  A major goal in neuroscience is to understand how populations of neurons code
for stimuli or actions. While the number of neurons that can be recorded
simultaneously is increasing at a fast pace, in most cases these recordings
cannot access a complete population. In particular, it is hard to
simultaneously record all the neurons of the same type in a given area. Recent
progress have made possible to profile each recorded neuron in a given area
thanks to genetic and physiological tools, and to pool together recordings from
neurons of the same type across different experimental sessions. However, it is
unclear how to infer the activity of a full population of neurons of the same
type from these sequential recordings. Neural networks exhibit collective
behaviour, e.g. noise correlations and synchronous activity, that are not
directly captured by a conditionally-independent model that would just put
together the spike trains from sequential recordings. Here we show that we can
infer the activity of a full population of retina ganglion cells from
sequential recordings, using a novel method based on copula distributions and
maximum entropy modeling. From just the spiking response of each ganglion cell
to a repeated stimulus, and a few pairwise recordings, we could predict the
noise correlations using copulas, and then the full activity of a large
population of ganglion cells of the same type using maximum entropy modeling.
Remarkably, we could generalize to predict the population responses to
different stimuli and even to different experiments. We could therefore use our
method to construct a very large population merging cells' responses from
different experiments. We predicted synchronous activity accurately and showed
it grew substantially with the number of neurons. This approach is a promising
way to infer population activity from sequential recordings in sensory areas.
