  We present an optimized conductance-based retina microcircuit simulator which
transforms light stimuli into a series of graded and spiking action potentials
through photo transduction. We use discrete retinal neuron blocks based on a
collation of single-compartment models and morphologically realistic
formulations, and successfully achieve a biologically real-time simulator. This
is done by optimizing the numerical methods employed to solve the system of
over 270 nonlinear ordinary differential equations and parameters. Our
simulator includes some of the most recent advances in compartmental modeling
to include five intrinsic ion currents of each cell whilst ensuring real-time
performance, in attaining the ion-current and membrane responses of the
photoreceptor rod and cone cells, the bipolar and amacrine cells, their
laterally connected electrical and chemical synapses, and the output ganglion
cell. It exhibits dynamical retinal behavior such as spike-frequency
adaptation, rebound activation, fast-spiking, and subthreshold responsivity.
Light stimuli incident at the photoreceptor rod and cone cells is modulated
through the system of differential equations, enabling the user to probe the
neuronal response at any point in the network. This is in contrast to many
other retina encoding schemes which prefer to `black-box' the preceding stages
to the spike train output. Our simulator is made available open source, with
the hope that it will benefit neuroscientists and machine learning
practitioners in better understanding the retina sub-circuitries, how retina
cells optimize the representation of visual information, and in generating
large datasets of biologically accurate graded and spiking responses.
