2pm, Thursday, 9 November 2006 Rate-correlation relationship in spiking neurons

Brent Doiron, NYU

How do neurons encode information about sensory inputs and motor outputs? In many cases, the averaged spiking rates of populations of similarly "tuned" neurons carry these signals. This has inspired mean-field models of these populations, which commonly assume that the individual neurons spike independently or have fixed values of correlations. We use linear response calculations and an intuitive statistical model to show that neither of these cases is to be expected.

Instead, correlations vary sharply with firing rates and hence with the signals themselves. This has strong consequences for the neural code. We illustrate these via closed-form calculations of Fisher information, which quantifies the accuracy of encoding.

This is joint work with Jaime de la Rocha, Eric Shea-Brown, Kreso Josic, and Alex Reyes.