Receptive Fields and Response Properties of Neurons in Layer 4 of Ferret Visual Cortex

Usrey, W. Martin; Sceniak, Michael P.; Chapman, Barbara

doi:10.1152/jn.00749.2002

Cited by 58 publications

(46 citation statements)

References 78 publications

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“…3). The filters produced by the LN model in response to natural stimuli and those produced by either model in response to noise stimuli showed no significant difference in preferred orientation from the orientations determined from studies with gratings, in agreement with previous studies (Smyth et al 2003;Usrey et al 2003). However, the correlation values are smaller than for comparisons between different filters (noise STA: R 2 ϭ 0.29, P ϭ 0.3, paired Wilcoxon test; noise MID: R 2 ϭ 0.24, P ϭ 0.3, paired Wilcoxon test, not shown in Fig.…”

Section: Orientation Selectivitysupporting

confidence: 91%

On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli

Sharpee

Miller²,

Stryker³

2008

Journal of Neurophysiology

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Sharpee TO, Miller KD, Stryker MP. On the importance of static nonlinearity in estimating spatiotemporal neural filters with natural stimuli. J Neurophysiol 99: 2496 -2509. First published March 19, 2008 doi:10.1152/jn.01397.2007. Understanding neural responses with natural stimuli has increasingly become an essential part of characterizing neural coding. Neural responses are commonly characterized by a linear-nonlinear (LN) model, in which the output of a linear filter applied to the stimulus is transformed by a static nonlinearity to determine neural response. To estimate the linear filter in the LN model, studies of responses to natural stimuli commonly use methods that are unbiased only for a linear model (in which there is no static nonlinearity): spike-triggered averages with correction for stimulus power spectrum, with or without regularization. Although these methods work well for artificial stimuli, such as Gaussian white noise, we show here that they estimate neural filters of LN models from responses to natural stimuli much more poorly. We studied simple cells in cat primary visual cortex. We demonstrate that the filters computed by directly taking the nonlinearity into account have better predictive power and depend less on the stimulus than those computed under the linear model. With noise stimuli, filters computed using the linear and LN models were similar, as predicted theoretically. With natural stimuli, filters of the two models can differ profoundly. Noise and natural stimulus filters differed significantly in spatial properties, but these differences were exaggerated when filters were computed using the linear rather than the LN model. Although regularization of filters computed under the linear model improved their predictive power, it also led to systematic distortions of their spatial frequency profiles, especially at low spatial and temporal frequencies. I N T R O D U C T I O NOver the course of nearly 50 years neurons in the primary visual cortex have been studied with a variety of stimuli, ranging from the classic studies using edges, bars, and moving gratings, to more recent studies with random inputs and stimuli derived from the natural environment, as reviewed by Felsen and Dan (2005) and . It is becoming increasingly important to develop unified models for neural responses to stimuli with a wide range of statistical properties, ideally extending to the fully natural case.To accomplish this, it is important to be able to derive a neural response model from responses to natural stimuli. One cannot simply derive a response model from responses to a simpler ensemble, such as noise, and then extrapolate to the natural case. Typically there are significant differences between response models derived for a given cell from responses to different stimulus ensembles. In particular, neural response models derived from one stimulus ensemble often provide better predictions for neural responses to novel stimuli of the same type than to novel stimuli with different statistical properties (David ...

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Section: Orientation Selectivitysupporting

confidence: 91%

On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli

Sharpee

Miller²,

Stryker³

2008

Journal of Neurophysiology

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“…Another observation is that there is good qualitative agreement between measured and predicted preferred orientation values. In general, previous investigations have noted a clear correlation between preferred orientation and that of the major axis of the spatial RF (Gardner et al 1999;Jones and Palmer 1987b;Usrey et al 2003). Peak response rates for kittens are weaker than those from adults, as expected (Braastad and Heggelund 1985).…”

