Abstract

this paper, we present a new, Informational Lesiontrig Method (ILM) which views a lesion as a noisy channel and applies controlled lesion to the network by varying the lesioning level from large to arbitrarily small magnitudes. Studying the ILM within the FCA framework, our main results are threefold: First, that lower lesioning levels permit more accurate FCA predictions. Second, that the usage of minute ILM lesioning levels can uncover the long-term effects of elements on the network's functioning. And third, that as the lesioning level decreases, the contribution values tend to approach limit values, reflecting the importance of these elements in the intact, normal functioning neurocontroller

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