Spike train signatures of retinal ganglion cell types.

The mammalian retina deconstructs the visual world using parallel neural channels, embodied in the morphological and physiological types of ganglion cells. We sought distinguishing features of each cell type in the temporal pattern of their spikes. As a first step, conventional physiological properties were used to cluster cells in eight types by a statistical analysis. We then adapted a method of P. Reinagel et al. (1999: J. Neurophysiol., 81, 2558-2569) to define epochs within the spike train of each cell. The spike trains of many cells were found to contain robust patterns that are defined by the (averaged) timing of successive interspike intervals in brief activity epochs. The patterns were robust across four different types of visual stimulus. Although the patterns are conserved in different visual environments, they do not prevent the cell from signaling the strength of its response to a particular stimulus, which is expressed in the number of spikes contained in each coding epoch. Clustering based on the spike train patterns alone showed that the spike train patterns correspond, in most but not all cases, to cell types pre-defined by traditional criteria. That the congruence is less than perfect suggests that the typing of rabbit ganglion cells may need further refinement. Analysis of the spike train patterns may be useful in this regard and for distinguishing the many unidentified ganglion cell types that exist in other mammalian retinas.

The spatial filtering properties of local edge detectors and brisk-sustained retinal ganglion cells.

We compared image computation in the rabbit retina by two different cell types: the so-called 'local edge detecting' ganglion cells and the well-known brisk-sustained ganglion cells. From both anatomical and physiological evidence, these cells are present in nearly equal numbers and thus overlap to sample the same regions of visual space. We recorded simultaneously from overlapping cells on a dense microelectrode array. The results were analysed using an anatomically realistic simulation of the retina's processing levels. The 'local edge detecting' cell was found to be tuned to higher spatial frequencies and to have a narrower spatial frequency bandpass than the brisk-sustained cells. Simulation revealed that this is due primarily to the 'zero-crossing' detector implied by the definition of the local edge detector. The outputs of the simulations in response to complex images were analysed quantitatively. The results showed the population of local edge detectors to transmit a sparser code than the brisk-sustained cells.