Spike Train Distance Research Articles

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus.

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

Towards a more accurate quasi-static approximation of the electric potential for neurostimulation with kilohertz-frequency sources * *This work has been partially supported by ANID Millennium Science Initiative Program through Millennium Nucleus for Applied Control and Inverse Problems NCN19-161.

Objective. Our goal was to determine the conditions for which a more precise calculation of the electric potential than the quasi-static approximation may be needed in models of electrical neurostimulation, particularly for signals with kilohertz-frequency components. Approach. We conducted a comprehensive quantitative study of the differences in nerve fiber activation and conduction block when using the quasi-static and Helmholtz approximations for the electric potential in a model of electrical neurostimulation. Main results. We first show that the potentials generated by sources of unbalanced pulses exhibit different transients as compared to those of charge-balanced pulses, and this is disregarded by the quasi-static assumption. Secondly, relative errors for current-distance curves were below 3%, while for strength-duration curves these ranged between 1%–17%, but could be improved to less than 3% across the range of pulse duration by providing a corrected quasi-static conductivity. Third, we extended our analysis to trains of pulses and reported a ‘congruence area’ below 700 Hz, where the fidelity of fiber responses is maximal for supra-threshold stimulation. Further examination of waveforms and polarities revealed similar fidelities in the congruence area, but significant differences were observed beyond this area. However, the spike-train distance revealed differences in activation patterns when comparing the response generated by each model. Finally, in simulations of conduction-block, we found that block thresholds exhibited errors above 20% for repetition rates above 10 kHz. Yet, employing a corrected value of the conductivity improved the agreement between models, with errors no greater than 8%. Significance. Our results emphasize that the quasi-static approximation cannot be naively extended to electrical stimulation with high-frequency components, and notable differences can be observed in activation patterns. As well, we introduce a methodology to obtain more precise model responses using the quasi-static approach, retaining its simplicity, which can be a valuable resource in computational neuroengineering.

Tactile Surface Roughness Categorization With Multineuron Spike Train Distance

Tactile sensing with spiking neural networks (SNNs) has attracted increasing attention in the past decades. In this article, a novel SNN framework is proposed for the tactile surface roughness categorization task. In contrast to supervised SNN methods such as ReSuMe and Tempotron that require prespecifying target spike trains, the presented method performs the classification through directly comparing the distance between multineuron spike trains. Unlike simple spike train fusion methods using average pairwise spike train distance or pooled spike train distance, the proposed method merges spike trains from different neurons with the multineuron spike train distance, which can capture the complex correlation of multiple spike trains. Specifically, the spike trains are generated via the Izhikevich neurons from tactile signals. The similarity of the multineuron spike trains is computed using the multineuron Victor–Purpura spike train distance, which can be efficiently implemented in an inductive manner. The classification can be performed by incorporating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors and the multineuron spike train distance as a similarity metric. The proposed framework is quite general, i.e., other multineuron spike train distances and spike train kernel-based methods can be readily incorporated. The effectiveness of the proposed method has been demonstrated on a tactile data set by comparing it with various feature- and spike-based methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In the soft neuromorphic implementation of biomimetic tactile sensing and the development of the tactile sensing capability in neurobotic systems, the processing and analysis of spike-like tactile signals are quite common. Inspired by human tactile perception, this article proposes a novel supervised spiking neural network method for tactile sensing tasks. The traditional methods have to prespecify target spike trains, which is still an open question. In addition, the current ways to fuse spike trains from multiple neurons are far from mature. This article tackles these two problems using spike train similarity comparison with multineuron spike train distance. The direct spike train similarity comparison avoids the need to prespecify target spike trains. The multineuron spike train distance can inherently fuse spike trains from different neurons. It is demonstrated that the proposed method is able to effectively perform classification in a tactile roughness discrimination task.

Hybrid Coding of Spatiotemporal Spike Data for a Bio-Inspired Camera

Recently, a novel bio-inspired camera was developed by mimicking the retina fovea to continuously accumulate luminance intensity and then fire spikes once the dispatch threshold is reached. In contrast to the conventional frame-based cameras and the emerging dynamic vision sensors, this spike camera has shown remarkable advantages in capturing fast-moving scenes in a frame-free manner with full texture reconstruction capabilities. However, the ultra-high temporal resolution makes the transmission or storage of the output data of spike camera (referred to as spike data) quite difficult. To address the above challenges, we propose a unified lossy spike coding framework, which exploits the motion patterns hidden in the spike data distribution to design the motion-fidelity coding modes for the first time. We investigate the spatiotemporal distribution of spike data and propose an intensity-based measurement of the spike train distance. Then, the adaptive polyhedron partitioning is proposed to deal with the spike data with different motion characteristics. Finally, the intra-/inter-polyhedron prediction with spike-time and spike-rate modes, transform and multi-layer quantization are proposed and introduced into the codec. We also construct a PKU-Spike dataset captured by the spike camera to evaluate the compression performance. The experimental results on the dataset demonstrate that the proposed approach is effective in compressing such spike data while maintaining the visual fidelity especially for high-speed scenarios.

