Speaker
Simon Schultz
(Imperial College)
Description
New technologies such as high-density multi-electrode array
recording and multiphoton calcium imaging allow the activity
of large numbers of neurons to be monitored. However,
analysis tools have lagged behind the experimental
technology, with most approaches limited to very small
population sizes. In the limit of short time windows, where
neuronal activity can be binarized without loss of
information, the Ising model provides a useful approach
towards capturing the information content of large neural
ensembles. I will show how maximum entropy models including
the Ising model fit with the information component analysis
theoretical framework for studying neural coding, and how
the Ising model can be used to decode large neural
ensembles. I will highlight some recent advances we have
made in scaling up our decoders, and demonstrate the
algorithms on in vivo multielectrode array and two photon
calcium imaging data.
