Abstract

Deciphering how neurons represent the external world is a fundamental goal in neuroscience. This requires identifying which features in the population response in a single trial are informative about the stimulus. Neurons can code stimuli using both space and time. Individual neurons show differential selectivity to certain stimuli across space at coarse time scales while representing others by modulating their activity at fine time scales. The information content in the population is modified from neural interactions across space and time. While this emphasizes the need to examine population responses across space and time, analyzing a population of hundreds of neurons is challenging when only a limited number of trials are available due to the high dimensionality of the joint spatiotemporal response space. We addressed this by introducing a novel method called space-by-time non-negative matrix factorization. The method describes the population activity with a low dimensional representation consisting of spatial modules, groups of neurons that are coactivated, and temporal modules, patterns that describe how these neurons modulate their spiking across time. The population activity in each trial is described by a set of coefficients, that indicate the level of activation of each spatial and temporal module in the trial. We used this method to analyze datasets from auditory, visual and somatosensory modalities. It identified physiologically meaningful spatial and temporal modules that described how each population coded stimuli in space and time. It further indicated the differential contributions of spatial and temporal dimensions for the population code. Particularly, the first spike latency was demonstrated to be informative at the population level. We refined the method to model the sub-Poisson, Poisson and supra-Poisson variability typically observed in spike counts. This refinement demonstrated enhanced capacity in identifying spatial and temporal modules from empirical data and indicated that the activity of a neural population code stimuli using multiple representations. Our findings indicate that our method is scalable to large populations of neurons and has the capacity to efficiently identify biologically meaningful and informative low dimensional representations.