Maximum likelihood parameter estimation of a spiking silicon neuron

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. The desired neural output is achieved through the use of complex neural models with multiple parameters which need to be tuned - a time consuming and difficult task. Silicon provides an attractive medium in which to implement these models. However due to fabrication imperfections the task of parameter configuration becomes even more complex. We show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neurons output to desired spike times. On average the interspike intervals of the predicted spike times match the desired interspike intervals to within 4% of the desired interspike interval.