Biological Simulation of Scale-invariant Time Cells

Scale-invariant timing has been observed in a wide range of behavioral experiments. The scale-invariant firing property of recently described time cells provides a possible neural substrate for this behavior. Earlier work proposed a mathematical model that generates scale-invariant time cells based on neurons with long time constants and an approximation of inverse Laplace transform. This paper presents a biologically detailed network model of this mathematical algorithm. The simulations incorporate exponentially decaying persistent firing maintained by the CAN-current and a network structure given by the inverse Laplace transform to generate time cells with scaleinvariant firing rates. This model provides the first biologically detailed neural circuit for generating scale-invariant time cells.