balanced synaptic input shapes the correlation between neural spike trains平衡突触神经高峰列车输入形状之间的关系.pdf

Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains Ashok Litwin-Kumar1,2*, Anne-Marie M. Oswald2,3, Nathaniel N. Urban2,3, Brent Doiron2,4* 1 Program for Neural Computation, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 2 Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America, 3 Department of Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 4 Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America Abstract Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states. Citation: Litwin-Kumar A, Oswald A-MM, Urban NN, Doiron B (2011) Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains. PLoS Comput Biol 7(12): e1002305. doi:10.1371/journal.pcbi.1002305 Editor: Olaf Sporns, Indiana University, United States of America