Temporal and rate code analysis of responses to low-frequency components in the bird’s own song by song system neurons

Fukushima, Masayuki; Rauske, Peter L.; Margoliash, Daniel

doi:10.1007/s00359-015-1037-0

Cited by 5 publications

(5 citation statements)

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“…Besides firing rates, the temporal pattern of spike timings also carries important information about brain functions. For instance, it has been shown that temporal patterns encode the information of auditory (Machens et al, 2001;Narayan et al, 2006;Wang et al, 2007;Fukushima et al, 2015;Krause et al, 2017), gustatory (Di Lorenzo and Victor, 2003), motor (Vargas-Irwin et al, 2015), olfactory (MacLeod et al, 1998), somatosensory (Harvey et al, 2013), vestibular (Jamali et al, 2016), and visual (Mechler et al, 1998;Victor and Purpura, 1998;Reich et al, 2001;Carrillo-Reid et al, 2015) systems, as well as behavioral adaptation (Logiaco et al, 2015) and sleep (Tabuchi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, by removing rate dependence from the SPIKEdistance, Satuvuori et al (2017) proposed the RI-SPIKE-distance as a distance purely sensitive to timing. The spike train distances developed so far have been used in a number of studies for the analysis of neural firing patterns (MacLeod et al, 1998;Mechler et al, 1998;Victor and Purpura, 1998;Machens et al, 2001;Reich et al, 2001; Di Lorenzo and Victor, 2003;Narayan et al, 2006;Wang et al, 2007;Harvey et al, 2013;Fukushima et al, 2015;Logiaco et al, 2015;Vargas-Irwin et al, 2015;Jamali et al, 2016;Krause et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Sihn

Kim

2019

Front. Comput. Neurosci.

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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 timescale 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sihn

Kim

2019

Front. Comput. Neurosci.

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“…This is done under the underlying assumptions that the rate is the most important feature of the response, that the number and density of spikes is high enough to estimate it reliably and, crucially, that the timing of the individual spikes can be neglected. These assumptions hold for a wide variety of real data ( Walter and Khodakhah, 2009 , Enoka and Duchateau, 2017 ) but there are also many datasets in which this is not the case ( van Rullen and Thorpe, 2001 , Harvey et al, 2013 , Fukushima et al, 2015 ) and for which so far no reliable and feasible method of latency correction has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Latency correction in sparse neuronal spike trains

Kreuz

Federico

Cecchini

et al. 2022

Journal of Neuroscience Methods

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“…This is done under the underlying assumptions that the rate is the most important feature of the response, that the number and density of spikes is high enough to estimate it reliably and, crucially, that the timing of the individual spikes can be neglected. These assumptions hold for a wide variety of real data [19,20] but there are also many datasets in which this is not the case [21,22,23] and for which so far no reliable and feasible method of latency correction has been proposed. Therefore, here we would like to address the complementary problem of latency correction in data in which there are not that many spikes and where the relevant information is not primarily in the rate but in the timing of each individual spike.…”

Section: Introductionmentioning

confidence: 99%

Latency correction in sparse neuronal spike trains

Kreuz¹,

Federico²,

Cecchini³

et al. 2022

Preprint

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Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. New Method: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. Results: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset.Comparison with Existing Method(s): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. Conclusions: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

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Temporal and rate code analysis of responses to low-frequency components in the bird’s own song by song system neurons

Cited by 5 publications

References 58 publications

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

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

Latency correction in sparse neuronal spike trains

Latency correction in sparse neuronal spike trains

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