A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover's Distance.

Neural spike train analysis methods are mainly used for understanding the temporal aspects of neural information processing. One approach is to measure the dissimilarity between the spike trains of a pair of neurons, often referred to as the spike train distance. The spike train distance has been often used to classify neuronal units with similar temporal patterns. Several methods to compute spike train distance have been developed so far. Intuitively, a desirable distance should be the shortest length between two objects. The Earth Mover’s Distance (EMD) can compute spike train distance by measuring the shortest length between two spike trains via shifting a fraction of spikes from one spike train to another. The EMD could accurately measure spike timing differences, temporal similarity, and spikes time synchrony. It is also robust to firing rate changes. Victor and Purpura (1996) distance measures the minimum cost between two spike trains. Although it also measures the shortest path between spike trains, its output can vary with the time-scale parameter. In contrast, the EMD measures distance in a unique way by calculating the genuine shortest length between spike trains. The EMD also outperforms other existing spike train distance methods in measuring various aspects of the temporal characteristics of spike trains and in robustness to firing rate changes. The EMD can effectively measure the shortest length between spike trains without being considerably affected by the overall firing rate difference between them. Hence, it is suitable for pure temporal coding exclusively, which is a predominant premise underlying the present study.

Topological exploration of artificial neuronal network dynamics.

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics.We propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike train distances systematically improves the performance of our method.

Non-parametric Physiological Classification of Retinal Ganglion Cells in the Mouse Retina.

Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1,000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities.

Using spike train distances to identify the most discriminative neuronal subpopulation

BackgroundSpike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses. New methodFor the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best. ResultsThe best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease. Comparison with existing methodsSpike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary. ConclusionsThe spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.

Processing of interaural phase differences in components of harmonic and mistuned complexes in the inferior colliculus of the Mongolian gerbil.

Harmonicity and spatial location provide eminent cues for the perceptual grouping of sounds. In general, harmonicity is a strong grouping cue. In contrast, spatial cues such as interaural phase or time difference provide for strong grouping of stimulus sequences but weak grouping for simultaneously presented sounds. By studying the neuronal basis underlying the interaction of these cues in processing simultaneous sounds using van Rossum spike train distance measures, we aim at explaining the interaction observed in psychophysical experiments. Responses to interaural phase differences imposed on single components of harmonic and mistuned complex tones as well as noise delay functions were recorded as multiunit responses from the inferior colliculus of Mongolian gerbils. Results revealed a better representation of interaural phase differences if imposed on a harmonic rather than a mistuned frequency component of a complex tone. The representation of interaural phase differences was better for long integration-time windows approximately reflecting firing rates rather than short integration-time windows reflecting the temporal pattern of the stimulus-driven response. We found only a weak impact of interaural phase differences if combined with mistuning of a component in a harmonic tone complex.

A spike train distance-based method to evaluate the response of mechanoreceptive afferents

Spike train distances have gained increasing attention in the neuroscience community and provided an important tool to quantify the similarity between spike trains. A number of comparisons of the spike train distances have been carried out and mainly focused on the discriminative or clustering ability of the spike train distance. This paper proposes a spike train distance-based method to compare repeatability and linearity of mechanoreceptive afferents. The compared spike train distances include both parameter-dependent and parameter-free distances. We examined these two features on the response of mechanoreceptive afferents under the sinusoidal stimuli. We demonstrated that the parameter-dependent spike train distances (i.e., the Victor–Purpura distance and the van Rossum distance) consistently outperform the parameter-free ones (i.e., the ISI distance, the SPIKE distance, and the Event-Synchronization distance) in terms of repeatability and linearity of mechanoreceptive afferents.

Which spike train distance is most suitable for distinguishing rate and temporal coding?

BackgroundIt is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. New methodWe use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? ResultsSpike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. Comparison with existing methodsWhile our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. ConclusionsThe most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data.

Measuring spike timing distance in the Hindmarsh-Rose neurons.

