Identifying Highresolution Spatiotemporal Components Contributing to the Fast Spiking Response Dynamics of Visual Neurons In many brain areas, responses to sensory stimuli vary due to other cognitive, motor, or task factors. In the visual system the integration of these modulatory factors and visual stimuli can change the spatiotemporal characteristics of visual neurons at various spatial and temporal scales. High resolution changes in the neuronsxe2x80x99 spatiotemporal sensitivity happening on fast timescales, however, can challenge computational models that aim to capture the neural computations underlying these fast dynamics. The timevarying visual sensitivity around the time of eye movements is an exemplar of such fast, dynamic modulatory computations. This study develops a statistical framework for identifying the highresolution spatiotemporal components of visual neurons in the middle temporal area of macaque monkeys during a rapid eye movement task. The identified components can be used in building dynamic encoding models capable of characterizing the timevarying stimulusresponse relationships with high resolutions and at the level of singletrial spiking activity. Such dynamic models with high temporal precision can be used to provide higher accuracy in the decoding of timevarying visual information from neuronal responses, which can in turn advance visual brainmachine interface systems to be able to operate robustly and with high accuracy in dynamic scenes.

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.