Dynamics of Pattern Motion Computation

Ma, Smith; Majaj, Najib J.; Movshon, J. Anthony

doi:10.1007/978-1-4419-0781-3_3

Cited by 9 publications

(12 citation statements)

References 57 publications

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“…More generally, this probabilistic and dynamical approach unveils how complex neural mechanisms observed at population levels (or from their read-outs) may be explained by the interactions between local dynamical rules. As mentioned above, both visual [Pack and Born, 2001, Pack et al, 2004, 2003, Smith et al, 2010 and somatosensory [Pei et al, 2010] systems exhibit similar neuronal dynamics when solving the aperture problem or other sensory integration tasks in space and time. This suggests that different sensory cortices might use similar computational principles for integrating sensory inflow into a coherent, nonambiguous representation of objects motion.…”

Section: Toward a Neural Implementationmentioning

confidence: 88%

Motion-Based Prediction Is Sufficient to Solve the Aperture Problem

Perrinet

Masson

2012

Neural Computation

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In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physiology and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independent of their texture. Second, we observe that incoherent features are explained away, while coherent information diffuses progressively to the global scale. Most previous models included ad hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights into the role of prediction underlying a large class of sensory computations.

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Section: Toward a Neural Implementationmentioning

confidence: 88%

Motion-Based Prediction Is Sufficient to Solve the Aperture Problem

Perrinet

Masson

2012

Neural Computation

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“…Human judgements of perceived speed have therefore generated much interest, and been studied with a range of psychophysics paradigms. The different results obtained in these studies suggest that rather than computing a veridical estimate, the visual system generates speed judgements influenced by contrast (Thompson 1982), speed range (Thompson, Brooks, and Hammett 2006), luminance (Hassan and Hammett 2015), spatial frequency (Brooks, Morris, and Thompson 2011;Simoncini et al 2012;Smith, Majaj, and Movshon 2010) and retinal eccentricity (Hassan, Thompson, and Hammett 2016). There are currently no theoretical models of the underlying mechanisms serving speed estimation which capture this dependence on such a broad range of image characteristics.…”

Section: Psychometric Resultsmentioning

confidence: 94%

Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures

et al. 2018

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A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The present study details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is first derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real time, on-the-fly texture synthesis using time-discretized auto-regressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log-likelihood of the probability density. The loglikelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicates previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a Gaussian likelihood centered at the true speed and a spatial frequency dependent width with a "slow speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width.

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“…However, the linearnonlinear model is not capable of explaining these features of pattern MT neurons. Due to the simulation of the propagation of activity, our model can replicate the temporal dynamics of component and pattern MT neurons observed by Smith et al (2005Smith et al ( , 2009, which shows a temporal delay in the pattern motion detection of the MT neurons. The linear-nonlinear model lacks these properties of the MT neurons (Simoncelli and Heeger, 1998;Rust et al, 2006).…”

Section: Discussionmentioning

confidence: 93%

“…The surround effect is excitatory for low levels of the contrast, below c r . Direction selective standard complex V1 neurons Hubel and Wiesel (1962), Dreher (1972), Movshon (1975), Movshon et al (1978a,b) End-stopped V1 neurons Hubel and Wiesel (1965), Sceniak et al (2001), Pack et al (2003), Tsui et al (2010) Orientation selective V1 neurons with suppressive surround (ECRF neurons) Cavanaugh et al (2002) Component and pattern selective MT neurons Adelson and Movshon (1982), Albright (1984), Rodman and Albright (1989), Livingstone et al (2001), Born and Bradley (2005) Difference in the temporal dynamics of the component and pattern MT neurons Smith et al (2005Smith et al ( , 2009 Projection of the complex V1 neurons to MT area Maunsell and van Essen (1983), Movshon and Newsome (1996) Projection of the end-stopped V1 neurons to MT area Movshon and Newsome (1996), Sceniak et al (2001) Projection of the ECRF neurons to MT area Hypothesized in the model Suppressive effect of the surround in V1 Hubel and Wiesel (1968), Cavanaugh et al (2002) Center-surround interaction of MT neurons Allman et al (1985), Raiguel et al (1995), Albright and Stoner (2002), Born and Bradley (2005) Adaptive modulatory effect of the surround Huang et al (2007Huang et al ( , 2008 Circuitry of the modulatory effect of the surround Hypothesized in the model Contrast dependency of the pattern selectivity of the MT neurons Kumbhani et al (2008) Contrast dependency of the suppressive effect of the surround in MT neurons Pack et al (2005) The activity of V1 neurons is gated by the activity of the ECRF neurons The activities of MT neurons with adaptive surrounds. The MT neurons selective to the rightward direction have high levels of activity in response to the motion o...…”

Section: Resultsmentioning

confidence: 99%

Adaptive Surround Modulation of MT Neurons: A Computational Model

Eskikand

Kameneva

Burkitt

et al. 2020

Front. Neural Circuits

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The classical receptive field (CRF) of a spiking visual neuron is defined as the region in the visual field that can generate spikes when stimulated by a visual stimulus. Many visual neurons also have an extra-classical receptive field (ECRF) that surrounds the CRF. The presence of a stimulus in the ECRF does not generate spikes but rather modulates the response to a stimulus in the neuron's CRF. Neurons in the primate Middle Temporal (MT) area, which is a motion specialist region, can have directionally antagonistic or facilitatory surrounds. The surround's effect switches between directionally antagonistic or facilitatory based on the characteristics of the stimulus, with antagonistic effects when there are directional discontinuities but facilitatory effects when there is directional coherence. Here, we present a computational model of neurons in area MT that replicates this observation and uses computational building blocks that correlate with observed cell types in the visual pathways to explain the mechanism of this modulatory effect. The model shows that the categorization of MT neurons based on the effect of their surround depends on the input stimulus rather than being a property of the neurons. Also, in agreement with neurophysiological findings, the ECRFs of the modeled MT neurons alter their center-surround interactions depending on image contrast.

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Dynamics of Pattern Motion Computation

Cited by 9 publications

References 57 publications

Motion-Based Prediction Is Sufficient to Solve the Aperture Problem

Motion-Based Prediction Is Sufficient to Solve the Aperture Problem

Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures

Adaptive Surround Modulation of MT Neurons: A Computational Model

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