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Background. Catheter tip malposition frequently occurs with subclavian vein catheterization.The supine position returns more and faster blood to the heart than the head down position.It is assumed that the soft catheter is easy to follow the flow forward. This study investigates the influence of catheter flexibility and position on right subclavian catheter placement. Methods. Patients who required subclavian venipuncture catheterization at the Department of Hematology and oncology, Shenzhen Children's hospital were randomly divided into two groups: test group A (80 patients) vs control group B (81 patients).Group A was in the flat position, and the guide wire was partially retracted during catheter placement.Group B used the traditional method of catheterization.Intraoperative ultrasound was used to check the direction of the catheter tip and postoperative chest X-ray was performed. Results. The correct rate of catheter tip orientation at the first insertion in group A was higher than that in group B (86.2% vs 66.7%, P < 0.05), and the rate of catheter misplacement in right internal jugular vein in group A was lower than that in group B (10.0% vs 29.6%, P < 0.05). The incidence of intraoperative arrhythmia in group A was lower than that in group B (0.0% vs 14.8%, P < 0.05).No significant differences in general data and other complications between them. Repeated catheterization failed in 5 patients,and other methods were used for catheterization. Conclusions. The use of the guide wire in the supine position can reduce catheter malposition in children with right subclavian vein catheterization.

Microsaccades play a critical role in refreshing visual information and have been shown to have direction-specific influences on human perception. However, the neural mechanisms underlying such direction-specific effects remains unknown. Here, we report the emergence of direction-specific microsaccade modulation in the middle layer of V2 but not in V1: responses of V2 neurons after microsaccades moved toward their receptive fields were stronger than those when microsaccades moved away. The decreased responses from V1 to V2, which are correlated with the amplitude of microsaccades away from receptive fields, suggest topographically location-specific suppression from an oculomotor source. Consistent with directional effects in V2, microsaccades function as a guide for monkeys’ behavior in a peripheral detection task; both can be explained by a dynamic neural network. Our findings suggest a V1-bypassing suppressive circuit for direction-specific microsaccade modulation in V2 and its functional influence on visual sensitivity, which highlights the optimal sampling nature of microsaccades.

Stimulus-dependence of gamma oscillations (GAMMA, 30–90 Hz) has not been fully understood, but it is important for revealing neural mechanisms and functions of GAMMA. Here, we recorded spiking activity (MUA) and the local field potential (LFP), driven by a variety of plaids (generated by two superimposed gratings orthogonal to each other and with different contrast combinations), in the primary visual cortex of anesthetized cats. We found two distinct narrow-band GAMMAs in the LFPs and a variety of response patterns to plaids. Similar to MUA, most response patterns showed that the second grating suppressed GAMMAs driven by the first one. However, there is only a weak site-by-site correlation between cross-orientation interactions in GAMMAs and those in MUAs. We developed a normalization model that could unify the response patterns of both GAMMAs and MUAs. Interestingly, compared with MUAs, the GAMMAs demonstrated a wider range of model parameters and more diverse response patterns to plaids. Further analysis revealed that normalization parameters for high GAMMA, but not those for low GAMMA, were significantly correlated with the discrepancy of spatial frequency between stimulus and sites’ preferences. Consistent with these findings, normalization parameters and diversity of high GAMMA exhibited a clear transition trend and region difference between area 17 to 18. Our results show that GAMMAs are also regulated in the form of normalization, but that the neural mechanisms for these normalizations might differ from those of spiking activity. Normalizations in different brain signals could be due to interactions of excitation and inhibitions at multiple stages in the visual system.