In the present paper, a simple spike timing distance is defined which can be used to measure the degree of synchronization with the information only encoded in the precise timing of the spike trains. Via calculating the spike timing distance defined in this paper, the spike train similarity of uncoupled Hindmarsh-Rose neurons in bursting or spiking states with different initial conditions is investigated and the results are compared with other spike train distance measures. Later, the spike timing distance measure is applied to study the synchronization of coupled or common noise-stimulated neurons. Counterintuitively, the addition of weak coupling or common noise doesn't enhance the degree of synchronization although after critical values, both of them can induce complete synchronizations. More interestingly, the common noise plays opposite roles for weak and strong enough couplings. Finally, it should be noted that the measure defined in this paper can be extended to measure large neuronal ensembles and the lag synchronization.

Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains.

The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

Recognizing tactile surface roughness with a biomimetic fingertip: A soft neuromorphic approach

Surface roughness is an important object property and can significantly affect the friction characteristics, wear resistance, and fatigue life of components. Although some work has been done on demonstrating the capability of specific tactile sensors for surface roughness discrimination, the soft neuromorphic approach by taking inspirations from neuroscience for tactile surface roughness discrimination is exceptionally rare. This paper aims to fill this gap, and presents a soft neuromorphic method for tactile surface roughness discrimination with a biomimetic fingertip. The analog tactile signals generated from polyvinylidene difluoride (PVDF) films are fed as input to the Izhikevich neurons in order to obtain spike trains. Two distinct decoding schemes based on k-nearest neighbors (kNN) in both spike feature space and spike train space are used for surface roughness discrimination. We thoroughly examined the different spike train distance based kNN (STD-kNN) algorithms for decoding spike trains. Eight standard rough surfaces with roughness values (Ra) of 50μm, 25μm, 12.5μm, 6.3μm 3.2μm, 1.6μm, 0.8μm, and 0.4μm are explored. The highest classification accuracy of (77.6±13.7) % can be achieved with kNN (k=11) classifier and the Victor−Purpura distance (q=0.024ms−1).

Spike train SIMilarity Space (SSIMS): a framework for single neuron and ensemble data analysis.

Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large-scale ensemble activity beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how spike train SIMilarity space (SSIMS) analysis captures the relationship between goal directions for an eight-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

Using spike train distances to identify the most discriminative neuronal subpopulation

During the last decade spike train distances [1-4] have become an essential means to characterize neural coding in a wide range of neurophysiological contexts. In a typical setup different stimuli are presented repeatedly and spike train distances are used to carry out a pairwise similarity analysis in order to evaluate whether responses to the same stimulus exhibit smaller distances than responses to different stimuli. With the increasing availability of multi-neuron recordings these kinds of analyses can now be performed not just for individual neurons but rather for simultaneously recorded neuronal populations. Recently, the bivariate Victor-Purpura and the van Rossum distances [1,2] have been extended to quantify dissimilarities between multi-unit responses. These new population measures [5,6] have been designed such that they can estimate the discrimination performance of either the population as a whole (summed population”) or of individual neurons (labelled line”) or of interpolations between these two extremes. However, the question that these approaches fail to answer is the following: In cases the two extremes fail, which subpopulation within the larger population discriminates the presented stimuli best? In this study we thus follow a complimentary approach and present an algorithm which addresses exactly this question. The brute-force approach of calculating the pairwise distance matrices and the stimulus discrimination performance for every possible neuronal subpopulation is not feasible even for moderate numbers of neurons. Instead, our algorithm relies on an iterative scheme which considerably restricts the number of subpopulations for which the pairwise distance matrices and the stimulus discrimination performance actually have to be calculated. The spike train distance that we use to evaluate whether the responses elicited by different stimuli can be distinguished is the SPIKE-distance [4,7]. Whereas distances like the Victor-Purpura or the van Rossum spike train distance rely on a time-scale parameter, the SPIKE-distance is parameter-free and time-scale independent. This allows for easy comparability of results obtained for vastly different firing rates (which depend on the size of the pooled subpopulation). In contrast to the ISI-distance [3] it is sensitive to spike timing which is an important property that will be needed in some of the setups used.

Spike train distance metric reveals plasticity in discrimination of salient calls by putative excitatory cells of the auditory cortex