Section: Resultsmentioning

confidence: 80%

“…Results of studies using extracellular spike rate measurements indicate that most simple cells exhibit sharper measured orientation tuning compared with what is predicted by linear spatial processes (Gardner et al 1999;Li et al 2003;Usrey et al 2003). However, a similar procedure conducted intracellularly found a strong agreement between orientation tuning predicted from spatial RFs and that measured using gratings (Lampl et al 2001).…”

mentioning

confidence: 89%

Development of orientation tuning in simple cells of primary visual cortex

Moore

Freeman

2012

Journal of Neurophysiology

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Moore BD 4th, Freeman RD. Development of orientation tuning in simple cells of primary visual cortex. J Neurophysiol 107: 2506-2516, 2012. First published February 8, 2012 doi:10.1152/jn.00719.2011.-Orientation selectivity and its development are basic features of visual cortex. The original model of orientation selectivity proposes that elongated simple cell receptive fields are constructed from convergent input of an array of lateral geniculate nucleus neurons. However, orientation selectivity of simple cells in the visual cortex is generally greater than the linear contributions based on projections from spatial receptive field profiles. This implies that additional selectivity may arise from intracortical mechanisms. The hierarchical processing idea implies mainly linear connections, whereas cortical contributions are generally considered to be nonlinear. We have explored development of orientation selectivity in visual cortex with a focus on linear and nonlinear factors in a population of anesthetized 4-wk postnatal kittens and adult cats. Linear contributions are estimated from receptive field maps by which orientation tuning curves are generated and bandwidth is quantified. Nonlinear components are estimated as the magnitude of the power function relationship between responses measured from drifting sinusoidal gratings and those predicted from the spatial receptive field. Measured bandwidths for kittens are slightly larger than those in adults, whereas predicted bandwidths are substantially broader. These results suggest that relatively strong nonlinearities in early postnatal stages are substantially involved in the development of orientation tuning in visual cortex. orientation selectivity; vision; nonlinearity THE TRANSFORMATION OF RECEPTIVE FIELDS (RFs) from centersurround organization in the early visual pathway to that of elongated edges in striate cortex is accompanied by the property of orientation selectivity of cortical neurons. Although orientation tuning is present at early postnatal stages, it narrows developmentally to a final adult level of selectivity (Bonds 1979; Freeman and Ohzawa 1992). Regarding the derivation of simple cell orientation selectivity, there is evidence supporting both feedforward and intracortical processing (see Ferster and Miller 2000 for a review). The classical model of orientation selectivity involves a hierarchical processing of information occurring from convergent arrays of feedforward lateral geniculate nucleus (LGN) input onto simple cells in area 17 of the visual cortex (Hubel and Wiesel 1962). Tests of this model have included direct recordings from LGN afferents (Chapman et al. 1991) and simultaneous recordings from pairs of monosynaptically connected LGN and simple cells (Reid and Alonso 1995;Tanaka 1983). In other tests, intracellular recordings of simple cells have been made while primary visual cortex was presumably inactivated. Reported findings indicate that specificity of orientation tuning can be accounted for entirely by thalamic input (Chung and F...

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“…Although this is true for other carnivores [59] , it is not a universal rule. For example, cells in layer 4 of the tree shrew cortex are not tuned to stimulus orientation [60] , but the orientation selectivity in layer 2/3 of the tree shrew appears to depend on the axial bias in its feed-forward input from layer 4, further refi ned by the intracortical circuits [61] .…”

Section: Comparisons With Other Speciesmentioning

confidence: 94%

Orientation selectivity in cat primary visual cortex: local and global measurement

Yan

Song

et al. 2015

Neurosci. Bull.

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In this study, we investigated orientation selectivity in cat primary visual cortex (V1) and its relationship with various parameters. We found a strong correlation between circular variance (CV) and orthogonal-topreferred response ratio (O/P ratio), and a moderate correlation between tuning width and O/P ratio. Moreover, the suppression far from the peak that accounted for the lower CV in cat V1 cells also contributed to the narrowing of the tuning width of cells. We also studied the dependence of orientation selectivity on the modulation ratio for each cell, which is consistent with robust entrainment of the neuronal response to the phase of the drifting grating stimulus. In conclusion, the CV (global measure) and tuning width (local measure) are signifi cantly correlated with the modulation ratio.

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Receptive Fields and Response Properties of Neurons in Layer 4 of Ferret Visual Cortex

Cited by 58 publications

References 78 publications

On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli

On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli

Development of orientation tuning in simple cells of primary visual cortex

Orientation selectivity in cat primary visual cortex: local and global measurement

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