Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis.

The acoustic startle reflex (ASR), a defensive response, is a contraction of the skeletal and facial muscles in response to an abrupt, intense (>80 db) auditory stimulus, which has been extensively studied in rats and humans. Prepulse inhibition (PPI) of ASR is the normal suppression of the startle reflex when an intense stimulus is preceded by a weak non-starting pre-stimulus. PPI, a measure of sensory motor gating, is impaired in various neuropsychiatric disorders, including schizophrenia, and is modulated by cognitive and emotional contexts such as fear and attention. We have modeled the fear modulation of PPI of ASR based on its anatomical substrates and taking into account data from behaving rats and humans. The model replicates the principal features of both phenomena and predicts underlying neural mechanisms. In addition, the model yields testable predictions.

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This paper presents a biologically inspired system for guiding and controlling a virtual hexapod robot. Our navigation and exploration system is composed of subsystems that execute processes of path integration, action selection, actuator control and correction of the robot's orientation. For the subsystem that serves the path integration function we modified an existing model of bio-inspired vector summation by adding the capability of performing online calculation. For the action selection subsystem that allows to switch between the behaviors of exploration, approaching a target and homing we modified an existing model of decision making for mediating social behaviors in mice. We added an additional circuit that projects a signal to the units representing each of the behaviors. In the case of the actuator control subsystem, the structure of a central pattern generator model that incorporates feedback and adaptation was used as the base for generating and transforming signals for the actuators. Finally, the orientation correction subsystem is a novel model that determines an error value from a desired and the current orientations. The proposed models were simulated as independent scripts and then implemented as ROS (Robot Operating System) nodes for controlling a robot simulation in Gazebo.

We propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed.