Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons.

ABSTRACT: Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity

SUBMITTER: Shomali SR 

PROVIDER: S-EPMC9932086 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature