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We present an optimized conductance-based retina microcircuit simulator whichtransforms light stimuli into a series of graded and spiking action potentialsthrough photo transduction. We use discrete retinal neuron blocks based on acollation of single-compartment models and morphologically realisticformulations, and successfully achieve a biologically real-time simulator. Thisis done by optimizing the numerical methods employed to solve the system ofover 270 nonlinear ordinary differential equations and parameters. Oursimulator includes some of the most recent advances in compartmental modelingto include five intrinsic ion currents of each cell whilst ensuring real-timeperformance, in attaining the ion-current and membrane responses of thephotoreceptor rod and cone cells, the bipolar and amacrine cells, theirlaterally connected electrical and chemical synapses, and the output ganglioncell. It exhibits dynamical retinal behavior such as spike-frequencyadaptation, rebound activation, fast-spiking, and subthreshold responsivity.Light stimuli incident at the photoreceptor rod and cone cells is modulatedthrough the system of differential equations, enabling the user to probe theneuronal response at any point in the network. This is in contrast to manyother retina encoding schemes which prefer to `black-box' the preceding stagesto the spike train output. Our simulator is made available open source, withthe hope that it will benefit neuroscientists and machine learningpractitioners in better understanding the retina sub-circuitries, how retinacells optimize the representation of visual information, and in generatinglarge datasets of biologically accurate graded and spiking responses.

A Real-Time Retinomorphic Simulator Using a Conductance-Based Discrete Neuronal Network