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.

Spike Train kernels for multiple neuron recordings

“…For r ∈ [0, 1000], J(r) was monotonically increasing and was maximized at r = 1000. As can be seen from Equation 5, the large value of r means the lower order terms of the polynomial are relatively larger than the higher order terms. This indicates that the higher order terms are less important, and the mixture kernel is sufficient at least for this data set.…”

“…In machine learning, this is called a classification task, and it can be performed when a distance is defined among observed data, in this case, spike trains. Thus, much work has been devoted to defining appropriate distances between spike trains [24,12,16,5]. It has been pointed out, however, that rather than looking at distances between spike trains, considering their kernels could be more productive [17,18].…”

Measures of spike train synchrony for data with multiple time scales

“…One drawback of these measures is the fixed time scale, since it sets a boundary between rate and time coding for the whole recording. However, for real data which typically contain many time scales (such as regular spiking and bursts), this is difficult to detect with a measure that is mainly sensitive to only one of them (Chicharro et al, 2011). …”

Impaired homeostatic regulation of feedback inhibition associated with system deficiency to detect fluctuation in stimulus intensity: a simulation study

“…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.…”