Background Accurately positioning totally implantable venous access device (TIVAD) catheters and reducing complications in pediatric patients are important and challenging. A number of studies have shown methods for locating the tip of the TIVAD catheter. We assessed the success and complications of TIVAD implantation guided by transesophageal echocardiography (TEE) via the internal jugular vein (IJV) for 294 patients in this retrospective study. Methods From May 2019 to March 2021, 297 cases of TIVADs in our hospital were analyzed in this observational, non-randomized, single-center study. The position of the catheter tip under TEE and chest radiography and rates of periprocedural, early, and late complications were evaluated. Results The implantation was successful in 242 (82.3%) cases which was in a proper position, and the results were consistent with those of postoperative chest radiography. A total of 72 complications were recorded. Of these, 1 case had a perioperative complication, 66 had early complications, and 5 had late complications after port implantation. The most common complications were local infection and catheter malposition, namely 10 (13.9%) cases of incision infection and 58 (80.6%) cases of catheter malposition. In total, 6 (8.3%) cases of port explantation were required. Conclusion Confirmation of proper TIVAD catheter positioning by TEE through an internal jugular approach in children was accurate and safe.

Genetically-encoded calcium indicators (GECI) are indispensable tools for real-time monitoring of intracellular calcium signals and cellular activities in living organisms. Current GECIs face the challenge of sub-optimal peak signal-to-baseline-ratio (SBR) with limited resolution for reporting subtle calcium transients. We report herein the development of a suite of calcium sensors, designated NEMO, with fast kinetics and wide dynamic ranges (>100-fold). NEMO indicators report Ca2+transients with peak SBRs ~20-fold larger than the top-of-the-range GCaMP series. NEMO sensors further enable the quantification of absolution calcium concentration with ratiometric or photochromic imaging. Compared to GCaMPs, NEMOs could detect single action potentials in neurons with a peak SBR two times higher and a median peak SBR four times largerin vivo, thereby outperforming most existing state-of-the-art GECIs. Given their high sensitivity and resolution to report intracellular Ca2+signals, NEMO sensors may find broad applications in monitoring neuronal activities and other Ca2+-modulated physiological processes in both mammals and plants.

Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in information processing. Studies have shown that the response property of GAMMA is related to neural interaction through local recurrent connections (RC), feed-forward (FF), and feedback (FB) connections. However, the relationship between GAMMA and long-range horizontal connections (HC) in the brain remains unclear. Here, we aimed to understand this question in a large-scale network model for the primary visual cortex (V1). We created a computational model composed of multiple excitatory and inhibitory units with biologically plausible connectivity patterns for RC, FF, FB, and HC in V1; then, we quantitated GAMMA in network models at different strength levels of HC and other connection types. Surprisingly, we found that HC and FB, the two types of large-scale connections, play very different roles in generating and modulating GAMMA. While both FB and HC modulate a fast gamma oscillation (around 50-60 Hz) generated by FF and RC, HC generates a new GAMMA oscillating around 30 Hz, whose power and peak frequency can also be modulated by FB. Furthermore, response properties of the two GAMMAs in a network with both HC and FB are different in a way that is highly consistent with a recent experimental finding for distinct GAMMAs in macaque V1. The results suggest that distinct GAMMAs are signatures for neural connections in different spatial scales and they might be related to different functions for information integration. Our study, for the first time, pinpoints the underlying circuits for distinct GAMMAs in a mechanistic model for macaque V1, which might provide a new framework to study multiple gamma oscillations in other cortical regions.

Both surface luminance and edge contrast of an object are essential features for object identification. However, cortical processing of surface luminance remains unclear. In this study, we aim to understand how the primary visual cortex (V1) processes surface luminance information across its different layers. We report that edge-driven responses are stronger than surface-driven responses in V1 input layers, but luminance information is coded more accurately by surface responses. In V1 output layers, the advantage of edge over surface responses increased eight times and luminance information was coded more accurately at edges. Further analysis of neural dynamics shows that such substantial changes for neural responses and luminance coding are mainly due to non-local cortical inhibition in V1’s output layers. Our results suggest that non-local cortical inhibition modulates the responses elicited by the surfaces and edges of objects, and that switching the coding strategy in V1 promotes efficient coding for luminance., How brightness is encoded in the visual cortex remains incompletely understood. By recording from macaque V1, the authors revealed a switch from surface to edge encoding that is mediated by widespread inhibition in the output layers of the cortex.

