In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

We studied the influence of steady annular light on the kinetics and sensitivity of horizontal cell (HC) responses to modulation of the intensity of small concentric spots in the turtle retina. As shown by previous investigators, when the intensity of the annulus was equal to the mean spot intensity, spot response kinetics were the same as those for the modulation of spatially uniform light. Turning off the annulus attenuated dramatically high-frequency flicker sensitivity and enhanced somewhat low-frequency sensitivity. This phenomenon reflects a modulation of synaptic transfer between cones and second-order neurons that is mediated by cones, and it will be referred to as cone-mediated surround enhancement (CMSE). Our main results are as follows: (a) The change in test-spot response sensitivity and kinetics upon dimming a steady surrounding annulus is a consequence of the change in spatial contrast rather than change in overall light level. (b) Introduction of moderate contrast between the mean spot intensity and steady surrounding light intensity causes a marked change in spot response kinetics. (c) The dependence of spot response kinetics on surrounding light can be described by a phenomenological model in which the steady state gain and the time constant of one or two single-stage, low-pass filters increase with decreasing annular light intensity (d) The effect of surrounding light on spot responses of a given HC is not determined by change in the steady component of the membrane potential of that cell. (e) Light outside the receptive field of an HC can affect that cell's spot response kinetics. (f) In an expanding annulus experiment, the distance over which steady annular light affects spot response kinetics varies among HCs and can be quite different even between two cells with closely matched receptive field sizes. (g) The degree of CMSE is correlated with HC receptive field size. This correlation suggests that part of the enhancement mechanism is located in the HC. Taken together, our results suggest the involvement of the inner retina in CMSE.

Surround enhancement (sensitization) is a poorly understood form of network adaptation in which the kinetics of the responses of retinal neurons to test stimuli become faster, and absolute sensitivity of the responses increases with increasing level of steady, surrounding light. Surround enhancement has been observed in all classes of retinal neurons in lower vertebrates except cones, in some primate retinal ganglion cells, and in human psychophysical studies. In theory, surround enhancement could be mediated by two broad classes of mechanisms, which are not mutually exclusive: one in which the kinetics of the transduction linking cone voltage to postsynaptic current in second-order neurons is modulated, and another in which the transformation of postsynaptic current to membrane voltage is modulated. We report here that both classes of mechanism play a role in surround enhancement measured in turtle horizontal cells (HCs). We stimulated the retina by modulating sinusoidally the illuminance of a bar placed at various positions in the HC receptive field. The bar was surrounded by either equally luminant or dim, steady light. Interpretation of responses in the context of a model for the cone-HC network led to the conclusion that the speeding up of response kinetics —due to selective increase in response gain at high temporal frequencies — by surround illuminance is almost completely accounted for by the change in the kinetics of the transduction linking cone membrane potential to HC postsynaptic current. However, surround illuminance also had an additional, surprising effect on the transformation between postsynaptic current and voltage: the space constant for signal spread in the HC network for the dim-surround condition was roughly twice as large as that for the bright-surround condition. Thus, increasing surround illuminance had analogous effects in the spatial and temporal domains: it restricted the time course and the spatial spread of signal. Both effects were dependent on the contrast between the mean bar illuminance and that of the surround, rather than on overall light level. When the stimulus with the bright surround was dimmed uniformly by a neutral density filter, the space constant did not increase, and response gain at high temporal frequencies did not decrease. Pharmacological experiments performed with dopamine and various agonists and antagonists indicated that, although exogenous dopamine can influence surround enhancement, endogenous dopamine does not play an important role in surround enhancement. We conclude that contrast in background light modulates the spatiotemporal properties of signal processing in the outer retina, and does so by a non-dopaminergic mechanism.

Due to an inadvertent oversight on the part of the authors, an important citation was omitted from the paper. The differential-Hebbian learning rule attributed to Klopf (1988) was also independently derived by Bart Kosko, of the University of Southern California School of Engineering, who coined the term "differential-Hebbian learning." Kosko's important contributions are described in:

Cognitive Neuroscience in Europe PS reviews three strands of European cognitive/behavioral neuroscience.Here are reviews ofthree recent volumes from Europe representing three major traditions or fields that have led to the currently emerging field of "cognitive neuroscience": brain and behm'ior, neural networks and neuropsychology.In Brain and Behavior, Bures and his co-authors remind us that in spite ofits current glamour, molecular neurobiological approaches call ne~'er lead to an understanding of the brain substrates of behm'ior. Bures and Bure.sol'lj are pioneering and leading scientists ill the sllldy of brainbases of learning and memory. They are now approaching the end of their careers and possess great wisdom-this book must be read by anyone professing an interest ill brain and behm'ior.. Neural Computers is edited by two members of the new generation of workers in the field of neural networks. Rolf Eckmiller and Christoph~'on der Malsburg. This volumesurveys the current state of the field. primarily In Europe, but also in the US and Japan. Europe is launching a major IIell' multinational initiath'e in neural networks (ESPRIT). To date, the US. and to a somewhat lesser extent Japan, hm'e dominated the field. Judging by this~'olume, hown'er, the Europeans are not far behind. It remains true that the major thrust of this field is computational rather than biological, but sewral pieces in NeuralComputers represent attempts to bridge to more realistic biological neural networks. To me this is the fundamental problem yet to be soil'ed in the field of neural networks.In From Neuropsychology to Mental Structure, Tim Shallice oven'iell's progress in the past two decades in the field that can claim to be the cornerstone of cognith'e neuroscience. namely the characterization and analysis of the structures of the human mind from the sllldy of humans with brain damage. A major issue in the book is the current controversy al'er use ofilldh'idual case studies vs group studies. Bill the conceplllaifocus is on the modularity ofmental struclllre, a notion~'eT}' comfortable to those who work at a more analyticalle~'e1 on brain systems and functions in animal models.