Publication

Abstract : To understand brain activity relating neurons to circuits to learning and behavior, we explored a bottom-up computational reconstruction of population signals arising from cerebellum granular layer. As a first implementation, using bio-realistic computational models of cerebellum granule cell, in vivo spike train patterns were computed and then translated into functional Magnetic Resonance Imaging, Blood Oxygen-Level Dependent (BOLD) signals. The BOLD response was generated from averaged activity arising from center-surround organization modeled by using excitatory-inhibitory ratios related to experimental data. The averaged responses were converted to BOLD signals using the balloon and modified Windkessel models. Although both models generated BOLD responses corresponding to neural activity, the temporal mismatch was attributed to the response by the delayed compliance parameter in the Windkessel model. The modeling suggests that experimental variability observed in the cerebellar micro-zones could be related to compliance chances, activation patterns and number of neurons. Although detailed neuro-vasculature information was not modeled, the advantage in this methodology is that cerebellar cortex may allow seemingly linear transformations of underlying spiking that could be then used to validate network reconstructions.