Towards a noise prediction model for in vivo neural recording.

The signal-to-noise ratio of in vivo extracellular neural recordings with microelectrodes is influenced by many factors including the impedance of the electrode-tissue interface, the noise of the recording equipment and biological background noise from distant neurons. In this work we study the different noise sources affecting the quality of neural signals. We propose a simplified noise model as an analytical tool to predict the noise of an electrode given its geometrical dimensions and impedance characteristics. With this tool we are able to quantify different noise sources, which is important to determine realistic noise specifications for the design of electronic neural recording interfaces.