A neural network model of multisensory representation of peripersonal space: effect of tool use.

Mauro Ursino; Melissa Zavaglia; Elisa Magosso; Andrea Serino; Di Pellegrino Giuseppe

A neural network model of multisensory representation of peripersonal space: effect of tool use.

Standard

A neural network model of multisensory representation of peripersonal space: effect of tool use. / Ursino, Mauro; Zavaglia, Melissa; Magosso, Elisa; Serino, Andrea; Giuseppe, Di Pellegrino.

In: Conf Proc IEEE Eng Med Biol Soc, Vol. 2007, 2007, p. 2735-2739.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Ursino, M, Zavaglia, M, Magosso, E, Serino, A & Giuseppe, DP 2007, 'A neural network model of multisensory representation of peripersonal space: effect of tool use.', Conf Proc IEEE Eng Med Biol Soc, vol. 2007, pp. 2735-2739. <http://www.ncbi.nlm.nih.gov/pubmed/18002560?dopt=Citation>

APA

Ursino, M., Zavaglia, M., Magosso, E., Serino, A., & Giuseppe, D. P. (2007). A neural network model of multisensory representation of peripersonal space: effect of tool use. Conf Proc IEEE Eng Med Biol Soc, 2007, 2735-2739. http://www.ncbi.nlm.nih.gov/pubmed/18002560?dopt=Citation

Vancouver

Ursino M, Zavaglia M, Magosso E, Serino A, Giuseppe DP. A neural network model of multisensory representation of peripersonal space: effect of tool use. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:2735-2739.

Bibtex

@article{e61c9c6e7aff4bc385ce0f36852c678f,

title = "A neural network model of multisensory representation of peripersonal space: effect of tool use.",

abstract = "This work describes an original neural network to simulate representation of the peripersonal space around one hand, in basal conditions and after training with a tool used to reach the far space. The model is composed of two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile). Neurons in the bimodal area integrate visual and tactile information and are activated only when a stimulus falls inside the peripersonal space. Moreover, the model assumes that synapses linking unimodal to bimodal neurons can be reinforced by an Hebbian rule during training, but this reinforcement is also under the influence of attention mechanisms. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training. This expansion of the peripersonal space depends on an expansion of the visual receptive field of bimodal neurons, due to a reinforcement of visual synapses, which were just latent before training. The model may be of value to analyze the neural mechanisms responsible for representing and plastically shaping peripersonal space, and in perspective, for interpretation of psychophysical data on patients with brain damage.",

keywords = "Animals, Humans, Visual Perception/*physiology, Neurons, Afferent/*physiology, *Neural Networks (Computer), Synapses/physiology, Hand/physiology, Haplorhini, *Personal Space, Physical Stimulation, Tool Use Behavior/physiology, Touch/*physiology, Animals, Humans, Visual Perception/*physiology, Neurons, Afferent/*physiology, *Neural Networks (Computer), Synapses/physiology, Hand/physiology, Haplorhini, *Personal Space, Physical Stimulation, Tool Use Behavior/physiology, Touch/*physiology",

author = "Mauro Ursino and Melissa Zavaglia and Elisa Magosso and Andrea Serino and Giuseppe, {Di Pellegrino}",

year = "2007",

language = "English",

volume = "2007",

pages = "2735--2739",

}

RIS

TY - JOUR

T1 - A neural network model of multisensory representation of peripersonal space: effect of tool use.

AU - Ursino, Mauro

AU - Zavaglia, Melissa

AU - Magosso, Elisa

AU - Serino, Andrea

AU - Giuseppe, Di Pellegrino

PY - 2007

Y1 - 2007

N2 - This work describes an original neural network to simulate representation of the peripersonal space around one hand, in basal conditions and after training with a tool used to reach the far space. The model is composed of two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile). Neurons in the bimodal area integrate visual and tactile information and are activated only when a stimulus falls inside the peripersonal space. Moreover, the model assumes that synapses linking unimodal to bimodal neurons can be reinforced by an Hebbian rule during training, but this reinforcement is also under the influence of attention mechanisms. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training. This expansion of the peripersonal space depends on an expansion of the visual receptive field of bimodal neurons, due to a reinforcement of visual synapses, which were just latent before training. The model may be of value to analyze the neural mechanisms responsible for representing and plastically shaping peripersonal space, and in perspective, for interpretation of psychophysical data on patients with brain damage.

AB - This work describes an original neural network to simulate representation of the peripersonal space around one hand, in basal conditions and after training with a tool used to reach the far space. The model is composed of two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile). Neurons in the bimodal area integrate visual and tactile information and are activated only when a stimulus falls inside the peripersonal space. Moreover, the model assumes that synapses linking unimodal to bimodal neurons can be reinforced by an Hebbian rule during training, but this reinforcement is also under the influence of attention mechanisms. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training. This expansion of the peripersonal space depends on an expansion of the visual receptive field of bimodal neurons, due to a reinforcement of visual synapses, which were just latent before training. The model may be of value to analyze the neural mechanisms responsible for representing and plastically shaping peripersonal space, and in perspective, for interpretation of psychophysical data on patients with brain damage.

KW - Animals

KW - Humans

KW - Visual Perception/physiology

KW - Neurons, Afferent/physiology

KW - Neural Networks (Computer)

KW - Synapses/physiology

KW - Hand/physiology

KW - Haplorhini

KW - Personal Space

KW - Physical Stimulation

KW - Tool Use Behavior/physiology

KW - Touch/physiology