Natural movies evoke spike trains with low spike time variability in cat primary visual cortex

Neuronal responses in primary visual cortex have been found to be highly variable. This has led to the widespread notion that neuronal responses have to be averaged over large numbers of neurons to obtain suitably invariant responses that can be used to reliably encode or represent external stimuli. However, it is possible that the high variability of neuronal responses may result from the use of simple, artificial stimuli and that the visual cortex may respond differently to dynamic, naturalistic images. To investigate this question, we recorded the responses of primary visual cortical neurons in the anesthetized cat under stimulation with time-varying natural movies. We found that cortical neurons on the whole exhibited a high degree of spike count variability, but a surprisingly low degree of spike time variability. The spike count variability was further reduced when all but the first spike in a burst were removed. We also found that responses exhibiting low spike time variability exhibited low spike count variability, suggesting that rate coding and temporal coding might be more compatible than previously thought. In addition, we found the spike time variability to be significantly lower when stimulated by natural movies as compared with stimulation using drifting gratings. Our results indicate that response variability in primary visual cortex is stimulus dependent and significantly lower than previous measurements have indicated.     

STD-Dependent and Independent Encoding of Input Irregularity as Spike Rate in a Computational Model of a Cerebellar Nucleus Neuron

Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.     

Analysis of phase-locked oscillations in multi-channel single-unit spike activity with wavelet cross-spectrum

Electrophysiological measures of neural activity frequently display oscillatory patterns at various frequencies. Furthermore, these oscillatory patterns can become dynamically synchronized across a wide region of the brain in a task-dependent manner. In this study, phase-locked oscillations in simultaneously recorded spike trains were analyzed using the wavelet cross-spectrum. Adaptation of the existent methods of calculating wavelet cross-spectrum to spike train data was straightforward. In contrast, new methods were needed for evaluating the statistical significance of the cross-spectrum. Although a permutation test based on a large number of re-sampled cross-spectra can provide a reliable estimate of statistical significance, this was quite time-consuming. As an alternative, statistical significance was determined with a normal probability density function estimated from a small number of re-sampled cross-spectra. When applied to neuron pairs recorded in the primate supplementary motor area, the re-sampling procedure produced a reliable outcome even when it was based on as few as ten re-sampled cross-spectra. These results suggest that the wavelet analysis in combination with a re-sampling procedure provides a useful tool to examine the dynamic patterns of temporal correlation in cortical spike trains.     

Time-resolved and time-scale adaptive measures of spike train synchrony

A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data.     

Ascertaining neuron importance by information theoretical analysis in motor Brain–Machine Interfaces

Point process modeling of neural spike recordings has the potential to capture with high specificity the information contained in spike time occurrence. In Brain–Machine Interfaces (BMIs) the neural tuning characteristic assessed from neural spike recordings can distinguish neuron importance in terms of its modulation with the movement task. Consequently, it improves generalization and reduces significantly computation in previous decoding algorithms, where models reconstruct the kinematics from recorded activities of hundreds of neurons. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the important neuron subsets for point process decoding on BMI. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performance using subset selection is studied with respect to different number of neurons and compared to the one by the full neuron ensemble. With much less computation, the extracted importance neurons provide comparable kinematic reconstructions compared to the full neuron ensemble. The performance of the extracted subset is compared to the random selected subset with same number of neurons to further validate the effectiveness of the subset-extraction approach.     

Favored patterns in spontaneous spike trains.

By using the modified detection method, favored patterns can be detected in a total of 44 spontaneous spike trains. Among these the 'periodical burst' discharge of one sympathetic preganglionic neuron and the 'fast-slow' alternative discharge of some hypothalamic neurons have visible characteristics, hence we use them to test the reliability of our method by comparing the detected patterns with the non-sequential interval histograms and oscillograms of the spike trains. The comparisons show that our method is reliable. The spike trains of nucleus raphe magnus (NRM) and the locus coeruleus (LC) have no visible characteristics; from these the following results have been observed: (1) all spike trains have one or more favored patterns; (2) some spike trains from neurons in the same nucleus have common fragments of favored patterns; (3) the favored patterns in spike trains recorded from different nuclei are different from each other; (4) some favored patterns in spike trains of the NRM neurons remain unchanged from beginning to end in 35-min records and their repetitions are relatively stable; and (5) microinjection of normal saline or normal serum into the LC has no significant influence on the occurrence of favored patterns in 35-min records of spike trains of the LC neurons. The above results indicate that the favored patterns in spike trains are objective and regular phenomena with relative stability. It seems likely that favored pattern may be used (as an index of the neuronal activity) in combination with the microinjection technique, etc., for various studies including studies on neural coding.     

Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory

A hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in PFC during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently, this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque PFC during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance.SIGNIFICANCE STATEMENT There are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation, and experimental data analysis to test empirically these two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.     

Trial-by-trial estimation of amplitude and latency variability in neuronal spike trains

The rate function underlying single-trial spike trains can vary from trial to trial. We propose to estimate the amplitude and latency variability in single-trial neuronal spike trains on a trial-by-trial basis. The firing rate over a trial is modeled by a family of rate profiles with trial-invariant waveform and trial-dependent amplitude scaling factors and latency shifts. Using a Bayesian inference framework we derive an iterative fixed-point algorithm from which the single-trial amplitude scaling factors and latency shifts are estimated. We test the performance of the algorithm on simulated data and then apply it to actual neuronal recordings from the sensorimotor cortex of the monkey.     

Monitoring of free calcium in the neuronal endoplasmic reticulum: an overview of modern approaches

This paper describes an improved spike triggered averaging technique for the assessment of control properties and conduction velocity (CV) of single motor units (MUs) of the tibialis anterior muscle during voluntary muscle contractions. The method is based on the detection of multi-channel surface EMG signals (with linear electrode arrays) and intramuscularly recorded single MU action potentials (MUAPs). Intramuscular electrodes were inserted in the muscle taking into account the MU structural properties (innervation zone, tendon locations, length of the fibers), assessed by the linear array surface EMG detection technique. An algorithm for intramuscular EMG signal decomposition is used to identify single MUAP trains. The MUAPs detected by the intramuscular EMG decomposition algorithm were used to trigger and average the multi-channel EMG signals. CV of single averaged surface MUAPs was estimated by the use of advanced signal processing methods based on multi-channel recordings which allow to consistently reduce the variance of CV estimates compared with traditional two channel delay estimators. The number of averaged potentials can thus be limited, resulting in high temporal resolution CV estimates. The developed technique was tested on recordings from the tibialis anterior muscle in 11 volunteers during fatigue. It was shown that the method allows the assessment of single MU CV changes (fatigue) as small as 0.1 m/s with less than 2 s data epochs. The method allows reliable assessment of firing rate and conduction properties of single MUs with applications for the investigation of central and peripheral fatigue mechanisms.     

Irregularity in neocortical spike trains: influence of measurement factors and another method of estimation

Irregularity in the interspike interval is a common phenomenon especially in the neocortex. A measure of this random variation in the spacing between neuronal spikes is usually obtained with the coefficient of variation CV (standard deviation/mean interspike interval). In excitable cells, the standard deviation in the interspike interval can be large and the mean firing rate often fluctuates. As a result, there can be substantial variability in the value of the CV computed for the same spike train using only slightly different samples as we show. Moreover, these CV values can be comparatively meaningless unless certain conditions are met. In doing so some researchers have selectively sampled data over a stable mean while others have used a wide range of trial times or subsets thereof (capture window) to compute the CV. This has made interpretation of the raw CV cumbersome. We demonstrate that the CV has a triple sensitivity, namely, for the size of the capture window, the spike count and the refractory period. We assuage these difficulties by introducing a modified term, the coefficient of variation proportion of maximum (CVpm) that offers transportability across different experimental conditions by compensating for the triplet.     

