Preferred Spatial Frequency Research Articles

Feature-specific divisive normalization improves natural image encoding for depth perception.

Vision science and visual neuroscience seek to understand how stimulus and sensor properties limit the precision with which behaviorally-relevant latent variables are encoded and decoded. In the primate visual system, binocular disparity-the canonical cue for stereo-depth perception-is initially encoded by a set of binocular receptive fields with a range of spatial frequency preferences. Here, with a stereo-image database having ground-truth disparity information at each pixel, we examine how response normalization and receptive field properties determine the fidelity with which binocular disparity is encoded in natural scenes. We quantify encoding fidelity by computing the Fisher information carried by the normalized receptive field responses. Several findings emerge from an analysis of the response statistics. First, broadband (or feature-unspecific) normalization yields Laplace-distributed receptive field responses, and narrowband (or feature-specific) normalization yields Gaussian-distributed receptive field responses. Second, the Fisher information in narrowband-normalized responses is larger than in broadband-normalized responses by a scale factor that grows with population size. Third, the most useful spatial frequency decreases with stimulus size and the range of spatial frequencies that is useful for encoding a given disparity decreases with disparity magnitude, consistent with neurophysiological findings. Fourth, the predicted patterns of psychophysical performance, and absolute detection threshold, match human performance with natural and artificial stimuli. The current computational efforts establish a new functional role for response normalization, and bring us closer to understanding the principles that should govern the design of neural systems that support perception in natural scenes.

How the window of visibility varies around polar angle.

Contrast sensitivity, the amount of contrast required to detect or discriminate an object, depends on spatial frequency (SF): The Contrast Sensitivity Function (CSF) peaks at intermediate SFs and drops at lower and higher SFs and is the basis of computational models of visual object recognition. The CSF varies from foveal to peripheral vision, but only a couple studies have assessed changes around polar angle of the visual field. Sensitivity is generally better along the horizontal than the vertical meridian, and better at the lower vertical than the upper vertical meridian, yielding polar angle asymmetries. Here, we investigate CSF attributes at polar angle locations at both group and individual levels, using Hierarchical Bayesian Modeling. This method enables precise estimation of CSF parameters by decomposing the variability of the dataset into multiple levels and analyzing covariance across observers. At the group level, peak contrast sensitivity and corresponding spatial frequency with the highest sensitivity are higher at the horizontal than vertical meridian, and at the lower than upper vertical meridian. At an individual level, CSF attributes (e.g., maximum sensitivity, the most preferred SF) across locations are highly correlated, indicating that although the CSFs differ across locations, the CSF at one location is predictive of the CSF at another location. Within each location, the CSF attributes co-vary, indicating that CSFs across individuals vary in a consistent manner (e.g., as maximum sensitivity increases, so does the SF at which sensitivity peaks), but more so at the horizontal than the vertical meridian locations. These results show similarities and uncover some critical polar angle differences across locations and individuals, suggesting that the CSF should not be generalized across iso-eccentric locations around the visual field. Our window of visibility varies with polar angle: It is enhanced and more consistent at the horizontal meridian.

A dynamic sequence of visual processing initiated by gaze shifts.

Animals move their head and eyes as they explore the visual scene. Neural correlates of these movements have been found in rodent primary visual cortex (V1), but their sources and computational roles are unclear. We addressed this by combining head and eye movement measurements with neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns followed gaze shifts and together these formed a temporal sequence that was absent in darkness. Gaze-shift responses resembled those evoked by sequentially flashed stimuli, suggesting a large component corresponds to onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency bias, consistent with coarse-to-fine processing. Recordings in freely gazing marmosets revealed a similar sequence following saccades, also aligned to spatial frequency preference. Our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling.

