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In the primate primary visual cortex (V1), the significance of individual action potentials has been difficult to determine, particularly in light of the considerable trial-to-trial variability of responses to visual stimuli. We show here that the information conveyed by an action potential depends on the duration of the immediately preceding interspike interval (ISI). The interspike intervals can be grouped into several different classes on the basis of reproducible features in the interspike interval histograms. Spikes in different classes bear different relationships to the visual stimulus, both qualitatively (in terms of the average stimulus preceding each spike) and quantitatively (in terms of the amount of information encoded per spike and per second). Spikes preceded by very short intervals (3 msec or less) convey information most efficiently and contribute disproportionately to the overall receptive-field properties of the neuron. Overall, V1 neurons can transmit between 5 and 30 bits of information per second in response to rapidly varying, pseudorandom stimuli, with an efficiency of approximately 25%. Although some (but not all) of our results would be expected from neurons that use a firing-rate code to transmit information, the evidence suggests that visual neurons are well equipped to decode stimulus-related information on the basis of relative spike timing and ISI duration.

Electrophysiologic and functional imaging studies have shown that the visual cortex produces differential responses to the presence or absence of structure within visual textures. To further define and characterize regions involved in the analysis of form, functional magnetic resonance imaging (fMRI) was used to detect changes in activation during the viewing of four levels of isodipole textures. The texture levels systematically differed in the density of visual features such as extended contours and blocks of solid color present within the images. A linear relationship between activation level and density of structure was observed in the striate cortex of human subjects. This finding suggests that a special subpopulation of striate cortical neurons participates in the ability to extract and process structural continuity within visual stimuli.

Neuronal activity often is treated as a composition of a stimulus-driven component and a second component that corrupts the signal, adding or deleting spikes at random. Standard quantitative methods such as peristimulus histograms and Fourier analysis use stimulus-locked averaging to enhance detection of the driven component of neuronal responses and de-emphasize the "noise." However, neural activity also includes bursts, oscillations, and other episodic events that standard averaging methods overlook. If this activity is stimulus independent, it can be characterized by standard power spectral analysis (or autocorrelation). But activity that is excited by (but not temporally locked to) the visual stimulus cannot be characterized by averaging or standard spectral analysis. Phase-locked spectral analysis (PLSA) is a new method that examines this "residual" activity-the difference between the individual responses to each cycle of a periodic stimulus and their average. With PLSA, residual activity is characterized in terms of temporal envelopes and their carriers. Previously, PLSA demonstrated broadband interactions between periodic visual stimuli and fluctuations in the local field potential of macaque V1. In the present study, single-unit responses (SUA) from parafoveal V1 in anesthetized macaque monkey are examined with this technique. Recordings were made from 21 neurons, 6 of which were recorded in pairs along with multiunit activity (MUA) from separate electrodes and 8 of which were recorded along with MUA from the same electrode. PLSA was applied to responses to preferred (orientation, direction, and spatial frequency) and nonpreferred drifting gratings. For preferred stimuli, all cells demonstrated broadband (1-10 Hz and higher) residual activity that waxed and waned with the stimulus cycle, suggesting that changes in the residual activity are introduced routinely by visual stimulation. Moreover, some reconstructed envelopes indicate that the residual activity was sharply gated by the stimulus cycle. Oscillations occasionally were seen in the power spectrum of single units. Phase-locked cross-spectra were determined for 3 SUA/SUA pairs and 11 SUA/MUA pairs. Residual activity in the cross-spectra was generally much less than the residual activity determined separately from each neuron. The reduction in the residual activity in the cross-spectra suggests that nearby neurons may gate inputs from distinct and relatively independent neuronal subpopulations that together generate the background rhythms of striate cortex.

To determine visual areas of the human brain involved in elementary form processing, functional magnetic resonance imaging (fMRI) was used to measure regional responses to two types of achromatic textures. Healthy young adults were presented with 'random' textures which lacked spatial organization of the black and white pixels that make up the image, and 'correlated' textures in which the pixels were ordered to produce extended contours and rectangular blocks at multiple spatial scales. Relative to a fixation condition, random texture stimulation resulted in increased signal intensity primarily in the striate cortex, with slight involvement of the cuneus and middle occipital, lingual and fusiform gyri. Correlated texture stimulation also resulted in activation of these areas, yet the regional extent of this activation was significantly greater than that produced by random textures. Unlike random stimulation, correlated stimulation additionally resulted in middle temporal activation. Direct comparison of the two stimulation conditions revealed significant differences most consistently in the anterior fusiform gyrus, but also in striate, middle occipital, lingual and posterior temporal regions in subjects with robust activation patterns. While both random and correlated stimulation produced activation in similar areas of the occipital lobe, the increase in regional activation during the correlated condition suggests increased recruitment of neuronal populations occurs in response to textures containing visually salient features. This increased recruitment occurs within striate, extrastriate and temporal regions of the brain, also suggesting the presence of receptive field mechanisms in the ventral visual pathway that are sensitive to features produced by higher-order spatial correlations., (Copyright 1998 Elsevier Science B.V.)

