Improved Similarity Measures for Small Sets of Spike Trains

Naud, Richard; Gerhard, Felipe; Mensi, Skander; Gerstner, Wulfram

doi:10.1162/neco_a_00208

Cited by 36 publications

(49 citation statements)

References 83 publications

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“…To validate our model, we quantified its predictive power using a similarity measure denoted M d * (Online Methods and ref. 35). On average, GLIF-L was able to predict more than 80% of the spikes (M d * = 0.807, s.d.…”

Section: -3 Npgmentioning

confidence: 99%

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Temporal whitening by power-law adaptation in neocortical neurons

et al. 2013

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Neural signaling requires a large amount of metabolic energy 1 . Consequently, neurons are thought to communicate using efficient codes in which redundant information is discarded 2 . Theories of efficient coding 3 successfully predict several features of sensory systems. At early stages of visual processing, inputs coming from the external world are decorrelated in both space and time [4][5][6][7] ; through sensory adaptation 8 , codes are dynamically modified so as to maximize information transmission [9][10][11][12] ; and sensory adaptation on multiple timescales 11,13,14 could possibly reflect the statistics of the external world 15 .Sensory adaptation is at least partially a result of intrinsic properties of individual neurons and, in particular, of SFA. SFA is not only observed at the early stages of sensory processing, but is also widespread in cortical neurons embedded in highly recurrent networks. Often modeled by a single process with one specific timescale 16,17 , SFA also occurs on multiple timescales [18][19][20] . In pyramidal neurons of the rat somatosensory cortex, three or more processing steps away from sensory receptors, SFA is scale free 21 , meaning that the effective speed at which individual neurons adapt is not fixed but depends on the input. Scale-free adaptation can be captured by simple threshold models with a power law-decaying spike-triggered process 22 that possibly describes the combined action of Na + -channel inactivation [23][24][25] and ion channels mediating adaptation currents [26][27][28] .Thus, three questions arise. First, can the temporal features of spiketriggered currents and spike-triggered changes in firing threshold, possibly spanning multiple timescales, be directly extracted from experimental data? Second, can SFA be explained by these spiketriggered effects? And finally, do the timescales of SFA match the temporal statistics of the inputs received by individual neurons? If temporal characteristics of inputs and SFA were matched, SFA could lead to a perfect decorrelation of the information contained in one spike with that of the previous one of the same neuron, a phenomenon known as temporal whitening 29 . Temporal whitening in turn implies that, at a high signal-to-noise ratio (SNR), information transmission is enhanced 30 . RESULTSThe question of whether SFA is optimally designed for efficient coding can only be addressed if both the dynamics of SFA and the statistical properties of the inputs generated in biologically relevant situations are known. We used a combined theoretical and experimental approach to extract the dynamics of spike-triggered processes and SFA directly from in vitro recordings of cortical neurons. We then analyzed the synaptically driven membrane potential dynamics recorded in vivo from somatosensory neurons during active whisker sensation (data from ref. 31). Our overall goal was to study whether adaptation optimally removes the temporal correlations in the input to single neurons embedded in the highly recurrent network of the cortex.SFA i...

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Section: -3 Npgmentioning

confidence: 99%

“…35. M d * quantifies the similarity between two groups of spike trains generated by two stochastic processes and corrects the bias caused by the small number of available repetitions.…”

Section: Data Preprocessingmentioning

confidence: 99%

Temporal whitening by power-law adaptation in neocortical neurons

et al. 2013

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“…Dauwels et al introduced a stochastic event synchrony (SES), which evaluates the similarity between generative models of spike trains [5]. Naud et al explored the statistical properties of similarity measures for small numbers of spike trains [23]. Johnson et al proposed an information-theoretic distance between spike trains using the Kullback-Leibler distance between estimated rate functions [16].…”

Section: Similarity Measures Between Spike Trainsmentioning

confidence: 99%

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

Tezuka

Claramunt

2016

Journal of Artificial Intelligence and Soft Computing Research

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Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike trains (sequences of action potentials) are observed at each neuron, but the way in which these neurons are connected is unknown. This paper introduces a novel method for estimating connections between nodes using a similarity measure between sequences of event times. Specifically, a normalized positive definite kernel defined on spike trains was used. The proposed method was evaluated using synthetic and real data, by comparing with methods using transfer entropy and the Victor-Purpura distance. Synthetic data was generated using CERM (Coupled Escape-Rate Model), a model that generates various spike trains. Real data recorded from the visual cortex of an anaesthetized cat was analyzed as well. The results showed that the proposed method provides an effective way of estimating the connectivity of a network when the time sequences of events are the only available information.

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“…Alternatively, geometric approaches can be employed to measure the similarity of spike trains [34][35][36][37][38]. One way to study neural responses is to convert the spike train to a sequence of ones and zeroes, indicating the presence or absence of a spike in the corresponding time bin.…”

Section: Introductionmentioning

confidence: 99%