The Suppressive Field of Neurons in Lateral Geniculate Nucleus

By Vincent Bonin, Valerio Mante, Matteo Carandini. 2005

257 citations recorded by the source corpus.

The responses of neurons in lateral geniculate nucleus (LGN) exhibit powerful suppressive phenomena such as contrast saturation, size tuning, and masking. These phenomena cannot be explained by the classical center-surround receptive field and have been ascribed to a variety of mechanisms, including feedback from cortex. We asked whether these phenomena might all be explained by a single mechanism, contrast gain control, which is inherited from retina and possibly strengthened in thalamus. We formalized an intuitive model of retinal contrast gain control that explicitly predicts gain as a function of local contrast. In the model, the output of the receptive field is divided by the output of a suppressive field, which computes the local root-mean-square contrast. The model provides good fits to LGN responses to a variety of stimuli; with a single set of parameters, it captures saturation, size tuning, and masking. It also correctly predicts that responses to small stimuli grow proportionally with contrast: were it not for the suppressive field, LGN responses would be linear. We characterized the suppressive field and found that it is similar in size to the surround of the classical receptive field (which is eight times larger than commonly estimated), it is not selective for stimulus orientation, and it responds to a wide range of frequencies, including very low spatial frequencies and high temporal frequencies. The latter property is hardly consistent with feedback from cortex. These measurements thoroughly describe the visual properties of contrast gain control in LGN and provide a parsimonious explanation for disparate suppressive phenomena.

Subfields: Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Building a mechanistic model of the development and function of the primary visual cortex. (2012, unknown): “The divisive inhibition implements the contrast gain control mechanisms found in RGC and LGN neurons [3, 4, 20, 32].”
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. (2015, methodology): “Surround suppression is widely observed in the visual cortex: in LGN (Bonin et al., 2005); V1 (Bair et al., 2003; Cavanaugh et al., 2002a, Cavanaugh, Bair, & Movshon, 2002b); V2 (Ito & Gilbert, 1999; Willmore, Prenger, & Gallant, 2010); V4 (Sundberg, Mitchell, & Reynolds, 2009); MT (Allman, Miezin,…”
Feed-Forward Segmentation of Figure-Ground and Assignment of Border-Ownership (2010, background): “Finally, inhibition from the surround has been shown to be orientation or direction selective [34–38].”
Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex (2013, methodology): “Contrast-gain control, also known as normalization, is a welldocumented phenomenon whereby neurons have a nonlinear response to input strength, compressing a wide range of inputs into a smaller range of responses (Bonin et al., 2005).”
Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback (2014, background): “, 1998; Stemmler, Usher, & Niebur, 1995) among which several mechanisms have stressed the importance of divisive inhibition to explain observed response nonlinearities and normalization properties (Bonin et al., 2005; Carandini & Heeger, 1994; Carandini et al., 1997).”
Cellular Basis for Contrast Gain Control over the Receptive Field Center of Mammalian Retinal Ganglion Cells (2007, result): “This hypothesis was supported by previous studies showing that adaptive signals can arise from peripheral regions of the receptive field, over several millimeters on the retina, well beyond the receptive field center (Shapley and Victor, 1979, 1981; Enroth-Cugell and Jakiela, 1980; Bonin et al., 200”
Complex dynamics of V1 population responses explained by a simple gain-control model (2009, unknown): “Potential advantages of implementing a large component of the contrast gain-control before V1 is that it could then help preserve tuning in the retina and LGN (as well as in cortex), and it could be implemented with a spatial pooling that might involve relatively fewer connections than required in V”
Do We Know What the Early Visual System Does? (2005, background): “These insights on gain control can be used to build a nonlinear model of LGN responses that is general enough to predict the responses to arbitrary stimuli (Bonin et al., 2005; Mante, 2005).”
The Statistical Computation Underlying Contrast Gain Control (2006, unknown): “As expected from studies in retina (Chander and Chichilnisky, 2001; Zaghloul et al., 2005) and in LGN (Bonin et al., 2005), these changes in gain were more pronounced in ON cells than in OFF cells.”
Contrast Gain Control in Auditory Cortex (2011, unknown): “This mechanism is well described by ‘‘divisive normalization,’’ whereby the range of visual input is adjusted according to the contrast of recent visual stimulation (Heeger, 1992; Carandini et al., 1997; Schwartz and Simoncelli, 2001; Bonin et al., 2005).”
Temporal properties of surround suppression in cat primary visual cortex (2007, background): “Indeed, LGN cells do exhibit a form of surround suppression ~Walker et al., 1999; Jones et al., 2000; Solomon et al., 2002; Ozeki et al., 2004; Bonin et al., 2005!.”
Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, methodology): “…or contrast diminish both the amplitude of the RF, the neuron's gain, and the duration of its temporal profile, the neuron's integration time (Bonin et al., 2005; Demb, 2002; Kaplan and Benardete, 2001; Lesica et al., 2007; Mante et al., 2005; Meister and Berry, 1999; Shapley and…”