后而体现,提示这一特征与放电间隔的分布特点无关。     

Determination of the precision of spike timing in the visual cortex of anaesthetised cats

 Neuronal cortical spike trains contain precisely replicating patterns whose presence cannot be accounted for by chance production. A comparison of the number of triplets of spikes present two times with the number of doublets replicated three times in the same window duration gives a frequency-insensitive measure of this type of fine temporal organisation. By varying the tolerance with which such precisely replicating patterns are detected, one can evaluate the accuracy of spike timing in spike trains. In the sample of data here analysed, it was found that replicating patterns were best seen in the precision range 0.4–1.4 ms (a result evidently at variance with a simple ‘integrate and fire’ model of neurons). Surprisingly, the fine temporal structure of spike trains thus evidenced was stronger at relatively low firing rate discharges and was present in both the ‘spontaneous’ and ‘evoked’ responses. Received: 3 April 1995/Accepted in revised form: 11 July 1995     

Unitary IPSPs drive precise thalamic spiking in a circuit required for learning

Song learning in birds requires a basal ganglia-thalamo-pallial loop that contains a calyceal GABAergic synapse in the thalamus. Information processing within this circuit is critical for proper song development; however, it is unclear whether activation of the inhibitory output of the basal ganglia structure Area X can drive sustained activity in its thalamic target, the medial portion of the dorsolateral thalamic nucleus (DLM). We show that high-frequency, random activation of this GABAergic synapse can drive precisely timed firing in DLM neurons in brain slices in the absence of excitatory input. Complex IPSP trains, including spike trains recorded in vivo, drive spiking in slices with high reproducibility, even between animals. Using a simple model, we can predict much of DLM's response to natural stimulus trains. These data elucidate basic rules by which thalamic relay neurons translate IPSPs into suprathreshold output and demonstrate extrathalamic GABAergic activation of thalamus.     

大鼠初级传入纤维与脊髓背角神经元间的动作电序列的突触传递

外周感觉神经元通过动作电位序列对信号进行编码,这些动作电位序列经过突触传递最终到达脑部。但是各种脉冲序列如何通过神经元之间的化学突触进行传递依然是一个悬而未决的问题。研究了初级传入A6纤维与背角神经元之间各种动作电位序列的突触传递过程。用于刺激的规则,周期、随机脉冲序列由短簇脉冲或单个脉冲构成。定义“事件”(event)为峰峰问期(intefspike interval)小于或等于规定阈值的最长动作电位串,然后从脉冲序列中提取事件间间期(interevent interval,IEI)。用时间,IEI图与回归映射的方法分析IEI序列,结果表明在突触后输出脉冲序列中可以检测到突触前脉冲序列的主要时间结构特征,特别是在短簇脉冲作为刺激单位时。通过计算输入与输出脉冲序列的互信息,发现短簇脉冲可以更可靠地跨突触传递由输入序列携带的神经信息。这些结果表明外周输入脉冲序列的主要时间结构特征可以跨突触传递,在突触传递神经信息的过程中短簇脉冲更为有效。这一研究在从突触传递角度探索神经信息编码方面迈出了一步。     

Processing of sensory signals by a non-spiking neuron in the leech

The non-spiking neurons 151 are present as bilateral pairs in each midbody ganglion of the leech nervous system and they are electrically coupled to several motorneurons. Intracellular recordings were used to investigate how these neurons process input from the mechanosensory P neurons in isolated ganglia. Induction of spike trains (15 Hz) in single P cells evoked responses that combined depolarizing and hyperpolarizing phases in cells 151. The phasic depolarizations, transmitted through spiking interneurons, reversed at around -20 mV. The hyperpolarization had two components, both reversing at around -65 mV, and which were inhibited by strychnine (10 micromol l(-1)). The faster component was transmitted through spiking interneurons and the slower component through a direct P-151 interaction. Short trains (<400 ms) of P cell spikes (15 Hz) evoked the phasic depolarizations superimposed on the hyperpolarization, while long spike trains (>500 ms) produced a succession of depolarizations that masked the hyperpolarizing phase. The amplitude and duration of the hyperpolarization reached their maximum at the initial spikes in a train, while the depolarizations persisted throughout the duration of the stimulus train. Both phases of the response were relatively unaffected by the spike frequency (5-25 Hz). The non-spiking neurons 151 processed the sensory signals in the temporal rather than in the amplitude domain.     

