Abstract
The mammalian retina encodes the visual world in action potentials generated by 20–50 functionally and anatomically-distinct types of retinal ganglion cell (RGC). Individual RGC types receive synaptic input from distinct presynaptic circuits; therefore, their responsiveness to specific features in the visual scene arises from the information encoded in synaptic input and shaped by postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing the computations reflected in RGC spike output. Therefore, we developed a statistical model, the separable Nonlinear Input Model, to characterize the excitatory and suppressive components of RGC receptive fields. We recorded RGC responses to a correlated noise (“cloud”) stimulus in an in vitro preparation of mouse retina and found that our model accurately predicted RGC responses at high spatiotemporal resolution. It identified multiple receptive fields reflecting the main excitatory and suppressive components of the response of each neuron. Significantly, our model accurately identified ON-OFF cells and distinguished their distinct ON and OFF receptive fields, and it demonstrated a diversity of suppressive receptive fields in the RGC population. In total, our method offers a rich description of RGC computation and sets a foundation for relating it to retinal circuitry.
Highlights
The early stages of sensory processing encode sensory input in a format that allows higher brain areas to extract information essential to guide behavior
The anatomy and retinal circuitry supporting the diversity of retinal ganglion cell (RGC) types has been studied in molecular[5], physiological[6], and computational detail[7,8,9], the methodology necessary to represent the diversity of retinal computations in RGC outputs is relatively anemic
The most commonly used mathematical model of retinal computation is the linear-nonlinear (LN) model, which describes the input-output transformation of the circuit in two steps: one, a linear filter that emphasizes particular spatial and temporal features in the stimulus delivered to the photoreceptors; two, a nonlinear function that captures spike generation in RGCs10
Summary
The early stages of sensory processing encode sensory input in a format that allows higher brain areas to extract information essential to guide behavior. The most commonly used mathematical model of retinal computation is the linear-nonlinear (LN) model, which describes the input-output transformation of the circuit in two steps: one, a linear filter that emphasizes particular spatial and temporal features in the stimulus delivered to the photoreceptors; two, a nonlinear function that captures spike generation in RGCs10. The NIM described detailed spatiotemporal RF maps in ON, OFF, and (uniquely) ON-OFF RGCs in the mammalian retina, and it revealed suppressive RFs unobserved in standard LN analyses Such detail provides a much fuller picture of the computations represented in RGC outputs and provides the means to understand their functional diversity
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