  We study the reconstruction of visual stimuli from spike trains, recording
simultaneously from the two H1 neurons located in the lobula plate of the fly
Chrysomya megacephala. The fly views two types of stimuli, corresponding to
rotational and translational displacements. If the reconstructed stimulus is to
be represented by a Volterra series and correlations between spikes are to be
taken into account, first order expansions are insufficient and we have to go
to second order, at least. In this case higher order correlation functions have
to be manipulated, whose size may become prohibitively large. We therefore
develop a Gaussian-like representation for fourth order correlation functions,
which works exceedingly well in the case of the fly. The reconstructions using
this Gaussian-like representation are very similar to the reconstructions using
the experimental correlation functions. The overall contribution to rotational
stimulus reconstruction of the second order kernels - measured by a chi-squared
averaged over the whole experiment - is only about 8% of the first order
contribution. Yet if we introduce an instant-dependent chi-square to measure
the contribution of second order kernels at special events, we observe an up to
100% improvement. As may be expected, for translational stimuli the
reconstructions are rather poor. The Gaussian-like representation could be a
valuable aid in population coding with large number of neurons.