Background neuronal activity in thenucleus supraopticus and its modifications under the influence of parathyroid hormone

In experiments on anesthetized cats, we found that i. v. injection of 5.0 U/kg of parathyroid hormone (PTH) results in modifications of the statistical parameters of the neuronal impulse activity in thenucleus supraopticus (SO) of the hypothalamus. Sliding frequency graphs, histograms of interspike intervals, autocorrelograms, and serial correlation coefficients were plotted and calculated before and after PTH injections; their comparison demonstrates that the hormone significantly modulates the temporal organization of spike trains generated by the neurons of this nucleus. We observed that PTH mostly activated SO neurons and diminished the level of spike grouping in their activity. The effect of PTH to a certain level depended on the initial frequency of background activity: an increase in the spiking frequency was typical of primarily dominating “low-frequency” neurons, while “high-frequency” units were mostly inhibited. The possible mechanisms of the observed modifications are discussed.     

Elimination of response latency variability in neuronal spike trains

 Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial variability. This may be reflected by the magnitude of the activity as well as by the response latency with respect to an external event, such as the onset of a sensory stimulus, or a behavioral event. Here we present a novel nonparametric method for estimating trial-by-trial differences in response latency from neuronal spike trains. The method makes use of the dynamic rate profile for each single trial and maximizes their total pairwise correlation by appropriately shifting all trials in time. The result is a new alignment of trials that largely eliminates the variability in response latency and provides a new internal trigger that is independent of experiment time. To calibrate the method, we simulated spike trains based on stochastic point processes using a parametric model for phasic response profiles. We illustrate the method by an application to simultaneous recordings from a pair of neurons in the motor cortex of a behaving monkey. It is demonstrated how the method can be used to study the temporal relation of the neuronal response to the experiment, to investigate whether neurons share the same dynamics, and to improve spike correlation analysis. Differences between this and other previously published methods are discussed. Received: 8 April 2002 / Accepted: 26 November 2002 / Published online: 7 April 2003 Correspondence to: Stefan Rotter (e-mail: rotter@biologie.uni-freiburg.de), Tel.: +49-761-2032862, Fax: +49-761-2032860 Acknowledgements. We are grateful to Alexa Riehle for providing us with the monkey data and for valuable discussions. We also thank Felix Kümmell, Hiroyuki Nakahara, and Shun-ichi Amari for helpful discussions. Partial funding was received by the Deutsche Forschungsgemeinschaft (DFG, SFB 505) and the German-Israeli Foundation (GIF). Additional support was provided by the RIKEN Brain Science Institute.     

Determination of the precision of spike timing in the visual cortex of anaesthetised cats

Neuronal cortical spike trains contain precisely replicating patterns whose presence cannot be accounted for by chance production. A comparison of the number of triplets of spikes present two times with the number of doublets replicated three times in the same window duration gives a frequency-insensitive measure of this type of fine temporal organisation. By varying the tolerance with which such precisely replicating patterns are detected, one can evaluate the accuracy of spike timing in spike trains. In the sample of data here analysed, it was found that replicating patterns were best seen in the precision range 0.4–1.4 ms (a result evidently at variance with a simple ‘integrate and fire’ model of neurons). Surprisingly, the fine temporal structure of spike trains thus evidenced was stronger at relatively low firing rate discharges and was present in both the ‘spontaneous’ and ‘evoked’ responses.     

Modifications of neuronal activity in the rat hypothalamic supraoptic nucleus after long-lasting vibrational stimulation

We analyzed the background impulse activity (BIA) generated by neurons of the rat hypothalamic supraoptic nucleus in the norm and under conditions of long-lasting vibrational stimulation (exposure 5, 10, or 15 days). Distributions of neurons by the level of regularity, dynamics of discharge trains, form of histograms of interspike intervals (ISIs), as well as distributions of neurons by the BIA frequency ranges, were studied. We also calculated the mean frequency of impulsation of the neurons under study and the coefficient of variation of ISIs. After vibrational influences, we found modifications of both the internal structure of the recorded spike trains and the mean frequency of impulsation within the entire studied group and different frequency subgroups. Neirofiziologiya/Neurophysiology, Vol. 38, No. 3, pp. 224–230, May–June, 2006.     

