“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 14559 |
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The accurate prediction of how spiking of a presynaptic neuron affects spike timing of a postsynaptic neuron in vivo has significant importance in a variety of questions in Neuroscience. An exact solution for this problem under conditions resembling in vivo, however, is lacking due to the nonlinearity of the neuron's spike generation mechanism. Neural activity in vivo exhibits significant variability. It is suggested that this variability reflects neuronal activity near the threshold regime, where small fluctuations of presynaptic neurons can significantly affect postsynaptic spike-timing. Here, we analytically investigate impact of a signaling input on a leaky integrate-and-fire neuron that receives background noise at the threshold regime. The signaling input models a synaptic or an assembly of nearly synchronous synaptic activity that conveys information while the background noise represents ongoing activity of many weak synapses. We demonstrate that, at the threshold regime, it is possible to obtain an exact solution that explains how the signaling input changes the spike-timing distribution. We then use the result to predict higher-order interactions of population activity caused by shared signaling inputs under different network architectures. This prediction allows us to uncover network architecture behind the population activity. In particular, we suggest architecture behind sparse population activity observed in monkey V1 or rat hippocampal neurons, which involves higher-order interactions. Contrary to common intuition, we find it unlikely that common inhibition causes the sparse activity; instead, we quantitatively show that the observed activity can result from local excitatory common inputs by comparing the theoretical prediction with empirical results obtained from the monkey V1 neurons.
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