Synchrony based learning rule of Hopfield like chaotic neural networks with desirable structure

In this paper a new learning rule for the coupling weights tuning of Hopfield like chaotic neural networks is developed in such a way that all neurons behave in a synchronous manner, while the desirable structure of the network is preserved during the learning process. The proposed learning rule is based on sufficient synchronization criteria, on the eigenvalues of the weight matrix belonging to the neural network and the idea of Structured Inverse Eigenvalue Problem. Our developed learning rule not only synchronizes all neuron’s outputs with each other in a desirable topology, but also enables us to enhance the synchronizability of the networks by choosing the appropriate set of weight matrix eigenvalues. Specifically, this method is evaluated by performing simulations on the scale-free topology.     

The acquisition of stone-tool use in captive chimpanzees

In order to investigate the acquisition of stone-tool use to crack nuts, experiments were conducted with five captive chimpanzees, employing combinations of trial-and-error, and three types of observation learning of varied difficulty. Two adults and one sub-adult succeeded in acquiring this behavior in the tests where tool use was demonstrated by the experimenter, or by a successful chimpanzee. Analysis of their behavior patterns showed the adults' and sub-adult's process to success to be divided into three stages; these can be characterized as trial-and-error, understanding of the method, and learning the technique, respectively. A factor for distinguishing success from failure is whether or not a subject observes demonstrations with interest in the behavior itself. This interest, in turn, depends on the intensity of the desire for nuts. When released, the successful chimpanzees practiced the same behavior in a group of chimpanzees, and the behavior newly diffused to an infant. Unlike the adults, the infant did not go through the staged process but reached the solution through goal-directed trial-and-error.     

Neurophysiological mechanisms involved in language learning in adults

Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished.     

iSEE: Interface structure,evolution, and energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.     

Learning by grasshoppers: association of colour/light intensity with food

ABSTRACT. Nymphs of Melanoplus sanguinipes learn to associate food with visual cues such as colour or light intensity. Such an ability in active exploratory insects may improve foraging efficiency by reducing time spent searching in unsuitable areas. Significant effects are demonstrated after a single rewarded trial, and the effects are extinguished after a variable number of unrewarded trials.     

Cage culture effects on mullets (Mugilidae) liver histology and blood chemistry profile

A comparative study of blood chemistry and histology was conducted on two groups of mullets (Mugilidae) living under different conditions with different feed sources. The aquaculture influenced mullet group (AIM), was collected near fish farms and the control group of mullet (CM) was caught in the waters without any aquaculture activities. Histological and biochemical procedures were employed to study liver histomorphology, plasma aspartate and alanine aminotransferase (AST, ALT), triglyceride (TRIG), cholesterol (CHOL), glucose (GLU) and total protein (TP) of both AIM and CM. Moderate histological changes (lipid infiltration) were observed in the liver of AIM. Significant changes in plasma variables were observed in AIM. Blood chemistry variables measured proved to be good indicators of artificial feed effects. Classical statistical approaches were applied to the blood chemistry and histopathology data. For the first time machine learning techniques were used to generate comprehensible classification models and to explore blood chemistry variable importance, strength, their mutual interactions or dependencies, and to investigate reliability of particular variables within the groups.     

Declining acceptability of a food plant for the polyphagous grasshopper Schistocerca americana: the role of food aversion learning

ABSTRACT Successive contacts with a food that was initially acceptable to the acridid Schistocerca americana (Drury) (Orthoptera: Acrididae) were monitored. It was found that acceptability, as measured by meal length, declined with experience until the food, spinach, was completely rejected. Reduced acceptability of spinach was maintained even after feeding for three meals on a fully acceptable and suitable food, a period of 2–3 h. Experiments with spinach-filled capsules placed in the gut suggested that the decline in acceptability was not due to direct feedback from the gut or haemolymph on the sensory or feeding control systems. Food aversion learning is indicated as the mechanism underlying the decline in acceptability.     

Distinct thermal migration behaviors in response to different thermal gradients in Caenorhabditis elegans

The nematode Caenorhabditis elegans exhibits a complex behavior called thermotaxis in response to temperature. This behavior is defined as a form of associative learning, in which temperature pairs with the presence or absence of food. Different interpretations have been drawn from the diverse results obtained by several groups, mainly because of the application of different methodologies for the analysis of thermotaxis. To clarify the discrepancies in behavioral observations and subsequent interpretations by different laboratories, we attempted to systematize several parameters to observe thermotaxis behavior as originally defined by Hedgecock and Russell in 1975. In this study, we show clearly how C. elegans can show a conditioned migration toward colder or warmer areas on a thermal gradient, given certain criteria necessary for the observation of thermotaxis. We thus propose to distinguish thermotaxis from other temperature-related behaviors, such as the warm avoidance response displayed at temperature gradients of 1°C/cm and steeper.