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Transcript
Lateral inhibition in neuronal interaction as a biological, computational and linguistic commodity CLAR-NET (Koutsomitopoulou 2004) is a model of neuronal activation patterns of language production and understanding, and within this framework we explore lateral inhibition (LI) as a biological, computational and linguistic commodity. The model utilizes Adaptive Resonance Theory equations (ART, Grossberg 1972 et seq.) and draws from natural language (NL) data mapped as nodes representing the basic argument structure of the input. Biologically motivated cognitive modeling is primarily concerned with the issue of the representation of serial NL input in dynamic parallel neural networks (NNs). For reasons of biological plausibility the neural models in this area of research often rely on unsupervised learning algorithms based on the Hebbian learning rule. For instance, the Kohonen self-organizing network (Kohonen 1982) utilizes unsupervised learning and it is useful especially for modeling data whose relationships are unknown. However, models like Kohonen’s often sacrifice biological faithfulness in favor of the engineering success of the endeavor. In Kohonen architectures the connections between a stimulated node and its closest neighbors are excitatory, whereas the connections to units farther away are considered inhibitory (Kohonen 1989, Miikkulainen 1991). This type of “long-distance" inhibitory links between neurons is not cerebral (Loritz 1999, 2002). The proposed CLAR-NET model shows how “minimal dipole anatomies” and LI help to systematically represent NL in a biologically faithful model. LI is a wellknown property of neuronal behavior. In prior LI models that have been proposed for NL (e.g. Rumelhart and McClelland 1981, Cottrell and Small 1983, Cottrell 1985, Small et al 1988 and Cottrell et al 1988), in spite of the presence of LI, there is no further provision for other crucial neurobiological features, such as sensitivity to the inhibition to excitation ratio (e.g. Rubenstein and Merzenich 2003, Mel and Schiller 2004), long-term vs. short-term memory activation processes (Koutsomitopoulou 2004), and the appreciation that in a model like CLAR-NET single neurons represent subnetworks (cortical columns) of “selfsimilar” neuronal anatomies which are the principal units of cerebral computation (Loritz 1999, 2002). With informative examples from homonymy, neologism and negation in NL we show how minimal dipole anatomies model NL within the CLAR-NET framework with biological faithfulness. The reference to LI is not wholly novel, but the particular analyses serve as a new way of modeling NL processing and quantifying fundamental notions like “context” and “homonymy” based on the neurobiological reality of LI. References Koutsomitopoulou E. (2004). A neural network model for the representation of natural language. PhD thesis, Georgetown University, Washington DC. Ann Arbor: UMI. 65:6, 3137058. Loritz D. (1999, paperback ed. 2002). How the Brain Evolved Language. Oxford University Press.