Multineuronal vectorization is more efficient than time-segmental vectorization for information extraction from neuronal activities in the inferior temporal cortex

In order for patients with disabilities to control assistive devices with their own neural activity, multineuronal spike trains must be efficiently decoded because only limited computational resources can be used to generate prosthetic control signals in portable real-time applications. In this study, we compare the abilities of two vectorizing procedures (multineuronal and time-segmental) to extract information from spike trains during the same total neuron-seconds. In the multineuronal vectorizing procedure, we defined a response vector whose components represented the spike counts of one to five neurons. In the time-segmental vectorizing procedure, a response vector consisted of components representing a neuron’s spike counts for one to five time-segment(s) of a response period of 1 s. Spike trains were recorded from neurons in the inferior temporal cortex of monkeys presented with visual stimuli. We examined whether the amount of information of the visual stimuli carried by these neurons differed between the two vectorizing procedures. The amount of information calculated with the multineuronal vectorizing procedure, but not the time-segmental vectorizing procedure, significantly increased with the dimensions of the response vector. We conclude that the multineuronal vectorizing procedure is superior to the time-segmental vectorizing procedure in efficiently extracting information from neuronal signals.     

Propagation of cortical synfire activity: survival probability in single trials and stability in the mean.

The synfire hypothesis states that under appropriate conditions volleys of synchronized spikes (pulse packets) can propagate through the cortical network by traveling along chains of groups of cortical neurons. Here, we present results from network simulations, taking full account of the variability in pulse packet realizations. We repeatedly stimulated a synfire chain of model neurons and estimated activity (a) and temporal jitter (sigma) of the spike response for each neuron group in the chain in many trials. The survival probability of the activity was assessed for each point in (a, sigma)-space. The results confirm and extend our earlier predictions based on single neuron properties and a deterministic state-space analysis [Diesmann, M., Gewaltig, M.-O., & Aertsen, A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature, 402, 529-533].     

Neuron synchronization in the rat gracilis nucleus facilitates sensory transmission in the somatosensory pathway

We have studied the role of the temporal correlation of multiple cell discharges in the facilitation of the somatosensory information transmission from the gracilis nucleus to the primary somatosensory (SI) cortex in anesthetized rats. Pairs of gracilis neurons or gracilis–SI cortical neurons were recorded during application of 20-ms tactile stimuli in control conditions and after electrical corticofugal stimulation. Cross-correlation of neural spike trains showed significant changes in synchronization of the neuron firing provoked by the corticofugal stimulation. To quantify the time–frequency alterations in the functional association within neuron pairs we used the wavelet coherence measure. We show that electrical stimulation of the SI cortex induces a short-lasting facilitation of tactile responses of projecting gracilis neurons if their receptive fields (RFs) overlap with the RF of the stimulated cortical area (matching condition). Moreover, synchronization of discharges of gracilis neurons with a common RF is increased by activation of the corticofugal projection. Synchronization is favored by a stimulus induced synchronous oscillatory activity of projecting neurons in the range 3–10 Hz. In the matching condition synchronous discharges in the gracilis increment the number of spikes elicited in the SI cortex. Thus the efficacy of the sensory transmission from the gracilis nucleus to the SI cortex is modulated by the corticofugal projection through two complementary mechanisms: (i) by changing the responsiveness (number of elicited spikes) of individual gracilis neurons; and (ii) by a dynamic consolidation of gracilis neurons with a common RF into microcircuits generating synchronous spikes.     

Measuring spike pattern reliability with the Lempel-Ziv-distance

Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel-Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calculating the deviation of the mean distance of spike trains obtained from multiple presentations of an identical stimulus from a Poisson reference. Using both the Lempel-Ziv-distance (LZ-distance) and a distance focussing on coincident firing, the pattern and timing reliability of neuronal firing is determined for spike data obtained along the visual information processing pathway of macaque monkey (LGN, simple and complex cells of V1, and area MT). In combination with the sequential superparamagnetic clustering algorithm, we show that the LZ-distance groups together spike trains with similar but not necessarily synchronized firing patterns. For both applications, we show how the LZ-distance gives additional insights, as it adds a new perspective on the problem of firing reliability determination and allows neuron classifications in cases, where other distance measures fail.     

