First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The authors present a model of persistent activity in neural networks as typically observed in working memory tasks and modeled as attractor dynamics. The authors note the shortcomings of existing models, namely the reliance on implausible global excitatory or inhibitory signals to reset the network dynamics after settling into an attractor state and the superfluousness of such a stable attractor, since in working memory tasks, the dynamics need only persistent as long as the task requires, not indefinitely. In the proposed model, these issues are confronted by incorporating short term synaptic facilitation and depression into a network model. Using a mean-field approach, the authors identify stable fixed points of the rate dynamics and how these fixed-points change as a function of network connectivity and timescale parameters.

Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustainable monitoring. This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring. To address this challenge in the context of activity monitoring, we have designed an adaptive activity monitoring framework. We first propose a switching Gaussian process to model the observed sensor signals emitting from the underlying activity states. To efficiently compute the Gaussian process model likelihood and quantify the context prediction uncertainty, we propose a block circulant embedding technique and use Fast Fourier Transforms (FFT) for inference. By computing the Bayesian loss function tailored to switching Gaussian processes, an adaptive monitoring procedure is developed to select features from available sensors that optimize the trade-off between sensor power consumption and the prediction performance quantified by state prediction entropy. We demonstrate the effectiveness of our framework on the popular benchmark of UCI Human Activity Recognition using Smartphones.

We have explored with three notions: conceptualization, and contextualization from situated cognition, and psychic reflection from activity theory for identifying activities into a method called the activity states framework (ASF). The purpose of our work is to build an AI system based on ASF for the identification of collaborators activities during situated context, e.g., collaborators are engaged in a tutorial activity. In this paper, we will introduce and propose how Web-mediated collaborative activities can be identified from collaborators communication exchanges by applying the ASF.

Oscillatory neural networks have been an increasing focus of study over the last several years. Here we consider simple oscillatory memories for short-term retention of items occurring as temporal sequences. By incorporating decay as well as interference, we find that it is easy to match behavioral data from human subjects recalling temporal sequences under different situations by adjusting a single parameter in the model. These results suggest that simple oscillatory memories capture at least some key properties of human short-term memory, and might be used effectively in future biologically-inspired cognitive architectures.

A very simple model of two reciprocally connected attractor neural networks is studied analytically in situations similar to those encountered in delay match-to-sample tasks with intervening stimuli and in tasks of memory guided attention. The model qualitatively reproduces many of the experimental data on these types of tasks and provides a framework for the understanding of the experimental observations in the context of the attractor neural network scenario.

A very simple model of two reciprocally connected attractor neural networks is studied analytically in situations similar to those encountered in delay match-to-sample tasks with intervening stimuli and in tasks of memory guided attention. The model qualitatively reproduces many of the experimental data on these types of tasks and provides a framework for the understanding of the experimental observations in the context of the attractor neural network scenario.

A very simple model of two reciprocally connected attractor neural networks isstudied analytically in situations similar to those encountered in delay match-to-sample tasks with intervening stimuli and in tasks of memory guided attention. The model qualitatively reproduces many of the experimental data on these types of tasks and provides a framework for the understanding of the experimental observations in the context of the attractor neural network scenario.