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.

Gamma-band activity, peaking around 30–100 Hz in the local field potential's power spectrum, has been found and intensively studied in many brain regions. Although gamma is thought to play a critical role in processing neural information in the brain, its cognitive functions and neural mechanisms remain unclear or debatable. Experimental studies showed that gamma rhythms are stochastic in time and vary with visual stimuli. Recent studies further showed that multiple rhythms coexist in V1 with distinct origins in different species. While all these experimental facts are a challenge for understanding the functions of gamma in the visual cortex, there are many signs of progress in computational studies. This review summarizes and discusses studies on gamma in the visual cortex from multiple perspectives and concludes that gamma rhythms are still a mystery. Combining experimental and computational studies seems the best way forward in the future.

Cortical inhibition plays an important role in information processing in the brain. However, the mechanisms by which inhibition and excitation are coordinated to generate functions in the six layers of the cortex remain unclear. Here, we measured laminar-specific responses to stimulus orientations in primary visual cortex (V1) of awake monkeys (male, Macaca mulatta). We distinguished inhibitory effects (suppression) from excitation, by taking advantage of the separability of excitation and inhibition in the orientation and time domains. We found two distinct types of suppression governing different layers. Fast suppression (FS) was strongest in input layers (4C and 6), and slow suppression (SS) was 3 times stronger in output layers (2/3 and 5). Interestingly, the two types of suppression were correlated with different functional properties measured with drifting gratings. FS was primarily correlated with orientation selectivity in input layers (r = −0.65, p < 10−9), whereas SS was primarily correlated with surround suppression in output layers (r = 0.61, p < 10−4). The earliest SS in layer 1 indicates the origin of cortical feedback for SS, in contrast to the feedforward/recurrent origin of FS. Our results reveal two V1 laminar subnetworks with different response suppression that may provide a general framework for laminar processing in other sensory cortices. SIGNIFICANCE STATEMENT This study sought to understand inhibitory effects (suppression) and their relationships with functional properties in the six different layers of the cortex. We found that the diversity of neural responses across layers in primary visual cortex (V1) could be fully explained by one excitatory and two suppressive components (fast and slow suppression). The distinct laminar distributions, origins, and functional roles of the two types of suppression provided a simplified representation of the differences between two V1 subnetworks (input network and output network). These results not only help to elucidate computational principles in macaque V1, but also provide a framework for general computation of cortical laminae in other sensory cortices.

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., Gamma rhythms in many brain regions are thought to play a role in information processing. This study reports the surprising coexistence of three narrow-band gamma rhythms in visual cortex with distinct coding properties for visual features and distinct neural origins.

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity., Author summary It is well known that the computation of neuronal circuits is carried out through the staged and cascade structure of different types of neurons. Nevertheless, the information, particularly sensory information, is processed in a network primarily with feedforward connections through different pathways. A peculiar example is the early visual system, where light is transcoded by the retinal cells, routed by the lateral geniculate nucleus, and reached the primary visual cortex. One meticulous interest in recent years is to map out these physical structures of neuronal pathways. However, most methods so far are limited to taking snapshots of a static view of connections between neurons. It remains unclear how to obtain a functional and dynamical neuronal circuit beyond the simple scenarios of a few randomly sampled neurons. Using simulated spiking neural networks of visual pathways with different scenarios of multiple stages, mixed cell types, and natural image stimuli, we demonstrate that a recent computational tool, named spike-triggered non-negative matrix factorization, can resolve these issues. It enables us to recover the entire structural components of neural networks underlying the computation, together with the functional components of each individual neuron. Utilizing it for complex cells of the primary visual cortex allows us to reveal every underpinning of the nonlinear computation. Our results, together with other recent experimental and computational efforts, show that it is possible to systematically dissect neural circuitry with detailed structural and functional components.

