We investigate the spatio-temporal dynamics of hand preshaping during prehension through a biologically plausible neural model. The hand grip formation is generated through neural modulation of basic motor programs th...
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We investigate the spatio-temporal dynamics of hand preshaping during prehension through a biologically plausible neural model. The hand grip formation is generated through neural modulation of basic motor programs that can be rescaled to accommodate different task demands. The model assumes a timing role to propioceptive reafferent information generated by the reaching component of the movement, avoiding the need of a preorganized functional temporal structure for the timing of prehension as some previous models have proposed. Predictions of the model in both Normal and Altered initial hand aperture conditions match key kinematic features present in human data. (C) 2007 Published by Elsevier B.V.
In computational experiments with a simplified cortical array we investigated the factors that give rise to the functional organization of the cerebral cortex during brain development. We show that a dynamical spatial...
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In computational experiments with a simplified cortical array we investigated the factors that give rise to the functional organization of the cerebral cortex during brain development. We show that a dynamical spatial modulation of plasticity in the substrate (i.e., a ''wave of plasticity'') induces higher functional development in the later-developing parts of the cortical array. This result suggests an account of the role that changes in developmental timing may have in the development of different cortical structures. Copyright (C) 1996 Elsevier Science Ltd
In this paper we propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects. The main idea is to measure the interests of a spectator w.r.t. an...
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ISBN:
(数字)9783319259369
ISBN:
(纸本)9783319259369;9783319259352
In this paper we propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects. The main idea is to measure the interests of a spectator w.r.t. an artwork by means of a model able to describe the behaviour dynamics. In this approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. The key idea of this paper consists in comparing a strengthened validation approach for neural networks based on classification with our novel proposal based on clustering;indeed, clustering allows to discover natural groups in the data, which are used to verify the neuronal response and to tune the computational model. Preliminary experimental results, based on a phantom database and obtained from a real world scenario, are shown. They underline the accuracy improvements achieved by the clustering-based approach in supporting the tuning of the model parameters.
The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits...
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The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.
To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer...
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ISBN:
(纸本)9781617823800
To localise the source of a sound, we use location-specific properties of the signals received at the two ears caused by the asymmetric filtering of the original sound by our head and pinnae, the head-related transfer functions (HRTFs). These HRTFs change throughout an organism's lifetime, during development for example, and so the required neural circuitry cannot be entirely hardwired. Since HRTFs are not directly accessible from perceptual experience, they can only be inferred from filtered sounds. We present a spiking neural network model of sound localisation based on extracting location-specific synchrony patterns, and a simple supervised algorithm to learn the mapping between synchrony patterns and locations from a set of example sounds, with no previous knowledge of HRTFs. After learning, our model was able to accurately localise new sounds in both azimuth and elevation, including the difficult task of distinguishing sounds coming from the front and back.
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