Nature 414, 173-179 (2001) This Article described patterns of labelling observed in olfactory cortex when a transneuronal tracer was co-expressed with single odorant receptor genes in the mouse olfactory epithelium. During efforts to replicate and extend this work, we have been unable to reproduce the reported findings. Moreover, we have found inconsistencies between some of the figures and data published in the paper and the original data. We have therefore lost confidence in the reported conclusions. We regret any adverse consequences that may have resulted from the paper's publication.

1. We recorded single-unit and multi-unit activity in response to transient presentation of texture and grating patterns at 25 sites within the parafoveal representation of V1, V2, and V3 of two awake monkeys trained to perform a fixation task. In grating experiments, stimuli varied in orientation, spatial frequency, or both. In texture experiments, stimuli varied in contrast, check size, texture type, or pairs of these attributes. 2. To examine the nature and precision of temporal coding, we compared individual responses elicited by each set of stimuli in terms of two families of metrics. One family of metrics, D(spike), was sensitive to the absolute spike time (following stimulus onset). The second family of metrics, D(interval), was sensitive to the pattern of interspike intervals. In each family, the metrics depend on a parameter q, which expresses the precision of temporal coding. For q = 0, both metrics collapse into the "spike count" metric D(Count), which is sensitive to the number of impulses but insensitive to their position in time. 3. Each of these metrics, with values of q ranging from 0 to 512/s, was used to calculate the distance between all pairs of spike trains within each dataset. The extent of stimulus-specific clustering manifest in these pairwise distances was quantified by an information measure. Chance clustering was estimated by applying the same procedure to synthetic data sets in which responses were assigned randomly to the input stimuli. 4. Of the 352 data sets, 170 showed evidence of tuning via the spike count (q = 0) metric, 294 showed evidence of tuning via the spike time metric, 272 showed evidence of tuning via the spike interval metric to the stimulus attribute (contrast, check size, orientation, spatial frequency, or texture type) under study. Across the entire dataset, the information not attributable to chance clustering averaged 0.042 bits for the spike count metric, 0.171 bits for the optimal spike time metric, and 0.107 bits for the optimal spike interval metric. 5. The reciprocal of the optimal cost q serves as a measure of the temporal precision of temporal coding. In V1 and V2, with both metrics, temporal precision was highest for contrast (ca. 10-30 ms) and lowest for texture type (ca. 100 ms). This systematic dependence of q on stimulus attribute provides a possible mechanism for the simultaneous representation of multiple stimulus attributes in one spike train. 6. Our findings are inconsistent with Poisson models of spike trains. Synthetic data sets in which firing rate was governed by a time-dependent Poisson process matched to the observed poststimulus time histogram (PSTH) overestimated clustering induced by D(count) and, for low values of q, D(spike)[q] and D(intervals)[q]. Synthetic data sets constructed from a modified Poisson process, which preserved not only the PSTH but also spike count statistics accounted for the clustering induced by D(count) but underestimated the clustering induced by D(spike)[q] and D(interval)[q].

SUMMARY1. Variation in stimulus contrast produces a marked effect on the dynamics of the cat retina. This contrast effect was investigated by measurement ofthe responses of X and Y ganglion cells. The stimuli were sine gratings or rectangular spots modulated by a temporal signal which was a sum of sinusoids. Fourier analysis of the neural response to such a stimulus allowed us to calculate first order and second order frequency kernels.2. The first order frequency kernel of both X and Y ganglion cells became more sharply tuned at higher contrasts. The peak amplitude also shifted to higher temporal frequency at higher contrasts. Responses to low frequencies of modulation (< 1 Hz) grew less than proportionally with contrast. However, response amplitudes at higher modulation frequencies (> 4 Hz) scaled approximately proportionally with contrast. Also, there was a marked phase advance in these latter components as contrast increased.3. The contrast effect was significantly larger for Y cells than for X cells. 4. The first order frequency kernel was measured with single sine waves as well as with the sum of sinusoids as a modulation signal. The transfer function measured in this way was much less affected by increases in contrast. This implied that stimulus energy at one temporal frequency could affect the response amplitude and phase shift at another temporal frequency.5. Direct proof was found that modulation at one frequency modifies the response at other frequencies. This was demonstrated by perturbation experiments in which the modulation stimulus was the sum of one strong perturbing sinusoid and seven weak test sinusoids.6. The shape of the graph of the amplitude of the first order frequency kernel vs. temporal frequency did not depend on the amplitudes of the first order components, but rather on local retinal contrast. This was shown in an experiment with a sine grating placed at different positions in the visual field. The shape of the first order kernel did not vary with spatial phase, while the magnitudes of the first order responses varied greatly with spatial phase.7. Models for the contrast gain control mechanism are considered in the Discussion.

running head: Spike train metrics key words: temporal coding, metric space, dynamic programming 1 Spike Train Metrics, Revised Victor and Purpura 17:20 October 30, 1998 ABSTRACT We present the mathematical basis of a new approach to the analysis of temporal coding. The foundation of the approach is the construction of several families of novel distances (metrics) between neuronal impulse trains. In contrast to most previous approaches to the analysis of temporal coding, the present approach does not attempt to embed impulse trains in a vector space, and does not assume a Euclidean notion of distance. Rather, the proposed metrics formalize physiologically-based hypotheses for what aspects of the firing pattern might be stimulus-dependent, and make essential use of the point process nature of neural discharges. We show that these families of metrics endow the space of impulse trains with related but inequivalent topological structures. We show how these metrics can be used to determine whether a set of observed responses have stimulus-dependent temporal structure without a vector-space embedding. We show how multidimensional scaling can be used to assess the similarity of these metrics to Euclidean distances. For two of these families of metrics (one based on spike times and one based on spike intervals), we present highly efficient computational algorithms for calculating the distances. We illustrate these ideas by application to artificial datasets and to recordings from auditory and visual cortex.

Connectivity in the cortex is organized at multiple scales 1-5, suggesting that scale-dependent correlated activity is particularly important for understanding the behavior of sensory cortices and their function in stimulus encoding. Here, we analyze the scale-dependent structure of cortical interactions by using maximum entropy models 6-9 to characterize multiple-tetrode recordings from primary visual cortex of anesthetized monkeys (Macaca mulatta). We compare the properties of firing patterns among local clusters of neurons (<300 microns) with neurons separated by larger distances (600-2500 microns). We find that local firing patterns are distinctive: while multi-neuronal firing patterns at larger distances can be predicted by pairwise interactions, patterns within local clusters often show evidence of high-order correlations. Surprisingly, these local correlations are flexible and rapidly reorganized by visual input. While they modestly reduce the amount of information that a cluster conveys, they also modify the format of this information, creating sparser codes by increasing the periods of total quiescence, and concentrating information into briefer periods of common activity. These results imply a hierarchical organization of neuronal correlations: simple pairwise correlations link neurons over scales of tens to hundreds of minicolumns, but on the scale of a few minicolumns, ensembles of neurons form complex subnetworks whose moment-to-moment effective connectivity is dynamically reorganized by the stimulus.