Despite significant progress in recent years in characterizing the response properties of neurons in the auditory cortex, the exact role that auditory cortical activity plays in sound processing remains highly debated. One hypothesized function is to improve the detection, discrimination or categorization of sounds that are behaviorally salient. We explore this possibility in a mouse model using species-specific ultrasonic calls. Single unit cortical recordings were taken in awake, head-restrained virgin and mother mice during playback of a variety of conspecific pup and adult calls. Previously [1], we had uncovered plasticity in the inhibition evoked by pup calls as they gained salience, observing changes from virgin to mother mice, the latter which recognize the importance of pup calls. Qualitatively, recorded putative excitatory neurons (based on spike waveform) also appeared different between animal groups in the call-evoked spike rasters. Here, we apply a novel normalization of spike train distances to quantify these differences and demonstrate plasticity in the functional call processing from evoked excitation. We used the Van Rossum and Victora-Purpura distance metrics to quantify differences between the “collapsed” spike trains (i.e. pooled across trials) evoked by different calls. We noticed that the magnitude of these distances were often dominated by overall evoked spike rate, which complicated comparisons across units with different degrees of call responsiveness. Hence, to obtain a clearer picture of changes between animal groups regardless of overall responsiveness (which was variable), we created for the distance between any pair of calls, a normalized spike distance between their “collapsed” spike trains. Specifically, the usual distance was divided by the average of the distance between spontaneous spike trains and those evoked by each call, thus quantifying average responsiveness to calls in terms of evoked spike train distances from silence. Our results continue to support the idea that the contrast in neural population encoding for these calls across core auditory cortical fields has improved. As in the case of evoked inhibition, the most pronounced changes arise in the lateral-band areas not normally tuned for the ultrasonic frequencies of the calls studied (See Fig. ​Fig.1).1). Taken together, this leads us to hypothesize that acquiring recognition of a class of vocalizations is correlated with refinement in the core auditory cortical representation of these sounds. Figure 1 Normalized Victor-Purpura distance between adult and pup calls of equivalent frequency, duration, amplitude, and initial frequency sweep. For each group of animals, these values were averaged over all single units from the Primary and Anterior Auditory ...

An information-geometric framework for statistical inferences in the neural spike train space

Statistical inferences are essentially important in analyzing neural spike trains in computational neuroscience. Current approaches have followed a general inference paradigm where a parametric probability model is often used to characterize the temporal evolution of the underlying stochastic processes. To directly capture the overall variability and distribution in the space of the spike trains, we focus on a data-driven approach where statistics are defined and computed in the function space in which spike trains are viewed as individual points. To this end, we at first develop a parametrized family of metrics that takes into account different warpings in the time domain and generalizes several currently used spike train distances. These new metrics are essentially penalized L ( p ) norms, involving appropriate functions of spike trains, with penalties associated with time-warping. The notions of means and variances of spike trains are then defined based on the new metrics when p = 2 (corresponding to the "Euclidean distance"). Using some restrictive conditions, we present an efficient recursive algorithm, termed Matching-Minimization algorithm, to compute the sample mean of a set of spike trains with arbitrary numbers of spikes. The proposed metrics as well as the mean spike trains are demonstrated using simulations as well as an experimental recording from the motor cortex. It is found that all these methods achieve desirable performance and the results support the success of this novel framework.

What can spike train distances tell us about the neural code?

Abstract Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code. We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code.

Studying the precision of temporal neural code: some limitations of spike train distances

Since the introduction of a family of cost-based distancesbetween spike trains by Victor and Purpura [1,2], spiketrain distances are widely used to study the precision ofthe temporal coding of neurons (see [3] for a review).Subsequently, other distances that act on convolutedspike trains have been proposed [4,5]. All of these dis-tances involve the choice of a parameter determining thetime scale at which the spike trains are compared. In gen-eral, the extent of the stimulus-specific clustering manifestin the distances between spike trains of a neuron firing inresponse to a set of trials of different stimuli is used toquantify the mutual information between the stimuli andthe spike trains in dependence of the respective parame-ter. The time scale for which the maximum mutual infor-mation is obtained serves as an indicator about theprecision of the temporal coding.Here we discuss limitations of the use of spike train dis-tances as well as principal shortcomings of the mutualinformation maximization procedure for the study ofneural coding. The implementation of this whole proce-dure relies on some initial implicit assumptions about theactual neural code: the single neuron is assumed as thecoding unit, the stimuli are conjectured to be the onesencoded by this neuron and the existence of an optimumtemporal precision is taken for granted. However, sincethe calculation of the spike train distances and the maxi-mization of mutual information are always possible andwill always provide an optimal time scale even if theseassumptions are incompatible with the actual code, naiveapplications can lead to wrong conclusions.To demonstrate the dangers of such implicit assumptions,we simulate examples in which the relation between stim-uli and spike trains is known and study the performanceof the techniques recovering the actual coding when theassumptions above are not justifiable. In particular, weconsider the non-existence of a unique time scale simulat-ing spike trains with bimodal or non-stationary firingrates. For this case we also compare the use of the timescale parametric distances with a scale adaptive distance[6]. Furthermore we show that misinterpretations can becaused by concluding a precision for a temporal codefrom neurons which in fact participate in a populationcoding. We also show that false conclusions can be drawndue to an incorrect identification of the stimulus relevantfor the neural code.