Robustness in Neural Circuits

Abstract

Complex systems are found everywherefrom scheduling to traffic, food to climate, economics to ecology, the brain, and the universe. Complex systems typically have many elements, many modes of interconnectedness of those elements, and often exhibit sensitivity to initial conditions. Complex systems by their nature are generally unpredictable and can be highly unstable. However, most highly connected complex systems are actually quite stable and resistant to disruption from minor changes in parameters [1]. This is a concept originating from Bernard and Cannon (homeostasis) and now is a central hypothesis of "robustness" in theoretical biology [2-5]. A more contemporary review by Demongeot and Demetrius [6] captures the relationship between the concepts of robustness, entropy, and complexity: The hypothesis that a positive correlation exists between the complexity of a biological system, as described by its connectance, and its stability, as measured by its ability to recover from disturbance, derives from the investigations of the physiologists, Bernard and Cannon, and the ecologist Elton. Studies based on the ergodic theory of dynamical systems and the theory of large deviations have furnished an analytic support for this hypothesis. Complexity in this context is described by the mathematical object evolutionary entropy, stability is characterized by the rate at which the system returns to its stable conditions (steady state or periodic attractor) after a random perturbation of its robustness. This article reviews the Kristen W. Carlson: Member, IEEE

External validation based on transfer learning for diagnosing atelectasis using portable chest X-rays

“…The ResNet50 network model parameters were obtained through the migration study less, high precision, deep residual layer network structure is complex, which solves the problem of low efficiency based on large training data sets and makes training more precise. Calculating the specificity, sensitivity, accuracy, and AUC revealed that the training model was highly robust (27), which provided external verification, with values of 100.00, 70.70, 86.90, and 98.39%, respectively. Finally, the features extracted from the training images were visualized by a heat map displaying the lung and the region near the heart.…”