STRUCT/FUNCT OF RETINAL CIRCUIT FOR SCOTOPIC LUMINANCE

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The long term goal is to determine how neural circuits in mammalian retina solve problems of signal processing. The present project concerns the circuit for night vision which is well defined. The input range spans 3-4 log units. At the low end each quantal event evokes a burst of 2-3 spikes in a ganglion cell (up to 20 events/second); above this level gain is controlled and varies inversely with mean luminance. The circuit's feedforward structure is known (1500 rods -> 100 rod bipolar -> 5 AII amacrine -> 4 b1 bipolar -> beta ganglion cell), and so are three of its feedback loops. This project addresses two questions: 1) By what mechanism does the circuit protect a quantal signal against noise? Lacking such a mechanism, the continuous dark noise from 1500 rods would tend to accumulate in the ganglion cell (as 1500) and obliterate the tiny signal. Noise might be removed by "thresholding" mechanisms at the first two stages of the circuit (where most convergence occurs). Candidate neurons to accomplish thresholding are, respectively, the rod horizontal cell and the A17 amacrine cell. 2) What is the mechanism for gain control? Candidate neurons for gain control are the rod horizontal and the interplexiform cells. To investigate these questions the project will: 1) Gather additional structural data regarding the feedback loops (measure fine features of the horizontal cell, quantitate gap junctions between AII amacrine cells, determine synaptic connections of the interplexiform cell). 2) Construct a compartmental model of each stage of the circuit (constrained by known structure and physiology). A model includes on the order of 103 neurons (104 compartments) and is constructed using a high-level language (based on "C") invented for this purpose. 3) Simulate the response of each stage at different light intensities to explore the dynamics and determine whether the mechanisms proposed for noise removal and gain control are plausible. 4) Simulate the overall circuit (constrained by results from individual stages and the known physiology of the ganglion cell) to explore whether the models of separate stages are compatible. Simulation of this multi-stage circuit, plus its several layers of feedback, should advance basic knowledge regarding the mechanisms of night vision, possibly identifying which sites in the circuit are most vulnerable to deterioration. Simulation should also help understand mechanisms that maintain stability (i.e., oppose seizures) in complex neural circuits. Also, simulating a realistic circuit with 103 neurons provides a start toward the larger scale simulations that will ultimately be needed to understand the brain.