Identifying properties of pattern completion neurons in a computational model of the visual cortex

Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles’ role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair’s power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.Author summary: Neural ensembles are groups of neurons that fire together in response to stimuli. Ensembles and pattern completion may play important functional roles in cognition. For example, in the mouse visual cortex, activation of an ensemble via stimulation of just two pattern completion neurons can drive visual perception. We improved the understanding of such pattern completion properties by developing a computational model of the visual cortex that recapitulated the cortex’s structural and functional properties. We then identified ensembles in our model, repeatedly stimulated different pairs of neurons, and recorded the fraction of times that this stimulation resulted in ensemble activation. We found that neurons that strongly activated ensembles could complete patterns when the average ensemble voltage was far from the threshold. We also found that graph theory parameters could reliably predict efficient pattern completion neurons. Lastly, we developed a novel latency metric that could identify pattern completion neurons in vivo using modern imaging techniques.