Neuronal responses to prolonged stimulation attenuate over time. Here, we ask a fundamental question: is adaptation a simple process for the neural system during which sustained input is ignored, or is it actually part of a strategy for the neural system to adjust its encoding properties dynamically? After simultaneously recording the activities of a group of bullfrog's retinal ganglion cells (dimming detectors) in response to sustained dimming stimulation, we applied a combination of information analysis approaches to explore the time-dependent nature of information encoding during the adaptation. We found that at the early stage of the adaptation, the stimulus information was mainly encoded in firing rates, whereas at the late stage of the adaptation, it was more encoded in neural correlations. Such a transition in encoding properties is not a simple consequence of the attenuation of neuronal firing rates, but rather involves an active change in the neural correlation strengths, suggesting that it is a strategy adopted by the neural system for functional purposes. Our results reveal that in encoding a prolonged stimulation, the neural system may utilize concerted, but less active, firings of neurons to encode information.

Neurons in primary visual cortex, V1, very often have extraclassical receptive fields (eCRFs). The eCRF is defined as the region of visual space where stimuli cannot elicit a spiking response but can modulate the response of a stimulus in the classical receptive field (CRF). We investigated the dependence of the eCRF on stimulus contrast and orientation in macaque V1 cells for which the laminar location was determined. The eCRF was more sensitive to contrast than the CRF across the whole population of V1 cells with the greatest contrast differential in layer 2/3. We confirmed that many V1 cells experience stronger suppression for collinear than orthogonal stimuli in the eCRF. Laminar analysis revealed that the predominant bias for collinear suppression was found in layers 2/3 and 4b. The laminar pattern of contrast and orientation dependence suggests that eCRF suppression may derive from different neural circuits in different layers, and may be comprised of two distinct components: orientation-tuned and untuned suppression. On average tuned suppression was delayed by ∼25 ms compared with the onset of untuned suppression. Therefore, response modulation by the eCRF develops dynamically and rapidly in time.

The local field potential (LFP) and multiunit activity (MUA) are extracellularly recorded signals that describe local neuronal network dynamics. In our experiments, the LFP and MUA, recorded from the same electrode in macaque primary visual cortex V1 in response to drifting grating visual stimuli, were evaluated on coarse timescales (∼1–5 s) and fine timescales (

Consistent with human perceptual data, we found many more black-dominant than white-dominant responses in layer 2/3 neurons of the macaque primary visual cortex (V1). Seeking the mechanism of this black dominance of layer 2/3 neurons, we measured the laminar pattern of population responses (multiunit activity and local field potential) and found that a small preference for black is observable in early responses in layer 4Cβ, the parvocellular-input layer, but not in the magnocellular-input layer 4Cα. Surprisingly, further analysis of the dynamics of black-white responses in layers 4Cβ and 2/3 suggested that black-dominant responses in layer 2/3 were not generated simply because of the weak black-dominant inputs from 4Cβ. Instead, our results indicated the neural circuitry in V1 is wired with a preference to strengthen black responses. We hypothesize that this selective wiring could be due to (1) feedforward connectivity from black-dominant neurons in layer 4C to cells in layer 2/3 or (2) recurrent interactions between black-dominant neurons in layer 2/3, or a combination of both.

We addressed how spatial frequency and orientation selectivity coexist and co-vary in Macaque primary visual cortex (V1) by simulating cortical layer 4Cα of V1 with a large-scale network model and then comparing the model’s behavior with a population of cells we recorded in layer 4Cα. We compared the distributions of orientation and spatial frequency selectivity, as well as the correlation between the two, in the model with what we observed in the 4Cα population. We found that (1) in the model, both spatial frequency and orientation selectivity of neuronal firing are greater and more diverse than the LGN inputs to model neurons; (2) orientation and spatial frequency selectivity co-vary in the model in a way very similar to what we observed in layer 4Cα neurons; (3) in the model, orientation and spatial frequency selectivity co-vary because of intra-cortical inhibition. The results suggest that cortical inhibition provides a common mechanism for selectivity in multiple dimensions.

