Cost minimization during simulated evolution of paired neural networks leads to asymmetries and specialization

Motivated by specialization (lateralization) that occurs in corresponding left and right regions of the cerebral cortex, several past computational models have studied conditions under which functional specialization can arise during learning due to underlying asymmetries in paired neural networks. However, these past studies have not addressed the basic issue of how such underlying asymmetries arise in the first place. As an initial step in addressing this issue, we investigated the hypothesis that underlying asymmetries will appear in paired neural networks during a simulated evolutionary process when fitness is based not only on maximizing performance, but also on minimizing various ‘costs’ such as energy consumption, neural connection weights, and response times. Simulated evolution under these conditions consistently produced networks with left–right asymmetries in region size, excitability and plasticity. These underlying asymmetries were often synergistic, leading to subsequent functional lateralization during network training. While our computational models are too simple for these results to be directly extrapolated to real nervous systems, they provide support for the hypothesis that brain asymmetries and lateralization in biological nervous systems may be a consequence of cost minimization present during evolution, and are the first computational demonstration of emergent population lateralization.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.