Contextual Variation and Memory Retrieval at Six Months

In 2 experiments we asked whether training in multiple contexts could eliminate context-dependent memory retrieval at 6 months as it does for adults. We found that 24-hour retention was disrupted when infants were trained in one context and tested in another but not when they were trained in multiple contexts prior to testing in a novel context (Experiment 1). After a long delay, however, training in multiple contexts did not facilitate memory retrieval in a novel context: An otherwise effective retrieval cue (the training mobile) did not alleviate forgetting 3 weeks later when it was presented in a novel context (Experiment 2). These findings demonstrate that multiple learning contexts can override the debilitating effects of an altered context on memory retrieval at 6 months, but only over the short term. The resistance of older memories to retrieval in novel contexts after long periods of disuse may be adaptive, insuring that potentially inappropriate or irrelevant memories will not be expressed.     

Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control

This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov–Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results.