Achromatic visual information is transferred from the retina to the brain through two parallel channels: ON-center cells carry “white” information and OFF-center cells “black” information (Nelson et al., 1978; Schiller, 1982; Schiller et al., 1986). Responses of ON and OFF retinal and thalamic neurons are approximately equal in magnitude (Krüger and Fischer, 1975; Kremers et al., 1993), but psychophysical studies have shown that humans detect light decrements (black) better and faster than increments (white) (Blackwell, 1946; Short, 1966; Krauskopf, 1980; Whittle, 1986; Bowen et al., 1989; Chan and Tyler, 1992; Kontsevich and Tyler, 1999; Chubb and Nam, 2000; Dannemiller and Stephens, 2001). From recordings of single-cell activity in the macaque monkey's primary visual cortex (V1), we found that black-dominant neurons substantially outnumbered white-dominant neurons in the corticocortical output layers 2/3, but the numbers of black- and white-dominant neurons were nearly equal in the thalamocortical input layer 4c. These results strongly suggest that the black-over-white preference is generated or greatly amplified in V1. The predominance of OFF neurons in layers 2/3 of V1, which provide visual input to higher cortical areas, may explain why human subjects detect black more easily than white. Furthermore, our results agree with human EEG and fMRI findings that V1 responses to decrements are stronger than to increments, though the OFF/ON imbalance we found in layers 2/3 of macaque V1 is much larger than in the whole V1 population in the human V1 experiments (Zemon et al., 1988, 1995; Olman et al., 2008).

Visual cortical neurones display a variety of visual properties. Among those that emerge in the primary visual cortex V1 are sharpening of selectivity for spatial frequency and for orientation. The selectivity for these stimulus attributes can be measured around the peak of the tuning function, usually as bandwidth. Other selectivity measures take into account the response across a broader range of stimulus values. An example of such a global measure is the circular variance of orientation tuning. Here we introduce a similar measure in the spatial frequency domain that takes into account the shape of the tuning curve at frequencies lower than the peak, called the low-spatial frequency variance. Our recent studies with dynamic stimuli suggest that the selectivity for spatial frequency and orientation is strongly correlated with the degree of suppression at low spatial frequencies and off-axis orientations. Here we extend the study of the global tuning to stimulus conditions that measure the response of cells to the presentation of drifting sinusoidal grating stimuli for periods of a few seconds. We find that under such steady-state stimulus conditions there is a strong correlation between the global selectivity measures, orientation circular variance and low spatial frequency variance. Consistent with previous studies, there is a weaker correlation between the local tuning measures, orientation and spatial frequency bandwidth. These results support the idea that there are multiple factors that contribute to tuning and that suppression observed in dynamic experiments is also likely to contribute to the global selectivity for steady-state stimuli.

Darkness and brightness are very different perceptually. To understand the neural basis for the visual difference, we studied the dynamical states of populations of neurons in macaque primary visual cortex when a spatially uniform area (8° × 8°) of the visual field alternated between black and white. Darkness evoked sustained nerve-impulse spiking in primary visual cortex neurons, but bright stimuli evoked only a transient response. A peak in the local field potential (LFP) γ band (30–80 Hz) occurred during darkness; white-induced LFP fluctuations were of lower amplitude, peaking at 25 Hz. However, the sustained response to white in the evoked LFP was larger than for black. Together with the results on spiking, the LFP results imply that, throughout the stimulus period, bright fields evoked strong net sustained inhibition. Such cortical brightness adaptation can explain many perceptual phenomena: interocular speeding up of dark adaptation, tonic interocular suppression, and interocular masking.

