A Self-Organizing Model of the Visual Development of Hand-Centred Representations

Galeazzi, Juan M.; Mender, Bedeho M. W.; Paredes, Mariana; Tromans, James Matthew; Evans, Benjamin D.; Minini, Loredana; Stringer, Simon M.

doi:10.1371/journal.pone.0066272

Cited by 5 publications

(27 citation statements)

References 60 publications

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“…Gain-modulated neurons in PPC have been found to generate depth-dependent activity (Blohm & Crawford, 2012) as well as hand-centered maps (Galeazzi et al, 2013) but because our setup does not have vergence and tactile information, we will not focus on these properties. Instead, we propose to use the gain-field neurons to estimate each incoming signal from the fusion of all, in their respective reference frame.…”

Section: Generalization From Visuomotor Associationmentioning

confidence: 99%

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

et al. 2015

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The so-called self-other correspondence problem in imitation demands to find the transformation that maps the motor dynamics of one partner to our own. This requires a general purpose sensorimotor mechanism that transforms an external fixation-point (partner's shoulder) reference frame to one's own body-centered reference frame. We propose that the mechanism of gain-modulation observed in parietal neurons may generally serve these types of transformations by binding the sensory signals across the modalities with radial basis functions (tensor products) on the one hand and by permitting the learning of contextual reference frames on the other hand. In a shoulder-elbow robotic experiment, gain-field neurons (GF) intertwine the visuo-motor variables so that their amplitude depends on them all. In situations of modification of the body-centered reference frame, the error detected in the visuo-motor mapping can serve then to learn the transformation between the robot's current sensorimotor space and the new one. These situations occur for instance when we turn the head on its axis (visual transformation), when we use a tool (body modification), or when we interact with a partner (embodied simulation). Our results defend the idea that the biologically-inspired mechanism of gain modulation found in parietal neurons can serve as a basic structure for achieving nonlinear mapping in spatial tasks as well as in cooperative and social functions.

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Section: Generalization From Visuomotor Associationmentioning

confidence: 99%

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

et al. 2015

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“…The same VisNet architecture was used in our previous simulations of the development of hand-centred visual neurons using trace learning [33–35]. However, in the simulations reported below, we implement a Hebbian learning rule in the model instead of the trace rule.…”

Section: Methodsmentioning

confidence: 99%

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

2017

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A subset of neurons in the posterior parietal and premotor areas of the primate brain respond to the locations of visual targets in a hand-centred frame of reference. Such hand-centred visual representations are thought to play an important role in visually-guided reaching to target locations in space. In this paper we show how a biologically plausible, Hebbian learning mechanism may account for the development of localized hand-centred representations in a hierarchical neural network model of the primate visual system, VisNet. The hand-centered neurons developed in the model use an invariance learning mechanism known as continuous transformation (CT) learning. In contrast to previous theoretical proposals for the development of hand-centered visual representations, CT learning does not need a memory trace of recent neuronal activity to be incorporated in the synaptic learning rule. Instead, CT learning relies solely on a Hebbian learning rule, which is able to exploit the spatial overlap that naturally occurs between successive images of a hand-object configuration as it is shifted across different retinal locations due to saccades. Our simulations show how individual neurons in the network model can learn to respond selectively to target objects in particular locations with respect to the hand, irrespective of where the hand-object configuration occurs on the retina. The response properties of these hand-centred neurons further generalise to localised receptive fields in the hand-centred space when tested on novel hand-object configurations that have not been explored during training. Indeed, even when the network is trained with target objects presented across a near continuum of locations around the hand during training, the model continues to develop hand-centred neurons with localised receptive fields in hand-centred space. With the help of principal component analysis, we provide the first theoretical framework that explains the behavior of Hebbian learning in VisNet.

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“…The synaptic connections between the successive layers of neurons are updated using associative learning. Although VisNet has been often used to model invariance in the ventral visual stream, it has been subsequently applied to simulate visual processes occurring in the dorsal stream (Rolls and Stringer, 2007 ; Galeazzi et al, 2013 ; Rolls and Webb, 2014 ). Both ventral and dorsal streams share architectural similarities, each consisting of a hierarchical series of neuronal layers with competition mediated by inhibitory interneurons within each layer (Rolls and Webb, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

“…A self-organizing hypothesis to account for how hand-centered representations could occur has been recently proposed (Galeazzi et al, 2013 ). Here, it was suggested that while the eyes are exploring a visual scene involving a target object in a fixed position with respect to the hand, a form of trace learning would allow the network to associate different views of the same hand-object spatial configuration.…”

Section: Introductionmentioning

confidence: 99%

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The Development of Hand-Centered Visual Representations in the Primate Brain: A Computer Modeling Study Using Natural Visual Scenes

Galeazzi

Minini

Stringer

2015

Front. Comput. Neurosci.

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Neurons that respond to visual targets in a hand-centered frame of reference have been found within various areas of the primate brain. We investigate how hand-centered visual representations may develop in a neural network model of the primate visual system called VisNet, when the model is trained on images of the hand seen against natural visual scenes. The simulations show how such neurons may develop through a biologically plausible process of unsupervised competitive learning and self-organization. In an advance on our previous work, the visual scenes consisted of multiple targets presented simultaneously with respect to the hand. Three experiments are presented. First, VisNet was trained with computerized images consisting of a realistic image of a hand and a variety of natural objects, presented in different textured backgrounds during training. The network was then tested with just one textured object near the hand in order to verify if the output cells were capable of building hand-centered representations with a single localized receptive field. We explain the underlying principles of the statistical decoupling that allows the output cells of the network to develop single localized receptive fields even when the network is trained with multiple objects. In a second simulation we examined how some of the cells with hand-centered receptive fields decreased their shape selectivity and started responding to a localized region of hand-centered space as the number of objects presented in overlapping locations during training increases. Lastly, we explored the same learning principles training the network with natural visual scenes collected by volunteers. These results provide an important step in showing how single, localized, hand-centered receptive fields could emerge under more ecologically realistic visual training conditions.

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A Self-Organizing Model of the Visual Development of Hand-Centred Representations

Cited by 5 publications

References 60 publications

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

Hebbian learning of hand-centred representations in a hierarchical neural network model of the primate visual system

The Development of Hand-Centered Visual Representations in the Primate Brain: A Computer Modeling Study Using Natural Visual Scenes

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