Split-trial analysis reveals the information capacity of neural population codes

Understanding how correlated neural noise affects neural population coding is a basic question in computational and systems neuroscience [1, 2, 3, 4]. Recent theoretical work suggests that shared noise along the stimulus encoding direction is the primary factor that limits information encoding (i.e., information-limiting noise) [5, 6]. Despite this theoretical insight, it has been difficult to test it experimentally due to the challenges in inferring information-limiting noise from neural data. To overcome this challenge, we have developed a method ( i.e ., split-trial analysis) to partition the noise in a neural population into information-limiting noise vs. non-information-limiting components. Our method is simple to implement, yet it is highly effective given a limited amount of data. Results from extensive numerical simulations show that split-trial analysis substantially outperforms existing methods in accuracy, efficiency, and robustness. Applications of split-trial analysis to a number of neurophysiological datasets reveals insights into the precision of the neural codes for several systems. First, it reveals a substantial amount of information-limiting noise in the mouse head direction system. Second, it uncovers a small yet positive information-limiting noise in the orientation code in mouse V1. Third, we discover that the information-limiting noise in the macaque pre-frontal cortex is highly consistent over time during a simple saccade task. Split-trial analysis is a general technique that should be widely applicable in analyzing the properties of neural population codes.