Enhancement and contextual modulation of visuospatial processing by thalamocollicular projections from ventral lateral geniculate nucleus

In the mammalian visual system, the ventral lateral geniculate nucleus (vLGN) of the thalamus receives salient visual input from the retina and sends prominent GABAergic axons to the superior colliculus (SC). However, whether and how vLGN contributes to fundamental visual information processing remains largely unclear. Here, we report in mice that vLGN facilitates visually-guided approaching behavior mediated by the lateral SC and enhances the sensitivity of visual object detection. This can be attributed to the extremely broad spatial integration of vLGN neurons, as reflected in their much lower preferred spatial frequencies and broader spatial receptive fields than SC neurons. Through GABAergic thalamocollicular projections, vLGN specifically exerts prominent surround suppression of visuospatial processing in SC, leading to a fine tuning of SC preferences to higher spatial frequencies and smaller objects in a context-dependent manner. Thus, as an essential component of the central visual processing pathway, vLGN serves to refine and contextually modulate visuospatial processing in SC-mediated visuomotor behaviors via visually-driven long-range feedforward inhibition.

Coarse-to-fine interaction on perceived depth in compound grating.

To encode binocular disparity, the visual system uses a pair of left eye and right eye bandpass filters with either a position or a phase offset between them. Such pairs are considered to exit at multiple scales to encode a wide range of disparity. However, local disparity measurements by bandpass mechanisms can be ambiguous, particularly when the actual disparity is larger than a half-cycle of the preferred spatial frequency of the filter, which often occurs in fine scales. In this study, we investigated whether the visual system uses a coarse-to-fine interaction to resolve this ambiguity at finer scales for depth estimation from disparity. The stimuli were stereo grating patches composed of a target and comparison patterns. The target patterns contained spatial frequencies of 1 and 4 cycles per degree (cpd). The phase disparity of the low-frequency component was 0° (at the horopter), -90° (uncrossed), or 90° (crossed), and that of the high-frequency components was changed independent of the low-frequency disparity, in the range between -90° (uncrossed) and 90° (crossed). The observers' task was to indicate whether the target appeared closer to the comparison pattern, which always shared the disparity with the low-frequency component of the target. Regardless of whether the comparison pattern was a 1-cpd + 4-cpd compound or a 1-cpd simple grating, the perceived depth order of the target and the comparison varied in accordance with the phase disparity of the high-frequency component of the target. This effect occurred not only when the low-frequency component was at the horopter, but also when it contained a large disparity corresponding to one cycle of the high-frequency component (±90°). Our findings suggest a coarse-to-fine interaction in multiscale disparity processing, in which the depth interpretation of the high-frequency changes based on the disparity of the low-frequency component.

Poster Session: Dynamic visual processing in post-saccadic V1 visual responses of the marmoset monkey.

All animals with image-forming eyes sample visual information through a "saccade and fixate" pattern of eye movements (Land and Nilsson, 2012). Here, we investigated how perisaccadic neural responses during free-viewing of natural images relate to the tuning properties of neurons in primary visual cortex (V1). We recorded from V1 in two marmoset monkeys using linear arrays as they freely viewed full-field natural scenes. We found that on average neural responses time-locked to saccade onset exhibited an early suppression followed by a post-saccadic rebound in excitatory activity. To determine whether the latency of these saccade dynamics varied with neural tuning properties we mapped tuning for spatial and temporal frequency, and orientation selectivity. In those trials marmosets free-viewed flashed full-field gratings that varied in spatial frequency and orientation. Among the neurons with significant orientation tuning, we observe a significant correlation between spatial frequency preference and the latency of post-saccadic rebound, with neurons tuned to higher spatial frequencies responding later. These results support the idea that neural responses during the saccade-fixation cycle follow coarse-to-fine distinctions proposed to operate in early visual processing (Burr, 1981; Watt, 1987; Hedge, 2008, Boi et al., 2017).

Robust deep learning object recognition models rely on low frequency information in natural images.

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.

Encoding fidelity of binocular receptive fields with internal noise in the presence of external variability from natural scenes

