Conditional Inference for Learning the Network Structure of Cortical Microcircuits

• Activity Number: 119
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Matthew Harrison*+
• Companies: Brown University
• Keywords: neuroscience ; point process ; conditional inference ; incidental parameters ; binary time series ; network

Abstract

The spiking dynamics of simultaneously recorded neurons from a small region of cortex reflect the local network structure of excitatory and inhibitory connections between observed neurons, as well as the time varying response of the neurons to their many unobserved and correlated inputs. Inference about the local network is easily contaminated by these unobserved nonstationary influences. We have been exploring conditional inference as an approach for statistically isolating local network dynamics from background nonstationarities. In this approach the background dynamics enter the model as incidental parameters, analogous to the classical Neyman-Scott problem, and conditioning serves the dual role of removing the incidental parameters and facilitating computation.