Neural activity in the gamma frequency range (“gamma”) is elevated during active cognitive states. Gamma has been proposed to play an important role in cortical function, although this is debated. Understanding what function gamma might fulfill requires a better understanding of its properties and the mechanisms that generate it. Gamma is characterized by its spectral power and peak frequency, and variations in both parameters have been associated with changes in behavioral performance. Modeling studies suggest these properties are co-modulated, but this has not been established. To test the relationship between these properties, we measured local field potentials (LFPs) and neuronal spiking responses in primary visual cortex of anesthetized monkeys, for drifting sinusoidal gratings of different sizes, contrasts, orientations and masked with different levels of noise. We find that there is no fixed relationship between LFP gamma power and peak frequency, and neither is related to the strength of spiking activity. We propose a simple model that can account for the complex stimulus dependence we observe, and suggest that separate mechanisms determine gamma power and peak frequency.

Studying the laminar pattern of neural activity is crucial for understanding the processing of neural signals in the cerebral cortex. We measured neural population activity [multiunit spike activity (MUA) and local field potential, LFP] in Macaque primary visual cortex (V1) in response to drifting grating stimuli. Sustained visually driven MUA was at an approximately constant level across cortical depth in V1. However, sustained, visually driven, local field potential power, which was concentrated in the γ-band (20–60 Hz), was greatest at the cortical depth corresponding to cortico-cortical output layers 2, 3, and 4B. γ-band power also tends to be more sustained in the output layers. Overall, cortico-cortical output layers accounted for 67% of total γ-band activity in V1, whereas 56% of total spikes evoked by drifting gratings were from layers 2, 3, and 4B. The high-resolution layer specificity of γ-band power, the laminar distribution of MUA and γ-band activity, and their dynamics imply that neural activity in V1 is generated by laminar-specific mechanisms. In particular, visual responses of MUA and γ-band activity in cortico-cortical output layers 2, 3, and 4B seem to be strongly influenced by laminar-specific recurrent circuitry and/or feedback.

Theoretical considerations have led to the concept that the cerebral cortex is operating in a balanced state in which synaptic excitation is approximately balanced by synaptic inhibition from the local cortical circuit. This paper is about the functional consequences of the balanced state in sensory cortex. One consequence is gain control: there is experimental evidence and theoretical support for the idea that local circuit inhibition acts as a local automatic gain control throughout the cortex. Second, inhibition increases cortical feature selectivity: many studies of different sensory cortical areas have reported that suppressive mechanisms contribute to feature selectivity. Synaptic inhibition from the local microcircuit should be untuned (or broadly tuned) for stimulus features because of the microarchitecture of the cortical microcircuit. Untuned inhibition probably is the source of Untuned Suppression that enhances feature selectivity. We studied inhibition's function in our experiments, guided by a neuronal network model, on orientation selectivity in the primary visual cortex, V1, of the Macaque monkey. Our results revealed that Untuned Suppression, generated by local circuit inhibition, is crucial for the generation of highly orientation-selective cells in V1 cortex.

One of the functions of the cerebral cortex is to increase the selectivity for stimulus features. Finding more about the mechanisms of increased cortical selectivity is important for understanding how the cortex works. Up to now, studies in multiple cortical areas have reported that suppressive mechanisms are involved in feature selectivity. However, the magnitude of the contribution of suppression to tuning selectivity is not yet determined. We use orientation selectivity in macaque primary visual cortex, V1, as an archetypal example of cortical feature selectivity and develop a method to estimate the magnitude of the contribution of suppression to orientation selectivity. The results show that untuned suppression, one form of cortical suppression, decreases the orthogonal-to-preferred response ratio (O/Pratio) of V1 cells from an average of 0.38 to 0.26. Untuned suppression has an especially large effect on orientation selectivity for highly selective cells (O/P< 0.2). Therefore, untuned suppression is crucial for the generation of highly orientation-selective cells in V1 cortex.

Gamma-band (25–90 Hz) peaks in local field potential (LFP) power spectra are present throughout the cerebral cortex and have been related to perception, attention, memory, and disorders (e.g., schizophrenia and autism). It has been theorized that gamma oscillations provide a “clock” for precise temporal encoding and “binding” of signals about stimulus features across brain regions. For gamma to function as a clock, it must be autocoherent: phase and frequency conserved over a period of time. We computed phase and frequency trajectories of gamma-band bursts, using time-frequency analysis of LFPs recorded in macaque primary visual cortex (V1) during visual stimulation. The data were compared with simulations of random networks and clock signals in noise. Gamma-band bursts in LFP data were statistically indistinguishable from those found in filtered broadband noise. Therefore, V1 LFP data did not contain clock-like gamma-band signals. We consider possible functions for stochastic gamma-band activity, such as a synchronizing pulse signal.

Gamma-band peaks in the power spectrum of local field potentials (LFP) are found in multiple brain regions. It has been theorized that gamma oscillations may serve as a ‘clock’ signal for the purposes of precise temporal encoding of information and ‘binding’ of stimulus features across regions of the brain. Neurons in model networks may exhibit periodic spike firing or synchronized membrane potentials that give rise to a gamma-band oscillation that could operate as a ‘clock’. The phase of the oscillation in such models is conserved over the length of the stimulus. We define these types of oscillations to be ‘autocoherent’. We investigated the hypothesis that autocoherent oscillations are the basis of the experimentally observed gamma-band peaks: the autocoherent oscillator (ACO) hypothesis. To test the ACO hypothesis, we developed a new technique to analyze the autocoherence of a time-varying signal. This analysis used the continuous Gabor transform to examine the time evolution of the phase of each frequency component in the power spectrum. Using this analysis method, we formulated a statistical test to compare the ACO hypothesis with measurements of the LFP in macaque primary visual cortex, V1. The experimental data were not consistent with the ACO hypothesis. Gamma-band activity recorded in V1 did not have the properties of a ‘clock’ signal during visual stimulation. We propose instead that the source of the gamma-band spectral peak is the resonant V1 network driven by random inputs.

We developed a new method to estimate the spatial extent of summation, the cortical spread, of the local field potential (LFP) throughout all layers of macaque primary visual cortex V1 by taking advantage of the V1 retinotopic map. We mapped multi-unit activity and LFP visual responses with sparse-noise at several cortical sites simultaneously. The cortical magnification factor near the recording sites was precisely estimated by track reconstruction. The new method combined experimental measurements together with a model of signal summation to obtain the cortical spread of the LFP. This new method could be extended to cortical areas that have topographic maps such as S1 or A1, and to cortical areas without functional columnar maps, such as rodent visual cortex. In macaque V1, the LFP was the sum of signals from a very local region, the radius of which was on average 250 μm. The LFP's cortical spread varied across cortical layers, reaching a minimum value of 120 μm in layer 4B. An important functional consequence of the small cortical spread of the LFP is that the visual field maps of LFP and MUA recorded at a single electrode site were very similar. The similar spatial scale of the visual responses, the restricted cortical spread, and their laminar variation led to new insights about the sources and possible applications of the LFP.

The concept of receptive field is a linear, feed-forward view of visual signal processing. Frequently used models of V1 neurons, like the dynamic linear filter static nonlinearity Poisson spike encoder model, predict that receptive fields measured with different stimulus ensembles should be similar. Here, we tested this concept by comparing spatiotemporal maps of V1 neurons derived from two very different, but commonly used, stimulus ensembles: sparse noise and Hartley subspace stimuli. We found maps from the two methods agreed for neurons in input layer 4C but were very different for neurons in superficial layers of V1. Many layer 2/3 cells have receptive fields with multiple elongated subregions when mapped with Hartley stimuli, but their spatial maps collapse to only a single, less-elongated subregion when mapped with sparse noise. Moreover, for upper layer V1 neurons, the preferred orientation for Hartley maps is much closer to the preferred orientation measured with drifting gratings than is the orientation preference of sparse-noise maps. These results challenge the concept of a stimulus-invariant receptive field and imply that intracortical interactions shape fundamental properties of layer 2/3 neurons.

