The article by Simon Laughlin explores how a simple coding procedure enhances a neuron's information capacity. Specifically, it examines the contrast-response function of first-order interneurons in the fly's compound eye, which closely matches the cumulative probability distribution of contrast levels in natural scenes. This matching allows the neurons to encode contrast fluctuations efficiently. The author uses information theory to argue that neurons should encode inputs so that all response levels are used with equal frequency to maximize information transmission. This strategy, known as "histogram equalization," ensures that common events are resolved with high resolution while underutilizing less common events. The study compares the interneuron's contrast-response function with natural scene contrasts and finds a strong match, indicating that these neurons use an efficient coding strategy. The successful application of information theory to neural coding supports the idea that redundancy reduction is crucial in visual processing and suggests that this coding procedure could be widely applicable in nervous systems.The article by Simon Laughlin explores how a simple coding procedure enhances a neuron's information capacity. Specifically, it examines the contrast-response function of first-order interneurons in the fly's compound eye, which closely matches the cumulative probability distribution of contrast levels in natural scenes. This matching allows the neurons to encode contrast fluctuations efficiently. The author uses information theory to argue that neurons should encode inputs so that all response levels are used with equal frequency to maximize information transmission. This strategy, known as "histogram equalization," ensures that common events are resolved with high resolution while underutilizing less common events. The study compares the interneuron's contrast-response function with natural scene contrasts and finds a strong match, indicating that these neurons use an efficient coding strategy. The successful application of information theory to neural coding supports the idea that redundancy reduction is crucial in visual processing and suggests that this coding procedure could be widely applicable in nervous systems.