Ruling out and ruling in neural codes.

The subject of neural coding has generated much debate. A key issue is whether the nervous system uses coarse or fine coding. Each has different strengths and weaknesses and, therefore, different implications for how the brain computes. For example, the strength of coarse coding is that it is robust to fluctuations in spike arrival times; downstream neurons do not have to keep track of the details of the spike train. The weakness, though, is that individual cells cannot carry much information, so downstream neurons have to pool signals across cells and/or time to obtain enough information to represent the sensory world and guide behavior. In contrast, with fine coding, individual cells can carry much more information, but downstream neurons have to resolve spike train structure to obtain it. Here, we set up a strategy to determine which codes are viable, and we apply it to the retina as a model system. We recorded from all the retinal output cells an animal uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, and used optimal, i.e., Bayesian, decoding. This approach makes it possible to obtain an upper bound on the performance of codes and thus eliminate those that are insufficient, that is, those that cannot account for behavioral performance. Our results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.

Selective ablation of a class of amacrine cells alters spatial processing in the retina.

Several recent studies have suggested that the spatial tuning of retinal ganglion cells may be a more complex process than previously thought. The working hypothesis for many years was that the tuning was shaped by operations performed in the first synaptic layer of the retina, but recent work shows that operations in the second synaptic layer, involving amacrine cells, also play a significant role (Cook and McReynolds, 1998; Taylor, 1999; Flores-Herr et al., 2001). Although it is clear that amacrine cells are involved, the precise roles of the different amacrine subtypes in the many aspects of spatial tuning have not yet been established. Here we used a cell class ablation method to remove one subtype, the neuropeptide Y-expressing cells (NPY cells), and tapped into a part of the circuitry that tunes ganglion cells toward large spatial patterns (low spatial frequencies). When the subtype was ablated, ganglion cells tuned toward low spatial frequencies, both ON- and OFF-type cells, lost this preferential tuning. The effect was specific because ablation of another amacrine subtype did not produce it. Further analysis showed that the change in tuning was attributable to a decrease in the receptive field surround size of the ganglion cell. Other parameters, such as the size, strength, and asymmetry of the center and the strength of the surround, were not statistically significantly affected. These results thus show a mechanism for tuning cells to low spatial frequencies; an operation in the second synaptic layer, mediated by NPY cells, extends the surround of the ganglion cell.

Classification of retinal ganglion cells: a statistical approach.

Numerous studies have shown that retinal ganglion cells exhibit an array of responses to visual stimuli. This has led to the idea that these cells can be sorted into distinct physiological classes, such as linear versus nonlinear or on versus off. Although many classification schemes are widely accepted, few studies have provided statistical support to favor one scheme over another. Here we test whether some of the most widely used classification schemes can be statistically verified, using the mouse retina as the model system. We used a cluster analysis approach and focused on 4 standard response parameters: 1) response latency, 2) response duration, 3) relative amplitude of the on and off responses, and 4) degree of nonlinearity in the stimulus-to-response transformation. For each parameter, we plotted its distribution and tested quantitatively, using a bootstrap method, whether it divided into distinct clusters. Our analysis showed that mouse ganglion cells clustered into several groups based on response latency, duration, and relative amplitude of the on and off responses, but did not cluster into more than one group based on degree of nonlinearity-the latter formed a single, large, continuous group. Thus while some well-known schemes for classifying ganglion cells could be statistically verified, others could not. Knowledge of which schemes can be confirmed is important for building models of how retinal output is processed and how retinal circuits are built. Finally, this cluster analysis approach is general and can be used to test other classification proposals as well, both physiological and anatomical.