Characterizing the properties of receptive fields that are involved in encoding behaviorally-relevant latent variables (binocular disparity, motion) is a focus of visual neuroscience research. The factors that determine which receptive field properties are most useful for encoding a particular latent variable in natural scenes are not well-understood. Here, using a stereo-image database with groundtruth disparity information at each pixel and a nonlinear-linear-nonlinear subunit modeling framework, we examine how external variability and internal noise jointly determine the binocular receptive field properties that are most useful for encoding binocular disparity in natural scenes. We compute the encoding fidelity, as indexed by Fisher information, of binocular receptive field pairs across preferred spatial frequency (SF) and differences in preferred phase disparity (binocular phase shift). Two major findings emerge. First, stimulus disparity and external variability (image and depth variability) determine the usefulness of different encoding subspaces, as defined by the preferred SFs of the binocular receptive field pairs. The most useful preferred SF decreases with stimulus disparity but increases with local depth variability. Second, internal noise determines the usefulness within the encoding subspaces of different basis elements, as defined by the binocular phase shifts of the receptive fields. In particular, with internal noise, encoding fidelity varies with the particular receptive field pairs within the subspace. Non-orthogonal receptive fields are most useful, a result that deviates from the quadrature-pairs posited by the disparity energy model. Most subunit modeling frameworks do not inject noise into the individual subunit responses. This modeling choice renders all subunit receptive field pairs spanning a subspace computationally equivalent. Incorporating subunit response noise makes more specific predictions regarding the particular receptive fields that are most useful for encoding a particular latent variable. The current modeling efforts bring us closer to understanding the principles governing the design of real neural systems in natural scenes.

Mapping spatial frequency preferences across human primary visual cortex.

Neurons in primate visual cortex (area V1) are tuned for spatial frequency, in a manner that depends on their position in the visual field. Several studies have examined this dependency using functional magnetic resonance imaging (fMRI), reporting preferred spatial frequencies (tuning curve peaks) of V1 voxels as a function of eccentricity, but their results differ by as much as two octaves, presumably owing to differences in stimuli, measurements, and analysis methodology. Here, we characterize spatial frequency tuning at a millimeter resolution within the human primary visual cortex, across stimulus orientation and visual field locations. We measured fMRI responses to a novel set of stimuli, constructed as sinusoidal gratings in log-polar coordinates, which include circular, radial, and spiral geometries. For each individual stimulus, the local spatial frequency varies inversely with eccentricity, and for any given location in the visual field, the full set of stimuli span a broad range of spatial frequencies and orientations. Over the measured range of eccentricities, the preferred spatial frequency is well-fit by a function that varies as the inverse of the eccentricity plus a small constant. We also find small but systematic effects of local stimulus orientation, defined in both absolute coordinates and relative to visual field location. Specifically, peak spatial frequency is higher for pinwheel than annular stimuli and for horizontal than vertical stimuli.

Coarse-to-fine processing drives the efficient coding of natural scenes in mouse visual cortex.

SUMMARYThe visual system processes sensory inputs sequentially, perceiving coarse information before fine details. Here we study the neural basis of coarse-to-fine processing and its computational benefits in natural vision. We find that primary visual cortical neurons in awake mice respond to natural scenes in a coarse-to-fine manner, primarily driven by individual neurons rapidly shifting their spatial frequency preference from low to high over a brief response period. This shift transforms the population response in a way that counteracts the statistical regularities of natural scenes, thereby reducing redundancy and generating a more efficient neural representation. The increase in representational efficiency does not occur in either dark-reared or anesthetized mice, which show significantly attenuated coarse-to-fine spatial processing. Collectively, these results illustrate that coarse-to-fine processing is state dependent, develops postnatally via visual experience, and provides a computational advantage by generating more efficient representations of the complex spatial statistics of ethologically relevant natural scenes.

Response Selectivity of the Lateral Posterior Nucleus Axons Projecting to the Mouse Primary Visual Cortex

