Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information.

ABSTRACT: Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probab

SUBMITTER: Kocillari L 

PROVIDER: S-EPMC11185652 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature