Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency

Spatial frequency (SF) and orientation tuning are intrinsic properties of neurons in primary visual cortex (area V1). To investigate the neural mechanisms mediating selectivity in the awake animal, we measured the temporal dynamics of SF and orientation tuning. We adapted a high-speed reverse-correlation method previously used to characterize orientation tuning dynamics in anesthetized animals to estimate efficiently the complete spatiotemporal receptive fields in area V1 of behaving macaques. We found that SF and orientation tuning are largely separable over time in single neurons. However, spatiotemporal receptive fields also contain a small nonseparable component that reflects a significant difference in response latency for low and high SF stimuli. The observed relationship between stimulus SF and latency represents a dynamic shift in SF tuning, and suggests that single V1 neurons might receive convergent input from the magno-and parvocellular processing streams. Although previous studies with anesthetized animals suggested that orientation tuning could change dramatically over time, we find no substantial evidence of dynamic changes in orientation tuning.reverse correlation ͉ striate cortex S patial frequency (SF) and orientation tuning are two of the most prominent features of neuronal selectivity in primary visual cortex (area V1) (1). The dynamics of SF and orientation tuning and their interactions are of particular interest to neurophysiologists because they can reveal important information about the specific circuits and mechanisms of neuronal selectivity (2). Dynamic tuning properties may also be critical for understanding natural vision, where eye movements can introduce complex temporal stimulus dynamics (3, 4).We simultaneously measured the temporal dynamics of both SF and orientation tuning in single V1 neurons in the awake primate. Conventional methods for estimating selectivity in V1 use stationary or drifting gratings of constant SF and orientation. Stimuli are presented for relatively long periods (0.3-1 s), and responses are quantified by mean firing rate. Often one stimulus parameter is varied at a time (e.g., orientation tuning is measured at the best SF). Although this approach can yield accurate orientation and SF tuning estimates, it fails to capture both stimulus interactions and temporal response dynamics that can be obscured by nonspecific onset transients. In addition, methods based on static stimuli can be time-consuming. Efficient characterization methods are particularly important when working with behaving animals, where time is almost always at a premium.One efficient way to characterize tuning that also captures temporal response dynamics is to estimate spatiotemporal receptive fields (STRFs) by using reverse correlation. The STRF describes the probability that a particular spatial stimulus will elicit a spike at a particular latency. In effect, the STRF provides a linear model of a neuron's spatiotemporal filtering characteristics (5, 6).In the visual system, STRFs are traditi...

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“…These tuning characteristics are similar to those reported in studies of area V1 using flashed or drifting gratings (14,15).…”

Section: Resultssupporting

confidence: 85%

Spatial frequency and orientation tuning dynamics in area V1

Mazer

Vinje²,

McDermott³

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

262

246

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“…More generally, this type of cell, which we call bar cells in the following, respond most strongly to single bars and their responses decrease with the addition of further parallel bars to make a grating. In previous studies Schiller et al (1976) also found many cells in area V1 which responded strongly to single bars and edges but did not respond to sine-wave gratings. Blakemore and Tobin (1972) measured the response of a 'complex' cell to a white bar of optimal orientation, position and size in the presence of a bar grating covering the area outside a circle which was somewhat larger than the region in which the cell responded to the bar stimulus.…”

Section: Introductionmentioning

confidence: 77%

“…The plausibility of this extension can be tested by measuring the response of single-bar cells of the type described by Schiller et al (1976) and von der Heydt et al (1992) as a function of both the width of the bars and the (fundamental) frequency of the bar grating. The computational model proposed above concerns the process of conveying or not conveying the activities of simple and complex cells to higher stages.…”

Section: Computational Model Of Bar Cellsmentioning

confidence: 99%

Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells

Petkov

Kruizinga

1997

Biological Cybernetics

151

126

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Abstract. Computational models of periodic-and aperiodicpattern selective cells, also called grating and bar cells, respectively, are proposed. Grating cells are found in areas V1 and V2 of the visual cortex of monkeys and respond strongly to bar gratings of a given orientation and periodicity but very weakly or not at all to single bars. This nonlinear behaviour, which is quite different from the spatial frequency filtering behaviour exhibited by the other types of orientation-selective neurons such as the simple cells, is incorporated in the proposed computational model by using an AND-type non-linearity to combine the responses of simple cells with symmetric receptive field profiles and opposite polarities.

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“…Figure 9 demonstrates the spatial specificity of BOLD by exploiting the non-uniform distribution of directionally selective cells across the layers of the striate cortex. About one-third of the striate cells are known to be directionally selective (Goldberg & Wurtz 1972;Schiller et al 1976;De Valois et al 1982;Albright 1984;Colby et al 1993). However, these cells do not exhibit a uniform laminar distribution.…”

Section: (Iii) Spatial Specificity Of Bold Fmrimentioning

confidence: 99%

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

Logothetis

2002

Phil. Trans. R. Soc. Lond. B

817

516

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Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygenlevel-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single-and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.

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Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency

Cited by 203 publications

References 3 publications

Spatial frequency and orientation tuning dynamics in area V1

Spatial frequency and orientation tuning dynamics in area V1

Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells

The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal

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