A simple coding procedure enhances a neuron's information capacity. Simon Laughlin shows that first-order interneurons in the fly's compound eye have contrast-response functions that match the cumulative probability distribution of natural scene contrasts, allowing efficient encoding of contrast fluctuations. This strategy, akin to histogram equalization in digital image processing, ensures all response levels are used with equal frequency, maximizing information transfer. The neurons' responses are adjusted to match the expected distribution of contrasts, increasing information capacity. The study compares the contrast-response functions of these neurons with natural scene contrasts, finding a sigmoidal match to the cumulative probability distribution. This suggests that the neurons use an efficient coding strategy based on information theory. The findings support Barlow's suggestion that redundancy reduction is important in neural coding. The strategy of matching input-output functions to signal distributions is equivalent to impedance matching, ensuring maximum information transfer. This coding procedure may have broader applications in nervous systems. The study was conducted using light detectors and intracellular recording techniques. The results indicate that the efficient coding of information into neurons is essential for visual processing. The findings highlight the importance of information theory in understanding neural coding.A simple coding procedure enhances a neuron's information capacity. Simon Laughlin shows that first-order interneurons in the fly's compound eye have contrast-response functions that match the cumulative probability distribution of natural scene contrasts, allowing efficient encoding of contrast fluctuations. This strategy, akin to histogram equalization in digital image processing, ensures all response levels are used with equal frequency, maximizing information transfer. The neurons' responses are adjusted to match the expected distribution of contrasts, increasing information capacity. The study compares the contrast-response functions of these neurons with natural scene contrasts, finding a sigmoidal match to the cumulative probability distribution. This suggests that the neurons use an efficient coding strategy based on information theory. The findings support Barlow's suggestion that redundancy reduction is important in neural coding. The strategy of matching input-output functions to signal distributions is equivalent to impedance matching, ensuring maximum information transfer. This coding procedure may have broader applications in nervous systems. The study was conducted using light detectors and intracellular recording techniques. The results indicate that the efficient coding of information into neurons is essential for visual processing. The findings highlight the importance of information theory in understanding neural coding.