Temporal Features of Spike Trains in the Moth Antennal Lobe Revealed by a Comparative Time-Frequency Analysis

The discrimination of complex sensory stimuli in a noisy environment is an immense computational task. Sensory systems often encode stimulus features in a spatiotemporal fashion through the complex firing patterns of individual neurons. To identify these temporal features, we have developed an analysis that allows the comparison of statistically significant features of spike trains localized over multiple scales of time-frequency resolution. Our approach provides an original way to utilize the discrete wavelet transform to process instantaneous rate functions derived from spike trains, and select relevant wavelet coefficients through statistical analysis. Our method uncovered localized features within olfactory projection neuron (PN) responses in the moth antennal lobe coding for the presence of an odor mixture and the concentration of single component odorants, but not for compound identities. We found that odor mixtures evoked earlier responses in biphasic response type PNs compared to single components, which led to differences in the instantaneous firing rate functions with their signal power spread across multiple frequency bands (ranging from 0 to 45.71 Hz) during a time window immediately preceding behavioral response latencies observed in insects. Odor concentrations were coded in excited response type PNs both in low frequency band differences (2.86 to 5.71 Hz) during the stimulus and in the odor trace after stimulus offset in low (0 to 2.86 Hz) and high (22.86 to 45.71 Hz) frequency bands. These high frequency differences in both types of PNs could have particular relevance for recruiting cellular activity in higher brain centers such as mushroom body Kenyon cells. In contrast, neurons in the specialized pheromone-responsive area of the moth antennal lobe exhibited few stimulus-dependent differences in temporal response features. These results provide interesting insights on early insect olfactory processing and introduce a novel comparative approach for spike train analysis applicable to a variety of neuronal data sets.     

Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells

Correlation between spike trains or neurons sometimes indicates certain neural coding rules in the visual system. In this paper, the relationship between spike timing correlation and pattern correlation is discussed, and their ability to represent stimulus features is compared to examine their coding strategies not only in individual neurons but also in population. Two kinds of stimuli, natural movies and checkerboard, are used to arouse firing activities in chicken retinal ganglion cells. The spike timing correlation and pattern correlation are calculated by cross-correlation function and Lempel–Ziv distance respectively. According to the correlation values, it is demonstrated that spike trains with similar spike patterns are not necessarily concerted in firing time. Moreover, spike pattern correlation values between individual neurons’ responses reflect the difference of natural movies and checkerboard; neurons cooperate with each other with higher pattern correlation values which represent spatiotemporal correlations during response to natural movies. Spike timing does not reflect stimulus features as obvious as spike patterns, caused by their particular coding properties or physiological foundation. As a result, separating the pattern correlation out of traditional timing correlation concept uncover additional insight in neural coding.     

Encoding of Time-varying Stimuli in Populations of Cultured Neurons

We wondered whether random populations of dissociated cultured cortical neurons, despite of their lack of structure and/or regional specialization, are capable of modulating their neural activity as the effect of a time-varying stimulation – a simulated ‘sensory’ afference. More specifically, we used localized low-frequency, non-periodic trains of stimuli to simulate sensory afferences, and asked how much information about the original trains of stimuli could be extracted from the neural activity recorded at the different sites. Furthermore, motivated by the results of studies performed both in vivo and in vitro on different preparations, which suggested that isolated spikes and bursts may play different roles in coding time-varying signals, we explored the amount of such ‘sensory’ information that could be associated to these different firing modes. Finally, we asked whether and how such ‘sensory’ information is transferred from the sites of stimulation (i.e., the ‘sensory’ areas), to the other regions of the neural populations. To do this we applied stimulus reconstruction techniques and information theoretic concepts that are typically used to investigate neural coding in sensory systems. Our main results are that (1) slow variations of the rate of stimulation are coded into isolated spikes and in the time of occurrence of bursts (but not in the bursts’ temporal structure); (2) increasing the rate of stimulation has the effect of increasing the proportion of isolated spikes in the average evoked response and their importance in coding for the stimuli; and, (3) the ability to recover the time course of the pattern of stimulation is strongly related to the degree of functional connectivity between stimulation and recording sites. These observations parallel similar findings in intact nervous systems regarding the complementary roles of bursts and tonic spikes in encoding sensory information. Our results also have interesting implications in the field of neuro-robotic interfaces. In fact, the ability of populations of neurons to code information is a prerequisite for obtaining hybrid systems, in which neuronal populations are used to control external devices.     

Spatial and temporal stochastic interaction in neuronal assemblies

Summary The observation of various types of spatio-temporal correlations in spike-patterns of multiple cortical neurons has shifted attention from rate coding paradigms to computational processes based on the precise timing of spikes in neuronal ensembles. In the present work we develop the notion of “spatial” and “temporal interaction” which provides measures for statistical dependences in coupled stochastic processes like multiple unit spike trains. We show that the classical Willshaw network and Abeles’ synfire chain model both reveal a moderate spatial interaction, but only the synfire chain model reveals a positive temporal interaction, too. Systems that maximize temporal interaction are shown to be almost deterministic globally, but posses almost unpredictable firing behavior on the single unit level.     

