Spike Metrics (original) (raw)
Related papers
Measuring representational distances–the spike-train metrics approach
2010
A fundamental problem in studying population codes is how to compare population activity patterns. Population activity patterns are not just spatial, but spatiotemporal. Thus, a principled approach to the problem of the comparison of population activity patterns begins with the comparison of the temporal activity patterns of a single neuron, and then, to the extension of the scope of this comparison to populations spread across space.
Current Opinion in Neurobiology, 2005
Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus, particularly appropriate for extracellularly recorded neural signals. The spike metric approach can be extended to multineuronal recordings, mitigating the 'curse of dimensionality' typically associated with analyses of multivariate data. Spike metrics have been usefully applied to the analysis of neural coding in a variety of systems, including vision, audition, olfaction, taste and electric sense.
A New Multineuron Spike Train Metric
Neural Computation, 2008
The Victor-Purpura spike-train metric has recently been extended to a family of multi-neuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The Victor-Purpura metric is one of the two metrics commonly used for quantifying the distance between two spike trains, the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multi-neuron metric. We believe this gives a metric which is both
Metric-space analysis of spike trains: theory, algorithms and application
Network: Computation in Neural Systems, 1997
We present the mathematical basis of a new approach to the analysis of temporal coding. The foundation of the approach is the construction of several families of novel distances (metrics) between neuronal impulse trains. In contrast to most previous approaches to the analysis of temporal coding, the present approach does not attempt to embed impulse trains in a vector space, and does not assume a Euclidean notion of distance. Rather, the proposed metrics formalize physiologically based hypotheses for those aspects of the firing pattern that might be stimulus dependent, and make essential use of the point-process nature of neural discharges. We show that these families of metrics endow the space of impulse trains with related but inequivalent topological structures. We demonstrate how these metrics can be used to determine whether a set of observed responses has a stimulus-dependent temporal structure without a vector-space embedding. We show how multidimensional scaling can be used to assess the similarity of these metrics to Euclidean distances. For two of these families of metrics (one based on spike times and one based on spike intervals), we present highly efficient computational algorithms for calculating the distances. We illustrate these ideas by application to artificial data sets and to recordings from auditory and visual cortex. †
Statistical analysis and inferences on spike trains are one of the central topics in neural coding. It is of great interest to understand the underlying distribution and geometric structure of given spike train data. However, a fundamental obstacle is that the space of all spike trains is not an Euclidean space, and non-Euclidean metrics have been commonly used in the literature to characterize the variability and pattern in neural observations. Over the past few years, two Euclidean-like metrics were independently developed to measure distance in the spike train space. An important benefit of these metrics is that the spike train space will be suitable for embedding in Euclidean spaces due to their Euclidean properties. In this paper, we systematically compare these two metrics on theory, properties, and applications. Because of its Euclidean properties, one of these metrics has been further used in defining summary statistics (i.e. mean and variance) and conducting statistical inferences in the spike train space. Here we provide equivalent definitions using the other metric and show that consistent statistical inferences can be conducted. We then apply both inference frameworks in a neural coding problem for a recording in geniculate ganglion stimulated by different tastes. It is found that both frameworks achieve desirable results and provide useful new tools in statistical inferences in neural spike train space.
Journal of Neuroscience Methods, 2007
Cost-based metrics formalize notions of distance, or dissimilarity, between two spike trains, and are applicable to single-and multineuronal responses. As such, these metrics have been used to characterize neural variability and neural coding. By examining the structure of an efficient algorithm . Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. J Neurosci Methods 124(2), 175-79] implementing a metric for multineuronal responses, we determine criteria for its generalization, and identify additional efficiencies that are applicable when related dissimilarity measures are computed in parallel. The generalized algorithm provides the means to test a wide range of coding hypotheses.
The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare the pattern of responses and their differences have been used by neurophysiologist to characterize the accuracy of the sensory responses studied. Among the most widely used analysis, we note methods based on Euclidian distances or on spike metric distance such as the one proposed by van Rossum. Methods based on artificial neural network and machine learning (such as self-organizing maps) have also gain popularity to recognize and/or classify specific input patterns. In this brief report, we first compare these three strategies using dataset from 3 different sensory systems. We show that the input-weighting procedure inherent to artificial neural network allows the extraction of the information most relevant to the dis...
Objective assessment of the functional role of spike train correlations using information measures
Visual …, 2001
The functional role of correlations between neuronal spike trains remains strongly debated. This debate partly stems from the lack of a standardized analysis technique capable of accurately quantifying the role of correlations in stimulus encoding. We believe that information theoretic measures may represent an objective method for analysing the functional role of neuronal correlations. Here we show that information analysis of pairs of spike trains allows the information content present in the firing rate to be disambiguated from any extra information that may be present in the temporal relationships of the two spike trains. We validate and illustrate the method by applying it to simulated data with variable degrees of known synchrony, and by applying it to recordings from pairs of sites in the primary visual cortex of anaesthetized cats. We discuss the importance of information theoretic analysis in elucidating the neuronal mechanisms underlying object identification. Supported by a Wellcome Trust project grant to M.P. Young and M.J. Tovée. We are grateful to S. Schultz and A. Treves for many useful discussion s on information theory and for their contributions to previous collaborations .
Finding the event structure of neuronal spike trains
BMC Neuroscience, 2011
Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across Neural Computation 23, 1-40 (2011) C Massachusetts Institute of Technology P1: ZXP NECO_a_00173-Toups NECO.cls May 30, 2011 15:8 U n c o r r e c t e d P r o o f 2 J. Toups et al.
Studying spike trains using a van Rossum metric with a synapse-like filter
Journal of Computational Neuroscience, 2009
Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.