Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

Meffin, Hamish; Hietanen, Markus A.; Cloherty, Shaun L.; Ibbotson, M.

doi:10.1152/jn.00431.2015

Cited by 14 publications

(16 citation statements)

References 36 publications

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“…Previous work has demonstrated that the classification of simple cells does not vary with contrast in the cat and primate (cat: Crowder et al, 2007; van Kleef et al, 2010; monkey: Henry and Hawken, 2013; Cloherty and Ibbotson, 2015; Meffin et al, 2015). We first examined whether this is also true in mouse V1 by measuring the changes in the modulation ratios for spiking rate (F 1 /F 0 ) and membrane potential (V 1 /V 0 ) of individual simple cells with contrast (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…While this difference between cell classes is clear when the stimulus strength is high, reducing stimulus strength diminishes the differences between the cell types. In particular, low contrast gratings evoke modulated responses in many complex cells (cat: Crowder et al, 2007; van Kleef et al, 2010; monkey: Henry and Hawken, 2013; Cloherty and Ibbotson, 2015; Meffin et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

Yunzab

Choi²,

Meffin

et al. 2019

eNeuro

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A central transformation that occurs within mammalian visual cortex is the change from linear, polarity-sensitive responses to nonlinear, polarity-insensitive responses. These neurons are classically labelled as either simple or complex, respectively, on the basis of their response linearity ( Skottun et al., 1991 ). While the difference between cell classes is clear when the stimulus strength is high, reducing stimulus strength diminishes the differences between the cell types and causes some complex cells to respond as simple cells ( Crowder et al., 2007 ; van Kleef et al., 2010 ; Hietanen et al., 2013 ). To understand the synaptic basis for this shift in behavior, we used in vivo whole-cell recordings while systematically shifting stimulus contrast. We find systematic shifts in the degree of complex cell responses in mouse primary visual cortex (V1) at the subthreshold level, demonstrating that synaptic inputs change in concert with the shifts in response linearity and that the change in response linearity is not simply due to the threshold nonlinearity. These shifts are consistent with a visual cortex model in which the recurrent amplification acts as a critical component in the generation of complex cell responses ( Chance et al., 1999 ).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

Yunzab

Choi²,

Meffin

et al. 2019

eNeuro

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“…The classifications based on these two methods often agree with one another (Dean and Tolhurst 1983;De Valois et al 1982a;Henry and Hawken 2013;Mata and Ringach 2005;Sengpiel et al 1997), but the agreement is not always complete (see Chen et al 2009;Skottun et al 1991 for reviews). Also, there are always some neurons whose behaviors fall between the "pure" types defined by either method (Crowder et al 2007;Hietanen et al 2013;Kagan et al 2002;Mata and Ringach 2005;Meffin et al 2015;Van Kleef et al 2010). Note that some recent physiological studies (Chance et al 1999Martinez and Alonso 2003;Priebe et al 2004; see also Mechler and Ringach 2002 for a review) have suggested that the simple and complex cells are better conceptualized as end points of a continuum rather than as a categorical distinction.…”

Section: Discussionmentioning

confidence: 99%

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

Sawada

Petrov

2017

Journal of Neurophysiology

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The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

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“…Most simple cells respond strongly to 8 oriented edges or gratings with a preference for a particular orientation (grating stimuli 9 consist of spatially periodic light and dark bands at a given orientation). 10 In contrast, complex cells exhibit significant nonlinear spatial integration and, while 11 they respond strongly to moving oriented edges, they do not show the other 12 characteristics of simple cells described above. One important property of complex cells 13 is their spatial phase invariance; i.e., strong responses are evoked by oriented gratings 14 with the preferred orientation, but for a wide range of spatial phases.…”

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confidence: 90%

Learning receptive field properties of complex cells in V1

Lian

Almasi

Grayden

et al. 2020

Preprint

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There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally. Author summaryMany cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of May 12, 2020 1/26 learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information. 30that is usually double-sided (e.g., squaring nonlinearity). Following this, th...

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Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

Cited by 14 publications

References 36 publications

Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

Synaptic Basis for Contrast-Dependent Shifts in Functional Identity in Mouse V1

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions

Learning receptive field properties of complex cells in V1

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