Neurons in the mouse primary visual cortex (V1) exhibit characteristic response selectivity to visual stimuli, such as orientation, direction and spatial frequency selectivity. Since V1 receives thalamic visual inputs from the lateral geniculate nucleus (LGN) and lateral posterior nucleus (LPN), the response selectivity of the V1 neurons could be influenced mostly by these inputs. However, it remains unclear how these two thalamic inputs contribute to the response selectivity of the V1 neurons. In this study, we examined the orientation, direction and spatial frequency selectivity of the LPN axons projecting to V1 and compared their response selectivity with our previous results of the LGN axons in mice. For this purpose, the genetically encoded calcium indicator, GCaMP6s, was locally expressed in the LPN using the adeno-associated virus (AAV) infection method. Visual stimulations were presented, and axonal imaging was conducted in V1 by two-photon calcium imaging in vivo. We found that LPN axons primarily terminate in layers 1 and 5 and, to a lesser extent, in layers 2/3 and 4 of V1, while LGN axons mainly terminate in layer 4 and, to a lesser extent, in layers 1 and 2/3 of V1. LPN axons send highly orientation- and direction-selective inputs to all the examined layers in V1, whereas LGN axons send highly orientation- and direction-selective inputs to layers 1 and 2/3 but low orientation and direction selective inputs to layer 4 in V1. The distribution of preferred orientation and direction was strongly biased toward specific orientations and directions in LPN axons, while weakly biased to cardinal orientations and directions in LGN axons. In spatial frequency tuning, both the LPN and LGN axons send selective inputs to V1. The distribution of preferred spatial frequency was more diverse in the LPN axons than in the LGN axons. In conclusion, LPN inputs to V1 are functionally different from LGN inputs and may have different roles in the orientation, direction and spatial frequency tuning of the V1 neurons.

Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity.

Task-optimized convolutional neural networks (CNNs) show striking similarities to the ventral visual stream. However, human-imperceptible image perturbations can cause a CNN to make incorrect predictions. Here we provide insight into this brittleness by investigating the representations of models that are either robust or not robust to image perturbations. Theory suggests that the robustness of a system to these perturbations could be related to the power law exponent of the eigenspectrum of its set of neural responses, where power law exponents closer to and larger than one would indicate a system that is less susceptible to input perturbations. We show that neural responses in mouse and macaque primary visual cortex (V1) obey the predictions of this theory, where their eigenspectra have power law exponents of at least one. We also find that the eigenspectra of model representations decay slowly relative to those observed in neurophysiology and that robust models have eigenspectra that decay slightly faster and have higher power law exponents than those of non-robust models. The slow decay of the eigenspectra suggests that substantial variance in the model responses is related to the encoding of fine stimulus features. We therefore investigated the spatial frequency tuning of artificial neurons and found that a large proportion of them preferred high spatial frequencies and that robust models had preferred spatial frequency distributions more aligned with the measured spatial frequency distribution of macaque V1 cells. Furthermore, robust models were quantitatively better models of V1 than non-robust models. Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.

Multiple gamma rhythms carry distinct spatial frequency information in primary visual cortex.

Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.

Functional organization of spatial frequency tuning in macaque V1 revealed with two-photon calcium imaging.

V1 neurons are functionally organized in orientation columns in primates. Whether spatial frequency (SF) columns also exist is less clear because mixed results have been reported. A definitive solution would be SF functional maps at single-neuron resolution. Here we used two-photon calcium imaging to construct first cellular SF maps in V1 superficial layers of five awake fixating macaques, and studied SF functional organization properties and neuronal tuning characteristics. The SF maps (850 × 850 μm2) showed weak horizontal SF clustering (median clustering index = 1.43 vs. unity baseline), about one sixth as strong as orientation clustering in the same sets of neurons, which argues against a meaningful orthogonal relationship between orientation and SF functional maps. These maps also displayed nearly absent vertical SF clustering between two cortical depths (150 & 300 μm), indicating a lack of SF columnar structures within the superficial layers. The underlying causes might be that most neurons were tuned to a narrow two-octave range of medium frequencies, and many neurons with different SF preferences were often spatially mixed, which disallowed finer grouping of SF tuning. In addition, individual SF tuning functions were often asymmetric, having wider lower frequency branches, which may help encode low SF information for later decoding.

The role of early visual experience in the development of spatial-frequency preference in the primary visual cortex.

