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Michael Arbib

Publications and source records attributed to Michael Arbib.

3 recordsLinked to original sources

Extending the mirror neuron system model, I. Audible actions and invisible grasps.

The paper introduces mirror neuron system II (MNS2), a new version of the MNS model (Oztop and Arbib in Biol Cybern 87 (2):116-140, 2002) of action recognition learning by mirror neurons of the macaque brain. The new model uses a recurrent architecture that is biologically more plausible than that of the original model. Moreover, MNS2 extends the capacity of the model to address data on audio-visual mirror neurons and on the response of mirror neurons when the target object was recently visible but is currently hidden.

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Mirror neurons and imitation: a computationally guided review.

Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of 'mirror systems' (not individual neurons) in the human. Current conceptual models attribute high level functions such as action understanding, imitation, and language to mirror neurons. However, only the first of these three functions is well-developed in monkeys. We thus distinguish current opinions (conceptual models) on mirror neuron function from more detailed computational models. We assess the strengths and weaknesses of current computational models in addressing the data and speculations on mirror neurons (macaque) and mirror systems (human). In particular, our mirror neuron system (MNS), mental state inference (MSI) and modular selection and identification for control (MOSAIC) models are analyzed in more detail. Conceptual models often overlook the computational requirements for posited functions, while too many computational models adopt the erroneous hypothesis that mirror neurons are interchangeable with imitation ability. Our meta-analysis underlines the gap between conceptual and computational models and points out the research effort required from both sides to reduce this gap.

Animals↗

Language evolution: neural homologies and neuroinformatics.

This paper contributes to neurolinguistics by grounding an evolutionary account of the readiness of the human brain for language in the search for homologies between different cortical areas in macaque and human. We consider two hypotheses for this grounding, that of Aboitiz and Garci;a [Brain Res. Rev. 25 (1997) 381] and the Mirror System Hypothesis of Rizzolatti and Arbib [Trends Neurosci. 21 (1998) 188] and note the promise of computational modeling of neural circuitry of the macaque and its linkage to analysis of human brain imaging data. In addition to the functional differences between the two hypotheses, problems arise because they are grounded in different cortical maps of the macaque brain. In order to address these divergences, we have developed several neuroinformatics tools included in an on-line knowledge management system, the NeuroHomology Database, which is equipped with inference engines both to relate and translate information across equivalent cortical maps and to evaluate degrees of homology for brain regions of interest in different species.

Animals↗