Background synaptic activity modulates spike train correlation

Abstract

Event Abstract Back to Event Background synaptic activity modulates spike train correlation Ashok L. Kumar1*, Maurice J. Chacron2 and Brent Doiron3 1 Carnegie Mellon University, Center for the Neural Basis of Cognition, United States 2 McGill University, Department of Physiology, Canada 3 University of Pittsburgh, Department of Mathematics, United States Sensory neurons have many synaptic inputs, not all of which directly transmit sensory information. Instead, some inputs serve to modulate the neuronal response to relevant stimuli. One example of neural modulation is that the intensity of background synaptic input determines the postsynaptic neuron's leak conductance and membrane potential variability. This in turn controls the gain of its firing rate response to an excitatory stimulus [1]. State dependent gain control in sensory neurons is necessary for proper stimulus processing in low and high contrast environments, and allows attentional state to modulate signal transfer. Most work that characterizes the modulatory influence of background activity has focused on single cell responses, but it is reasonable to expect that such activity may also modulate population responses. We study how background activity shapes correlations between pairs of neurons using simulations, a theory from non-equilibrium statistical mechanics, and data recorded from electrosensory pyramidal neurons in weakly electric fish. We find that increasing background activity can decrease the output spike train correlation of two neurons over long time scales but increase pairwise synchrony on short time scales. We study these effects in simulated integrate-and-fire neurons. Using a linear response theory [2,3], we find that changes in correlation are related to single cell response properties including gain and integration timescale. We first demonstrate these changes in correlation using pairs of model neurons receiving partially correlated conductance-based synaptic activity. The neuron pairs received low or high levels of background activity, and spike train statistics were compared for these two states. We next demonstrate similar state-dependent changes in a recurrent network of neurons in which correlations arise through coupling. Finally, we test our predictions on simultaneously recorded extracellular spike data from primary sensory nucleus in the electrosensory system of weakly electric fish. We induce a state change in the neuronal response properties by driving the animal with stimuli mimicking a prey or a communication signal [4]. Consistent with our theoretical findings, the timescale and correlation of recorded neuron pairs vary depending on these processing states. The findings connect state-dependent changes in single cell response properties, such as firing rate gain, to changes in correlated activity in a neuronal population.