Nonlinear models of the first synapse in the light-adapted fly retina

1. Randomly modulated light stimuli were used to characterize the nonlinear dynamic properties of the synapse between photoreceptors and large monopolar neurons (LMC) in the fly retina. Membrane potential fluctuations produced by constant variance contrast stimuli were recorded at eight different levels of background light intensity. 2. Representation of the photoreceptor-LMC input-output data in the form of traditional characteristic curves indicated that synaptic gain was reduced by light adaptation. However, this representation did not include the time-dependent properties of the synaptic function, which are known to be nonlinear. Therefore nonlinear systems analysis was used to characterize the synapse. 3. The responses of photoreceptors and LMCs to random light fluctuations were characterized by second-order Volterra series, with kernel estimation by the parallel cascade method. Photoreceptor responses were approximately linear, but LMC responses were clearly nonlinear. 4. Synaptic input-output relationships were measured by passing the light stimuli to LMCs through the measured photoreceptor characteristics to obtain an estimate of the synaptic input. The resulting nonlinear synaptic functions were well characterized by second-order Volterra series. They could not be modeled by a linear-nonlinear-linear cascade but were better approximated by a nonlinear-linear-nonlinear cascade. 5. These results support two possible structural models of the synapse, the first having two parallel paths for signal flow between the photoreceptor and LMC, and the second having two distinct nonlinear operations, occurring before and after chemical transmission. 6. The two models were each used to calculate the synaptic gain to a brief change in photoreceptor membrane potential. Both models predicted that synaptic gain is reduced by light adaptation.