There is a transformation in behavior in the visual system of cats and primates, from neurons in the Lateral Geniculate Nucleus (LGN) that are not tuned for orientation to orientation-tuned cells in primary visual cortex (V1). The visual stimuli that excite V1 can be well controlled, and the thalamic inputs to V1 from the LGN have been measured precisely. Much has been learned about basic principles of cortical neurophysiology on account of the intense investigation of the transformation between LGN and V1. Here we present a discussion of different models for visual cortex and orientation selectivity, and then discuss our own experimental findings about the dynamics of orientation selectivity. We consider what these theoretical analyses and experimental results imply about cerebral cortical function. The conclusion is that there is a very important role for intracortical interactions, especially cortico-cortical inhibition, in producing neurons in the visual cortex highly selective for orientation.

There is a transformation in behavior in the visual system of cats and primates, from neurons in the Lateral Geniculate Nucleus (LGN) that are not tuned for orientation to orientation-tuned cells in primary visual cortex (V1). The visual stimuli that excite V1 can be well controlled, and the thalamic inputs to V1 from the LGN have been measured precisely. Much has been learned about basic principles of cortical neurophysiology on account of the intense investigation of the transformation between LGN and V1. Here we present a discussion of different models for visual cortex and orientation selectivity, and then discuss our own experimental findings about the dynamics of orientation selectivity. We consider what these theoretical analyses and experimental results imply about cerebral cortical function. The conclusion is that there is a very important role for intracortical interactions, especially cortico-cortical inhibition, in producing neurons in the visual cortex highly selective for orientation.

Previous research has established that orientation selectivity depends to a great extent on suppressive mechanisms in the visual cortex. In this study, we investigated the spatial organization and the time-course of these mechanisms. Stimuli were presented in circular windows of “optimal” and “large” radii. The two stimulus sizes were chosen based on an area-response function measured with drifting gratings at high contrast. The “optimal” size was defined as the smallest radius that elicited the peak response (average value of 0.45°), whereas “large” was defined as two to five times the optimal size. We found that the peak amplitude of tuned enhancement and untuned suppression varied

Currently there is considerable debate as to the nature of the pathways that are responsible for the perception and motor performance. We have studied the relationship between perceived speed, which is the experiential representation of a moving stimulus, and the speed of smooth pursuit eye movements, the motor action. We determined psychophysical thresholds for detecting small perturbations in the speed of moving patterns, and then by an ideal observer analysis computed analogous "oculometric" thresholds from the eye movement traces elicited by the same stimuli on the same trials. Our results confirm those of previous studies that show a remarkable agreement between perceptual judgments for speed discrimination and the fine gradations in eye movement speed. We analyzed the initial pursuit period of long duration (1000 ms) and short (200 ms) duration perturbations. When we compared the errors for perception and pursuit on a trial-by-trial basis there was no correlation between perceptual errors and eye movement errors. The observation that both oculometric and psychometric performance were similar, with Weber fractions in the normal range, but that there is no correlation in the errors suggests that the motor system and perception share the same constraints in their analysis of motion signals, but act independently and have different noise sources. We simulated noise in two models of perceptual and eye movement performance. In the first model we postulate an initial common source for the perceptual and eye movement signals. In that case about ten times the observed noise is required to produce no correlation in trial-by-trial performance. In the second model we postulate that the perceptual signal is a combination of a reafferent eye velocity signal plus the perturbation signal while the pursuit signal is derived from the oculomotor plant plus the perturbation signal. In this model about three times the noise level in the independent signals will mask any correlation due to the common perturbation signal.

Oscillatory neural activity within the gamma band (25–90 Hz) is generally thought to be able to provide a timing signal for harmonizing neural computations across different brain regions. Using time–frequency analyses of the dynamics of gamma-band activity in the local field potentials recorded from monkey primary visual cortex, we found identical temporal characteristics of gamma activity in both awake and anesthetized brain states, including large variability of peak frequency, brief oscillatory epochs (