  The characterization of network and biophysical properties from neural
spiking activity is an important goal in neuroscience. A framework that
provides unbiased inference on causal synaptic interaction and single neural
properties has been missing. Here we applied the stochastic dynamics extension
of Maximum Entropy -- the Maximum Caliber Principle -- to infer the transition
rates of network states. Effective synaptic coupling strength and neuronal
response functions for various network motifs can then be computed. The
inferred minimal model also enables leading-order reconstruction of inter-spike
interval distribution. Our method is tested with numerical simulated spiking
networks and applied to data from salamander retina.