The mature functioning of the primary visual cortex depends on postnatal visual experience, while the orientation/direction preference is established just after eye-opening, independently of visual experience. In this study, we find that visual experience is required for the normal development of spatial-frequency (SF) preference in mouse primary visual cortex. We show that age- and experience-dependent shifts in optimal SFs towards higher frequencies occurred similarly in excitatory neurons and parvalbumin-positive interneurons. We also show that some excitatory and parvalbumin-positive neurons preferentially responded to visual stimuli consisting of very high SFs and posterior directions, and that the preference was established at earlier developmental stages than the SF preference in the standard frequency range. These results suggest that early visual experience is required for the development of SF representation and shed light on the experience-dependent developmental mechanisms underlying visual cortical functions. Early visual experience is crucial for the maturation of visual cortical functions. It has been demonstrated that the orientation and direction preferences in individual neurons of the primary visual cortex are well established immediately after eye-opening. The postnatal development of spatial frequency (SF) tuning and its dependence on visual experience, however, has not been thoroughly quantified. In this study, macroscopic imaging with flavoprotein autofluorescence revealed that the optimal SFs shift towards higher frequency values during normal development in mouse primary visual cortex. This developmental shift was impaired by binocular deprivation during the sensitive period, postnatal 3weeks (PW3) to PW6. Furthermore, two-photon Ca2+ imaging revealed that the developmental shift of the optimal SFs, depending on visual experience, concurrently occurs in excitatory neurons and parvalbumin-positive inhibitory interneurons (PV neurons). In addition, some excitatory and PV neurons exhibited a preference for visual stimuli consisting of particularly high SFs and posterior directions at relatively early developmental stages; this preference was not affected by binocular deprivation. Thus, there may be two distinct developmental mechanisms for the establishment of SF preference depending on the frequency values. After PW3, SF tuning for neurons tuned to standard frequency ranges was sharper in excitatory neurons and slightly broader in PV neurons, leading to considerably attenuated SF tuning in PV neurons compared to excitatory neurons by PW5. Our findings suggest that early visual experience is far more important than orientation/direction selectivity for the development of the neural representation of the diverse SFs.

Impaired Adaptation and Laminar Processing of the Oddball Paradigm in the Primary Visual Cortex of Fmr1 KO Mouse.

Both adaptation and novelty detection are an integral part of sensory processing. Recent animal oddball studies have advanced our understanding of circuitry underlying contextual processing in early sensory areas. However, it is unclear how adaptation and mismatch (MM) responses depend on the tuning properties of neurons and their laminar position. Furthermore, given that reduced habituation and sensory overload are among the hallmarks of altered sensory perception in autism, we investigated how oddball processing might be altered in a mouse model of fragile X syndrome (FX). Using silicon probe recordings and a novel spatial frequency (SF) oddball paradigm, we discovered that FX mice show reduced adaptation and enhanced MM responses compared to control animals. Specifically, we found that adaptation is primarily restricted to neurons with preferred oddball SF in FX compared to WT mice. Mismatch responses, on the other hand, are enriched in the superficial layers of WT animals but are present throughout lamina in FX animals. Last, we observed altered neural dynamics in FX mice in response to stimulus omissions. Taken together, we demonstrated that reduced feature adaptation coexists with impaired laminar processing of oddball responses, which might contribute to altered sensory perception in FX syndrome and autism.

Uniform spatial pooling explains topographic organization and deviation from receptive-field scale invariance in primate V1

Receptive field (RF) size and preferred spatial frequency (SF) vary greatly across the primary visual cortex (V1), increasing in a scale invariant fashion with eccentricity. Recent studies reveal that preferred SF also forms a fine-scale periodic map. A fundamental open question is how local variability in preferred SF is tied to the overall spatial RF. Here, we use two-photon imaging to simultaneously measure maps of RF size, phase selectivity, SF bandwidth, and orientation bandwidth—all of which were found to be topographically organized and correlate with preferred SF. Each of these newly characterized inter-map relationships strongly deviate from scale invariance, yet reveal a common motif—they are all accounted for by a model with uniform spatial pooling from scale invariant inputs. Our results and model provide novel and quantitative understanding of the output from V1 to downstream circuits.

Local organization of spatial frequency tuning dynamics in the cat visual areas 17 and 18.

