Intrinsically motivated learning of visual motion perception and smooth pursuit

Zhang, Chong; Zhao, Yu; Triesch, Jochen; Shi, Bertram E.

doi:10.1109/icra.2014.6907110

Cited by 20 publications

(19 citation statements)

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“…This framework models the joint emergence of both perception and action, and accounts for the importance of the development of normal vergence control and binocular vision in achieving a symmetric mOKN. The framework is a based on the active efficient coding model (Zhao et al, 2012;Zhang et al, 2014;Teuliè re et al, 2015), which posits that For example, the model matches with Hubel and Wiesel measurements of ocular dominance histograms of normal kittens and kittens with artificially introduced squint (Hubel & Wiesel, 1965).…”

Section: Discussionmentioning

confidence: 99%

“…In our simulations, we considered only horizontal eye movements, since most studies in the literature focus on horizontal, rather than vertical OKN (Knapp, Proudlock, & Gottlob, 2013). However, the model could be extended to handle vertical eye movements in a straightforward manner (Vikram et al, 2014;Zhang et al, 2014).…”

Section: Developmental Model Of the Optokinetic Reflexmentioning

confidence: 99%

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An active-efficient-coding model of optokinetic nystagmus

2016

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Optokinetic nystagmus (OKN) is an involuntary eye movement responsible for stabilizing retinal images in the presence of relative motion between an observer and the environment. Fully understanding the development of OKN requires a neurally plausible computational model that accounts for the neural development and the behavior. To date, work in this area has been limited. We propose a neurally plausible framework for the joint development of disparity and motion tuning in the visual cortex and of optokinetic and vergence eye-movement behavior. To our knowledge, this framework is the first developmental model to describe the emergence of OKN in a behaving organism. Unlike past models, which were based on scalar models of overall activity in different neural areas, our framework models the development of the detailed connectivity both from the retinal input to the visual cortex and from the visual cortex to the motor neurons. This framework accounts for the importance of the development of normal vergence control and binocular vision in achieving normal monocular OKN behaviors. Because the model includes behavior, we can simulate the same perturbations as past experiments, such as artificially induced strabismus. The proposed model agrees both qualitatively and quantitatively with a number of findings from the literature on both binocular vision and the optokinetic reflex. Finally, our model makes quantitative predictions about OKN behavior using the same methods used to characterize OKN in the experimental literature.

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Section: Discussionmentioning

confidence: 99%

Section: Developmental Model Of the Optokinetic Reflexmentioning

confidence: 99%

An active-efficient-coding model of optokinetic nystagmus

2016

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“…In neuroscience, several studies have highlighted the fact that eye movement patterns are not the same when trying to learn a skill, and once this skill is acquired [54]. In developmental robotics, visual attention is also considered as a common study case, most of the time used as a way to develop proprioceptive skills (for example, predict the position of the hand in a field of view [1]) or visual servoing skills (learning options from visual inputs [32], smooth pursuit [60], simultaneous gaze control and reaching [28]). However, learning the visual aspect of salient elements has not been examined so far in a developmental robotics framework, although clear evidences show that such saliency is, at least partially, learned [24], [6].…”

Section: B Exploration Strategies For Learningmentioning

confidence: 99%

BioVision: A Biomimetics Platform for Intrinsically Motivated Visual Saliency Learning

2019

IEEE Trans. Cogn. Dev. Syst.

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Abstract-We present BioVision, a bio-mimetics platform based on the human visual system. BioVision relies on the foveal vision principle based on a set of cameras with wide and narrow fields of view. We present in this platform a mechanism for learning visual saliency in an intrinsically motivated fashion. This model of saliency, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment.The proposed approach includes two intertwined components. On the one hand, a method for learning and incrementally updating a model of visual saliency from foveal observations. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, based on the IAC (Intelligent Adaptive Curiosity) algorithm is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency.We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that IAC can drastically decrease the required time for learning a reliable saliency model. We also investigate the behavior of IAC in a non static environment, and how well this algorithm can adapt to changes.

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“…In these elegant studies investigating efficient coding, dual cameras learn to proficiently track targets together (Zhang et al . ) and recalibrate after physical misalignment (Lonini et al . ).…”

Section: Introductionmentioning

confidence: 99%

Unlocking neural complexity with a robotic key

Stratton

Hasselmo

Milford

2016

The Journal of Physiology

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Key Points: Implementing neural systems on robots has successfully elucidated many principles of neural sensorimotor processing in animals. Despite some understanding of how the brain processes perceptual input and generates direct motor output, we only poorly understand what happens in betweenthe neural processes that turn sensory input into coherent behavioural output through time. Brains exhibit complex emergent properties that cannot be understood by studying neural components in isolation. Complex brains have evolved for comprehending and interacting with complex environments in the real world. Studies utilising neural controllers on robots in the real world can exploit real-world complexity (without having to model it) while simultaneously offering an entirely observable, fully controllable experimental model. Abstract:Complex brains evolved in order to comprehend and interact with complex environments in the real world. Despite significant progress in our understanding of perceptual representations in the brain, our understanding of how the brain carries out higher level processing remains largely superficial. This disconnect is understandable, since the direct mapping of sensory inputs to perceptual states is readily observed, while mappings between (unknown) stages of processing and intermediate neural states is not. We argue that testing theories of higher level neural processing on robots in the real world offers a clear path forward, since: 1. The complexity of the neural robotic controllers can be staged as necessary, avoiding the almost This article is protected by copyright. All rights reserved. intractable complexity apparent in even the simplest current living nervous systems; 2.Robotic controller states are fully observable, avoiding the enormous technical challenge of recording from complete intact brains; and 3. Unlike computational modelling, the real world can stand for itself when using robots, avoiding the computational intractability of simulating the world at an arbitrary level of detail. We suggest that embracing the complex and often unpredictable closed-loop interactions between robotic neuro-controllers and the physical world will bring about deeper understanding of the role of complex brain function in the high-level processing of information and the control of behaviour.Abbreviations: CPG, central pattern generator; HD, head direction; AEC, active efficient encoding; STDP, spike timing-dependent plasticity.

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Intrinsically motivated learning of visual motion perception and smooth pursuit

Cited by 20 publications

References 24 publications

An active-efficient-coding model of optokinetic nystagmus

An active-efficient-coding model of optokinetic nystagmus

BioVision: A Biomimetics Platform for Intrinsically Motivated Visual Saliency Learning

Unlocking neural complexity with a robotic key

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