LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis

Troyer, Todd W.; Krukowski, Anton E.; Miller, Kenneth D.

doi:10.1152/jn.2002.87.6.2741

Cited by 47 publications

(53 citation statements)

References 41 publications

(42 reference statements)

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“…The early broad excitation that could be caused by LGN input is expected on theoretical grounds to show a response at all orientations (McLaughlin et al 2000;Troyer et al 1998Troyer et al , 2002Wielaard et al 2001). Our experimental data are consistent with this theoretical prediction as evident in the presence of early enhancement at all orientations.…”

Section: Model-based Interpretation Of Orientation Dynamicssupporting

confidence: 89%

Dynamics of Orientation Tuning in Macaque V1: The Role of Global and Tuned Suppression

Ringach

Hawken²,

Shapley³

2003

Journal of Neurophysiology

137

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Ringach, Dario, Michael J. Hawken, and Robert Shapley. Dynamics of orientation tuning in macaque V1: the role of global and tuned suppression. J Neurophysiol 90: 342-352, 2003. First published February 26, 2003 10.1152/jn.01018.2002. The temporal development of neural selectivity to physical attributes of a visual stimulus, such as its orientation and spatial frequency, can provide important clues about mechanisms of cortical tuning. We measured the dynamics of orientation tuning in macaque primary visual cortex (V1) and found several dynamical features in the data: changes in global enhancement and suppression, narrowing of orientation bandwidth, small but significant shifts in preferred orientation, and "Mexican-hat" tuning curves. The dynamics data were analyzed with a model that sums two fixed, tuned components (enhancement and suppression) and one global (untuned) component. The analysis suggests that there is early global enhancement followed by global and tuned suppression. Tuned suppression accounts for the dynamical reduction of orientation bandwidth and for the generation of Mexican-hat tuning profiles. Our findings imply that global and tuned suppression are important factors that determine the selectivity and dynamics of V1 responses to orientation.The temporal development of neural selectivity to stimulus attributes can provide important clues about the underlying circuitry (Bredfeldt and Ringach 2002; Pei et al. 1994; Ringach et al. 1997b;Volgushev et al. 1995). Cortical excitation and inhibition onto a neuron are expected to be delayed with respect to the monosynaptic input from the lateral geniculate nucleus (LGN). Suppose one measures responses of a cortical neuron at different delay times with respect to stimulus onset. The early response would be dominated by excitation from the LGN input, whereas the late response would correspond to a combination of both LGN input and intracortical interactions. For orientation-tuning dynamics, some groups report dynamical changes in the shape of the tuning curves (Pei et al. 1994; Ringach et al. 1997b;Volgushev et al. 1995) while others observe a scaling of its magnitude and no significant changes in its shape (Celebrini et al. 1993;Gillespie et al. 2001; Mazer et al. 2002; Muller et al. 2001;Sharon and Grinvald 2002).In our earlier study (Ringach et al. 1997b) on the timing of the development of orientation tuning, we found that there were, in many cells, significant dynamical changes in shape of the orientation tuning curves. In particular, we suggested that the development of Mexican-hat tuning curves in the response could be accounted for by the presence of a tuned suppressive component centered on the preferred orientation of the cell. In more recent studies, we have concluded that in addition to a tuned suppressive component, there is a global suppressive component involved in the tuning for orientation and spatial frequency (Bredfeldt and Ringach 2002; Ringach et al. 2002a). However, up until now, we only reported results about these global compon...

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Section: Model-based Interpretation Of Orientation Dynamicssupporting

confidence: 89%

Dynamics of Orientation Tuning in Macaque V1: The Role of Global and Tuned Suppression

Ringach

Hawken²,

Shapley³

2003

Journal of Neurophysiology

137

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“…Physiological support for this m odel of image representation dates back to Hubel and Wiesel's work with feline striate cortex, which revealed both "simple" and "complex" cells capable of detecting edges and lines 7 . The receptive field properties of these cells are believed to arise through a combination of aligned inputs from the LGN and intra-cortical circuitry [8][9][10] . Computationally, the receptive fields of these cells are commonly modeled as Gabor functions with excitatory and inhibitory lobes [11][12][13][14][15][16] .…”

mentioning

confidence: 99%

Dissociated Dipoles: Image Representation via Non-local Comparisons

Balas¹,

Sinha²

2003

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“…A common choice of firing rate functions F r (x) for LGN neurons has been the half-wave rectification function (Gazères et al 1998;Troyer et al 1998Troyer et al , 2002Einevoll and Heggelund 2000), but sigmoidal functions have also been used (Hayot and Tranchina 2001;Yousif and Denham 2007). For now we leave F r ON (x), as well as the other firing-rate functions introduced below, unspecified.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Here we model OFF LGN relay cells as 'shadow' cells of the ON cells, i.e., they are assumed to receive the same time-dependent input with opposite phase, i.e., opposite sign (Troyer et al 2002). The firing rate of an OFF cell is thus in analogy with (3) given bŷ…”

Section: Mathematical Modelmentioning

confidence: 99%

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A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus

et al. 2012

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The receptive fields of cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. To probe the possible roles of these feedforward and feedback inputs in shaping the temporal receptive-field structure of LGN relay cells, we here present and investigate a minimal mechanistic firing-rate model tailored to elucidate their disparate features. The model for LGN relay ON cells includes feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. From a general firing-rate model formulated in terms of Volterra integral equations, we derive a single delay differential equation with absolute delay governing the dynamics of the system. A freely available and easy-to-use GUI-based MATLAB version of this minimal mechanistic LGN circuit model is provided. We particularly investigate the LGN relay-cell impulse response and find through thorough explorations of the model's parameter space that both purely feedforward models and feedback models with feedforward excitation only, can account quantitatively for previously reported experimental results. We find, however, that the purely feedforward model predicts two impulse response measures, the time to first peak and the biphasic index (measuring the relative weight of the rebound phase) to be anticorrelated. In contrast, the models with feedback predict different correlations between these two measures. This suggests an experimental test assessing the relative importance of feedforward and feedback connections in shaping the impulse response of LGN relay cells.

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LGN Input to Simple Cells and Contrast-Invariant Orientation Tuning: An Analysis

Cited by 47 publications

References 41 publications

Dynamics of Orientation Tuning in Macaque V1: The Role of Global and Tuned Suppression

Dynamics of Orientation Tuning in Macaque V1: The Role of Global and Tuned Suppression

Dissociated Dipoles: Image Representation via Non-local Comparisons

A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus

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