Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia.

The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot's motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a 'behavioral trap'. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.

An open-source low-cost wireless sensor system for acquisition of human movement data.

Several fields of research such as medicine, robotics, sports, informatics, etc., require the analysis of human movement. Traditional systems for acquisition and analysis of human movement data are based on video cameras or active sensors. However, those systems are limited to high-resource settings. Wearable devices allow monitoring subjects outside typical clinical or research environments. Here, we present an open source low-cost wireless sensor system for acquisition of human movement data. Our system consists of two main parts: a server that stores data and, one or more wearable sensor modules that collect movement data through Inertial Measurement Units (IMUs) and transmit them wirelessly to the server. As a proof of concept, we measured human gait activity. Our results show that our system with IMUs can acquire quantifiable movement data. Characteristics such as open source code and its low-cost, make our system a viable alternative for clinical or research.

Analixity: An open source, low-cost analysis system for the elevated plus maze test, based on computer vision techniques.

Manual analysis of behavioral tests in rodents involves inspection of video recordings by a researcher that assesses rodent movements to quantify parameters related with a behavior of interest. The assessment of the researcher during the quantification of such parameters can introduce variability among experimental conditions or among sessions of analysis. Here, we introduce Analixity, a video processing software for the elevated plus maze test (EPM), in which quantification of behavioral parameters is automatic, reducing the time spent in analysis and solving the variability problem. Analixity is an adaptable multiplatform open-source system. Analixity generates an Excel file with the quantified behavioral variables, such as time spent in open and closed arms and in the center zone, number of entries to each zone and total distance traveled during the test. For validation, we compared results obtained by Analixity with results obtained by manual analysis. We did not find statistically significant differences. In addition, we compared the results obtained by Analixity with results obtained by the commercial software ANY-maze. We did not find statistically significant differences in the quantification of parameters such as time spent in open arms, time spent in closed arms, time spent in center zone, number of closed arms, open arms entries, and anxiety index. We concluded that Analixity is an open-source software as reliable and effective as a commercial software.

A robot model of the basal ganglia: behavior and intrinsic processing.

The existence of multiple parallel loops connecting sensorimotor systems to the basal ganglia has given rise to proposals that these nuclei serve as a selection mechanism resolving competitions between the alternative actions available in a given context. A strong test of this hypothesis is to require a computational model of the basal ganglia to generate integrated selection sequences in an autonomous agent, we therefore describe a robot architecture into which such a model is embedded, and require it to control action selection in a robotic task inspired by animal observations. Our results demonstrate effective action selection by the embedded model under a wide range of sensory and motivational conditions. When confronted with multiple, high salience alternatives, the robot also exhibits forms of behavioral disintegration that show similarities to animal behavior in conflict situations. The model is shown to cast light on recent neurobiological findings concerning behavioral switching and sequencing.