  Neuronal circuits formed in the brain are complex with intricate connection
patterns. Such complexity is also observed in the retina as a relatively simple
neuronal circuit. A retinal ganglion cell receives excitatory inputs from
neurons in previous layers as driving forces to fire spikes. Analytical methods
are required that can decipher these components in a systematic manner.
Recently a method termed spike-triggered non-negative matrix factorization
(STNMF) has been proposed for this purpose. In this study, we extend the scope
of the STNMF method. By using the retinal ganglion cell as a model system, we
show that STNMF can detect various computational properties of upstream bipolar
cells, including spatial receptive field, temporal filter, and transfer
nonlinearity. In addition, we recover synaptic connection strengths from the
weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of
a ganglion cell into a few subsets of spikes where each subset is contributed
by one presynaptic bipolar cell. Taken together, these results corroborate that
STNMF is a useful method for deciphering the structure of neuronal circuits.
