Controlled analysis of neurocontrollers with informational lesioning

TitleControlled analysis of neurocontrollers with informational lesioning
Publication TypeJournal Article
Year of Publication2003
AuthorsKeinan A, Meilijson I, Ruppin E
JournalPhilos Trans A Math Phys Eng Sci
Date Publishedoct
KeywordsAdaptation, Biological Evolution, Cognition, Computer Simulation, Feedback, Information Storage and Retrieval, Models, Nerve Net, Neural Networks (Computer), Neural Pathways, Neurological, Neurons, Physiological

How does one aim to understand neural information processing? One of the difficult first challenges is to identify the roles of the network's elements. To this end a functional contribution analysis ({FCA)} method has been developed and applied for studying the neurocontrollers of evolutionary autonomous agents ({EAAs).} The {FCA} processes data composed of multiple lesion experiments and the corresponding performance levels that the agent obtains under these lesions. It calculates the contribution values ({CVs)} of the network's elements such that the ability to predict the agent's performance under new, unseen lesions is maximized. Previous analysis has found a strong dependence of the {CVs} and the prediction error on the specific type of lesioning method used, i.e. on the way in which the activity of lesioned neurons is disrupted. We present a new, informational lesioning method ({ILM)}, which views a lesion as a noisy channel and applies a 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 {CVs} tend to approach limit values, reflecting the importance of these elements in the intact, normal-functioning neurocontroller.