Nonlinear principal components analysis of neuronal spike train data

Many recent approaches to decoding neural spike trains depend critically on the assumption that for low-pass filtered spike trains, the temporal structure is optimally represented by a small number of linear projections onto the data. We therefore tested this assumption of linearity by comparing a linear factor analysis technique (principal components analysis) with a nonlinear neural network based method. It is first shown that the nonlinear technique can reliably identify a neuronally plausible nonlinearity in synthetic spike trains. However, when applied to the outputs from primary visual cortical neurons, this method shows no evidence for significant temporal nonlinearities. The implications of this are discussed. Received: 29 November 1996 / Accepted in revised form: 1 July 1997     

Neural Coding with Graded Membrane Potential Changes and Spikes

The neural encoding of sensory stimuli is usually investigated for spike responses, although many neurons are known to convey information by graded membrane potential changes. We compare by model simulations how well different dynamical stimuli can be discriminated on the basis of spiking or graded responses. Although a continuously varying membrane potential contains more information than binary spike trains, we find situations where different stimuli can be better discriminated on the basis of spike responses than on the basis of graded responses. Spikes can be superior to graded membrane potential fluctuations if spikes sharpen the temporal structure of neuronal responses by amplifying fast transients of the membrane potential. Such fast membrane potential changes can be induced deterministically by the stimulus or can be due to membrane potential noise that is influenced in its statistical properties by the stimulus. The graded response mode is superior for discrimination between stimuli on a fine time scale.     

Feed-forward, feedback and lateral interactions in membrane potentials and spike trains from the visual cortex in vivo.

Neurons in the visual cortex receive input from the lateral geniculate nucleus (feed-forward), higher order visual areas (feedback) and local neurons in the surroundings (lateral interactions). Here we first briefly review the approximate timing and proportion of these three types of influences on the membrane potentials in visual areas 17, 18 and 19. Then we present original results from an independent component analysis of multiunit spike trains in the same visual areas to resolve the contribution from these three sources. We stimulated the visual cortex of the ferret with a small transient contrast square stimulus and recorded the multiunit activity in areas 17, 18 and 19 with single or multiple electrodes. The spike trains had three reproducible components having their maxima at 40, 55 and 105ms after the start of the presentation of the stimulus. The time course of the third component was significantly correlated with the population membrane potential in the supragranular layers of areas 17, 18 and 19. The first spike train component was interpreted as a feed-forward response, the second spike train component as driving the laterally spreading depolarization and the third spike train component as the firing caused by the lateral spreading- and the feedback depolarization.     

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.     

Membrane Potential Fluctuations Determine the Precision of Spike Timing and Synchronous Activity: A Model Study

It is much debated on what time scale information is encoded by neuronal spike activity. With a phenomenological model that transforms time-dependent membrane potential fluctuations into spike trains, we investigate constraints for the timing of spikes and for synchronous activity of neurons with common input. The model of spike generation has a variable threshold that depends on the time elapsed since the previous action potential and on the preceding membrane potential changes. To ensure that the model operates in a biologically meaningful range, the model was adjusted to fit the responses of a fly visual interneuron to motion stimuli. The dependence of spike timing on the membrane potential dynamics was analyzed. Fast membrane potential fluctuations are needed to trigger spikes with a high temporal precision. Slow fluctuations lead to spike activity with a rate about proportional to the membrane potential. Thus, for a given level of stochastic input, the frequency range of membrane potential fluctuations induced by a stimulus determines whether a neuron can use a rate code or a temporal code. The relationship between the steepness of membrane potential fluctuations and the timing of spikes has also implications for synchronous activity in neurons with common input. Fast membrane potential changes must be shared by the neurons to produce synchronous activity.     

Activity-dependent suppression of spontaneous spike generation in the Retzius neurons of the leech Hirudo medicinalis L.

We report on factors affecting the spontaneous firing pattern of the identified serotonin-containing Retzius neurons of the medicinal leech. Increased firing activity induced by intracellular current injection is followed by a ‘post-stimulus-depression’ (PSD) without spiking for up to 23 s. PSD duration depends both on the duration and the amplitude of the injected current and correlates inversely with the spontaneous spiking activity. In contrast to serotonin-containing neurons in mammals, serotonin release from the Retzius cells presumably does not mediate the observed spike suppression in a self-inhibitory manner since robust PSD persists after synaptic isolation. Moreover, single additional spikes elicited at specific delays after spontaneously occurring action potentials are sufficient to significantly alter the firing pattern. Since sub-threshold current injections do not affect the ongoing spiking pattern and PSD persists in synaptically isolated preparations our data suggest that PSD reflects an endogenous and ‘spike-dependent’ mechanism controlling the spiking activity of Retzius cells in a use-dependent way.