A neural model of how the brain computes heading from optic flow in realistic scenes

Browning, N. Andrew; Grossberg, Stephen; Mingolla, Ennio

doi:10.1016/j.cogpsych.2009.07.002

Cited by 42 publications

(43 citation statements)

References 114 publications

(223 reference statements)

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“…ViSTARS has previously been shown capable of humanlike accuracy in heading detection tasks in realistic scenes (Browning et al, 2007a(Browning et al, , 2007b(Browning et al, , 2007c(Browning et al, , 2008b. The present article also demonstrates how the MT -/MSTv stream can represent object position in response to realistic scenes at accuracies that enable the STARS steering circuitry to necessary to reactively avoid obstacles and approach goals.…”

Section: Resultsmentioning

confidence: 57%

“…Heading based models and navigation models are discussed in Browning, Grossberg and Mingolla (2008b) and Elder et al (2007). Here we concentrate on object motion estimation models.…”

Section: Discussionmentioning

confidence: 99%

“…The multi-scale input transformation and model levels 1 to 6 were used to compute heading in Browning, Grossberg & Mingolla (2008b). Our treatment closely follows their description and we use identical parameters.…”

Section: Appendix a Model Equations Parameters And Implementationmentioning

confidence: 99%

“…In our simulations, F 1 = 10.225, and σ 1 = 1. This value of F 1 was chosen by Browning, Grossberg & Mingolla (2008b) In equation (1.3), parameter G 1 defines the value at which the output signal attains onehalf of its maximum value, and term φ 1 is the firing threshold. In our simulations G 1 2 = 0.001, and φ 1 = 0.1.…”

Section: Level 1: On-center Off-surround Network (γ)mentioning

confidence: 99%

“…Rather, it used the equations that describe scenic geometry of Longuet-Higgins & Prazdny (1980) as model inputs. Browning, Grossberg, and Mingolla (2008b) showed how ViSTARS could build upon STARS to directly process visual data, notably virtual world animations and driving video sequences of realistic visual scenes, as well as random dot displays, to compute accurate heading, or direction of travel, estimates at human-like accuracies. To accomplish this, the motion processing front end of ViSTARS adapted a biological motion perception model, called the 3D FORMOTION model, that has been progressively developed to explain and predict large perceptual and neurobiological data bases about motion perception (Baloch & Grossberg, 1997;Chey, Grossberg, & Mingolla, 1997;Berzhanskaya, Grossberg, & Mingolla, 2007;Grossberg, Mingolla, & Viswanathan, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

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Section: Resultsmentioning

confidence: 57%

“…Heading based models and navigation models are discussed in Browning, Grossberg and Mingolla (2008b) and Elder et al (2007). Here we concentrate on object motion estimation models.…”

Section: Discussionmentioning

confidence: 99%

Section: Appendix a Model Equations Parameters And Implementationmentioning

confidence: 99%

Section: Level 1: On-center Off-surround Network (γ)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Resonance Theory

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Grossberg²

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Adaptive Resonance Theory 2 Adaptive Resonance Theory DefinitionAdaptive Resonance Theory, or ART, is both a cognitive and neural theory of how the brain quickly learns to categorize, recognize, and predict objects and events in a changing world, and a set of algorithms which computationally embody ART principles and are used in large-scale engineering and technological applications where fast, stable, incremental, learning about complex changing environments is needed. ART clarifies the brain processes from which conscious experiences emerge. It predicts a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance, and Synchrony (CLEARS), including the prediction that "all conscious states are resonant states." This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. ART predicts how top-down attention works and regulates fast stable learning of recognition categories. In particular, ART articulates a critical role for "resonant" states in driving fast stable learning; hence the name adaptive resonance. These resonant states are bound together, using top-down attentive feedback in the form of learned expectations, into coherent representations of the world. ART hereby clarifies one important sense in which the brain carries out predictive computation. ART has explained and successfully predicted a wide range of behavioral and neurobiological data, including data about human cognition and the dynamics of spiking laminar cortical networks. ART algorithms have been used in large-scale applications such as medical data base prediction, remote sensing, airplane design, and the control of autonomous adaptive robots. Motivation and Background

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A neural model of how the brain computes heading from optic flow in realistic scenes

Cited by 42 publications

References 114 publications

Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

Visual Navigation in a Cluttered World

Adaptive Resonance Theory

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