We show that spike timing adds to the information content of spike trains for transiently presented stimuli but not for comparable steady-state stimuli, even if the latter elicit transient responses. Contrast responses of 22 single neurons in macaque V1 to periodic presentation of steady-state stimuli (drifting sinusoidal gratings) and transient stimuli (drifting edges) of optimal spatiotemporal parameters were recorded extracellularly. The responses were analyzed for contrast-dependent clustering in spaces determined by metrics sensitive to the temporal structure of spike trains. Two types of metrics, cost-based spike time metrics and metrics based on Fourier harmonics of the response, were used. With both families of metrics, temporal coding of contrast is lacking in responses to drifting sinusoidal gratings of most (simple and complex) V1 neurons. However, two-thirds of all neurons, mostly complex cells, displayed significant temporal coding of contrast for edge stimuli. The Fourier metrics indicated that different response harmonics are partially independent, and their combined use increases information about transient stimuli. Our results demonstrate the importance of stimulus transience for temporal coding. This finding is significant for natural vision because moving edges, which are present in moving object boundaries, and saccades induce transients. We think that an abrupt change in the adapted state of the local visual circuitry triggers the temporal structuring of spike trains in V1 neurons.

We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25-50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the "pooled phases" approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index Hpooled. In the "individual phases" approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index Hindiv. Hpooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas Hindiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.

We described a novel approach to the study of how spike trains encode sensory information. This approach emphasizes the idea that spike trains are sequences of discrete events, rather than approximations to continuous signals. Aided by some simple heuristics, such as a caricature of neurons as coincidence detectors, we constructed candidate notions of "distances" between spike trains, considered as points in an abstract space. Each candidate distance was evaluated for relevance to biological encoding by determining whether it led to systematic, stimulus-dependent, clustering of the neural responses. We showed here that these distance can also be used to construct a "response space" for the neuron. The response space, which is typically not Euclidean, can represent two or three stimulus attributes. We also introduced the notion of a "consensus spike train," defined as the spike train with minimum average distance from a set of observed responses. For the distances we considered, the consensus spike train (for a particular stimulus) contained only those spikes that were present at consistent times across the observed responses to that stimulus, and thus contained fewer spikes than the typical observed responses. Nevertheless, these consensus spike trains provided an equivalent (or even superior) representation of the stimulus array.

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].

The striate cortex of macaque monkeys contains an array of patches which stain heavily for the enzyme cytochrome oxidase (CO blobs). Cells inside and outside these blobs are often described as belonging to two distinct populations or streams. In order to better understand the function of the CO blobs, we measured the contrast sensitivity and spatial frequency response of single neurons in and around the CO blobs. Density profiles of each blob were assessed using a new quantitative method, and correlations of local CO density with the physiology were noted. We found that the CO density dropped off gradually with distance from blob centers: in a typical blob the CO density dropped from 75% to 25% over 100 microns. Recordings were confined to cortical layers 2/3. Most neurons in these layers have poor contrast sensitivity, similar to that of the parvocellular neurons in the lateral geniculate nucleus. However, in a small proportion of layers 2/3 neurons we found higher contrast sensitivity, similar to that of the magnocellular neurons. These neurons were found to cluster near blob centers. This finding is consistent with (indirect) parvocellular input spread uniformly throughout layers 2/3, and (indirect) magnocellular input focused on CO blobs. We also measured spatial tuning curves for both single units and multiple unit activity. In agreement with other workers we found that the optimal spatial frequencies of cells near blob centers were low (median 2.8 c/deg), while the optimal frequencies of cells in the interblob regions were spread over a wide range of spatial frequencies. The high cut-off spatial frequency of multi-unit activity increased with distance from blob centers. We found no correlation between spatial bandwidth and distance from blob centers. All measured physiological properties varied gradually with distance from CO blob centers. This suggests that the view of blob cells subserving visual functions which are entirely distinct from non-blob cells may have to be reevaluated.

Spatial correlations define the statistical structure of any visual image. Two-point correlations inform the visual system about the spatial frequency content of an image. Higher-order correlations can capture salient features such as object contours. We studied "isodipole" texture discrimination in V1 to determine if higher-order spatial correlations can be extracted by early stages of cortical processing. We made epicortical, local field potential, and single-cell recordings of responses elicited by isodipole texture interchange in anesthetized monkeys. Our studies demonstrate that single neurons in V1 can signal the presence of higher-order spatial correlations in visual textures. This places a computational mechanism, which may be essential for form vision at the earliest stage of cortical processing.