Less Segregated Processing of Visual Information in V2 than in V1 of the Monkey Visual Cortex

To test the possibility of cross-talk between parallel pathways dealing with different aspects of visual information, such as orientation, direction of motion and colour in cortical area V2, we quantitatively analysed visual responses of 121 V2 cells recorded from anaesthetized and paralysed macaques and compared them with those of 147 V1 cells. A selectivity index of visual responses was calculated for each neuron, which was then classified as selective or not to a particular attribute of visual stimuli. Twenty-one percent of the V2 neurons had dual selectivity to both colour and direction of stimulus motion (C&D cells). In V1, only 5% of the cells were C&D cells. Thus, the proportion of C&D cells significantly increased from V1 to V2. We also carried out cross-correlation analysis of spike trains recorded simultaneously from pairs of V2 neurons or pairs of V1 neurons. In V2, correlated firings could be observed between cells with completely different optimal orientation, such as orthogonal, while it was never observed in V1. The cross-correlation analysis further indicated that functional interactions in V2 were more widespread than those in V1. These results suggest that neurons which have different functional properties become less segregated, and that functional interactions become more widespread in V2 than in V1.     

Central somatosensory conduction in man: neural generators and interpeak latencies of the far-field components recorded from neck and right or left scalp and earlobes

Early somatosensory evoked potential (SEP) components to median nerve or finger stimulation were recorded with non-cephalic references in normal young adults. Detailed topographic data over scalp and neck were related to anatomical observations on the actual conduction distances in dorsal column, medial lemniscus and thalamo-cortical parts of the somatosensory pathway. The extrapolation of afferent conduction velocity (CV) measured from sensory nerve potentials along the peripheral nerve to the C6-C7 spinal segments identified the spinal entry time with the onset of the neck N11 or scalp P11 (far field 2 or FF2). The first far field (FF1) is generated in the nerve proximal to axilla. The definite latency shift of the spinal negativity along the neck indicates a CV of 58 m/sec. Data about the maximal diameter of lemniscal axons in man were used to calculate a CV of 40.5 m/sec. Consideration of transit times from spinal entry to cortex and of synaptic delays clarified the arrival times of the afferent volley at various relay nuclei, and also suggested a thalamo-cortical CV of about 33 m/sec. Interpeak and onset-to-peak measures on scalp far fields suggest that FF3-FF4 are generated in medial lemniscus rather than above the thalamus. Consistent differences in amplitude, but not in wave form, were recorded at right and left earlobes for FF2 (larger ipsilaterally) and FF3-FF4 (larger contralaterally). The scalp topography of far fields was analysed in detail.     

Neural correlation is stimulus modulated by feedforward inhibitory circuitry

Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory-inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation.     

Large identified pyramidal cells in macaque motor and premotor cortex exhibit "thin spikes": implications for cell type classification

Recent studies have suggested that extracellular recordings of putative cortical interneurons have briefer spikes than those of pyramidal neurons, providing a means of identifying cortical cell types in recordings from awake monkeys. To test this, we investigated the spike duration of antidromically identified pyramidal tract neurons (PTNs) recorded from primary motor (M1) or ventral premotor cortex (area F5) in 4 awake macaque monkeys. M1 antidromic latencies (ADLs) were skewed toward short ADLs (151 PTNs; 0.5-5.5 ms, median 1.1 ms) and significantly different from that of F5 ADLs (54 PTNs; 1.0-6.9 ms, median 2.6 ms). The duration of PTN spikes, recorded with a high-pass filter of 300 Hz and measured from the negative trough to the positive peak of the spike waveform, ranged from 0.15 to 0.71 ms. Importantly, we found a positive linear correlation between ADL and spike duration in both M1 (R(2) = 0.40, p < 0.001) and F5 (R(2) = 0.57, p < 0.001). Thus PTNs with the shortest ADL (fastest axons) had the briefest spikes, and since PTN soma size is correlated with axon size and conduction velocity, it is likely that the largest pyramidal neurons (Betz cells in M1) have spikes with short durations (0.15-0.45 ms), which overlap heavily with those reported for putative interneurons in previous studies in non-primates. In summary, one class of physiologically identified cortical pyramidal neuron exhibits a wide variety of spike durations and the results suggest that spike duration alone may not be a reliable indicator of cell type.