Recognition of post-learning alteration of hippocampal ripples by convolutional neural network differs in the wild-type and AD mice.

Evidence indicates that sharp-wave ripples (SWRs) are primary network events supporting memory processes. However, some studies demonstrate that even after disruption of awake SWRs the animal can still learn spatial task or that SWRs may be not necessary to establish a cognitive map of the environment. Moreover, we have found recently that despite a deficit of sleep SWRs the APP/PS1 mice, a model of Alzheimer's disease, show undisturbed spatial reference memory.

Adaptive Tracking Control for Robots With an Interneural Computing Scheme.

Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine.

Operator control of interneural computing machines.

A dynamic representation of neural population responses asserts that motor cortex is a flexible pattern generator sending rhythmic, oscillatory signals to generate multiphasic patterns of movement. This raises a question concerning the design and control of new computing machines that mimic the oscillatory patterns and multiphasic patterns seen in neural systems. To address this issue, we design an interneural computing machine (INCM) made of plastic random interneural connections.

Decirculation process in neural network dynamics.

We describe a decirculation process which marks perturbations of network structure and neural updating that are necessary for evolutionary neural networks to proceed from one circulating state to another. Two aspects of control parameters, screen updating and flow diagrams, are developed to quantify such perturbations, and hence to manage the dynamics of evolutionary neural networks. A dynamic state-shifting algorithm is derived from the decirculation process.