A Quick and Easy Way to Estimate Entropy and Mutual Information for Neuroscience

Calculations of entropy a signal or mutual information between two variables are valuable analytical tools in the field neuroscience. They can be applied to all types data, capture non-linear interactions and model independent. Yet limited size number recordings one collect series experiments makes their calculation highly prone sampling bias. Mathematical methods overcome this so-called “sampling disaster” exist, but require significant expertise, great time computational costs. As such, there is need for simple, unbiased computationally efficient tool estimating level information. In article, we propose that application entropy-encoding compression algorithms widely used text image fulfill these requirements. By simply saving PNG picture format measuring file on hard drive, estimate changes through different conditions. Furthermore, with some simple modifications file, also evolution stimulus observed responses We first demonstrate applicability method using white-noise-like signals. Then, while kind experimental conditions, provide examples its patch-clamp recordings, detection place cells histological data. Although does not give an absolute value information, it mathematically correct, simplicity broad use make powerful estimation experiments.