Spatial frequency (SF) is a prominent feature to which most neurons in cat areas 17 and 18 (area 17/18) exhibit tuning selectivity. Previous studies have shown that neurons with similar SF tunings are locally clustered into SF preference domains. However, the functional organization of SF tuning remains not fully understood. Neurons in these areas show a variety of SF tuning dynamics; however, it is unknown how neurons with diverse dynamics are locally organized to form the population dynamics of the domains. The laminar organization of SF dynamics is also unknown, knowledge of which may be useful for determining how SF tuning dynamics of cat area 17/18 neurons arise in cortical circuits. To address these issues, we recorded the activities of multiple neurons in the cat area 17/18 using microelectrode arrays and characterized the time courses of the SF tunings of these neurons by a subspace reverse correlation. A wide range of SF dynamics was already present in the input layer, suggesting that intracortical mechanisms contribute to generating SF dynamics inside this layer but do not further shape it outside this layer. Local neuronal pools with similar SF tunings contained diverse SF dynamics. The average preferred SF of a pool similarly increased with response time. Moreover, the range of single-neuron preferred SFs in a pool tended to increase with time. Our results suggest that, in the presence of organized tuning diversity within an SF domain, the cortical SF organization remains stable during response time in cat area 17/18.NEW & NOTEWORTHY In cat area 17/18, we found that a local pool of neurons with similar spatial frequency (SF) tunings shows diverse but organized dynamics. Our results suggest that, in the presence of organized tuning diversity within an SF domain, the cortical SF organization remains stable over response time in these areas. Laminar analysis suggests that intracortical mechanisms contribute to generating SF dynamics inside the input layer but do not further shape it outside this layer.

TRNS effects on visual contrast detection

In recent years, transcranial electrical stimulation (tES) has been used to improve cognitive and perceptual abilities and to boost learning. In the visual domain, transcranial random noise stimulation (tRNS), a type of tES in which electric current is randomly alternating in between two electrodes at high frequency, has shown potential in inducing long lasting perceptual improvements when coupled with tasks such as contrast detection. However, its cortical mechanisms and online effects have not been fully understood yet, and it is still unclear whether these long-term improvements are due to early-stage perceptual enhancements of contrast sensitivity or later stage mechanisms such as learning consolidation. Here we tested tRNS effects on multiple spatial frequencies and orientation, showing that tRNS enhances detection of a low contrast Gabor, but only for oblique orientation and high spatial frequency (12 cycles per degree of visual angle). No improvement was observed for low contrast and vertical stimuli. These results indicate that tRNS can enhance contrast sensitivity already after one training session, however this early onset is dependent on characteristics of the stimulus such as spatial frequency and orientation. In particular, the shallow depth of tRNS is likely to affect superficial layers of the visual cortex where neurons have higher preferred spatial frequencies than cells in further layers, while the lack of effect on vertical stimuli might reflect the optimization of the visual system to see cardinally oriented low contrast stimuli, leaving little room for short-term improvement.Taken together, these results suggest that online tRNS effects on visual perception are the result of a complex interaction between stimulus intensity and cortical anatomy, consistent with previous literature on brain stimulation.

Visual cortex neurons phase-lock selectively to subsets of LFP oscillations.

It is generally thought that apart from receptive field differences, such as preferred orientation and spatial frequency selectivity, primary visual cortex neurons are functionally similar to each other. However, the genetic diversity of cortical neurons plus the existence of inputs additional to those required to explain known receptive field properties might suggest otherwise. Here we report the existence of desynchronized states in anesthetized cat area 17 lasting up to 45 min, characterized by variable narrow-band local field potential (LFP) oscillations in the range 2-100 Hz and the absence of a synchronized 1/f frequency spectrum. During these periods, spontaneously active neurons phase-locked to variable subsets of LFP oscillations. Individual neurons often ignored frequencies that others phase-locked to. We suggest that these desynchronized periods may correspond to REM sleep-like episodes occurring under anesthesia. Frequency-selective codes may be used for signaling during these periods. Hence frequency-selective combination and frequency-labeled pathways may represent a previously unsuspected dimension of cortical organization. NEW & NOTEWORTHY We investigated spontaneous neuronal firing during periods of desynchronized local field potential (LFP) activity, resembling REM sleep, in anesthetized cats. During these periods, neurons synchronized their spikes to specific phases of multiple LFP frequency components, with some neurons ignoring frequencies that others were synchronized to. Some neurons fired at phase alignments of frequency pairs, thereby acting as phase coincidence detectors. These results suggest that internal brain signaling may use frequency combination codes to generate temporally structured spike trains.