Detecting NonRedundant Collective Activity of Neurons Brain activity is characterized by the activity of huge number of neurons that are densely connected in complex networks. Detecting how neural activity is coordinated across time and space over brain circuits is a very challenging task. In particular, ensembles of neurons may coactivate in specific patterns whose expression correlates to various stimuli, cognitive states, or behavioral outcomes. These patterns are expressed on a variety of timescales, from milliseconds to hundreds of milliseconds or seconds. Here, we extend a recentlyintroduced method for extracting stereotypical firing patterns by applying several postprocessing steps that enable the precise estimation of pattern wavefronts irrespective of the integration timescale used to detect patterns. Using electrophysiology data recorded in mouse visual cortex, we show that this approach enables a more precise estimation of the relation between firing patterns and mesoscale dynamics, reflected in the localfield potential. The method removes the redundancy and blurring generated by the convolution of spiketrains with exponentiallydecaying kernels and offers a sharper representation of the expression of firing patterns in brain data.

Multiscale relevance and informative encoding in neuronal spike trains Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric xe2x80x93 which we call multiscale relevance (MSR) xe2x80x93 to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a nonparametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freelybehaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.