The influence of the parasitic angiosperm Orobanche aegyptiaca on the growth and shoot/root allometry of tomato plants was studied in two experiments. In the first, the density of infection was manipulated, with host and parasite biomass being measured 56 d after planting (d.a.p.). In the second, multiple harvests were made from 14 to 91 d.a.p. at one level of infection (20 mg seed dm""). The first experiment demonstrated an approximately linear reduction in host biomass up to 10 mg seed dm"^ soil, beyond which there was no further reduction. The parasite also depressed shoot/root ratio, which changed prior to any decrease in total biomass. These trends were maintained in the second experiment, becoming more pronounced with time. Significant reductions were observed in host biomass compared with that of uninfected controls from 42 d.a.p., following the emergence of the parasite above ground, and corresponded with the onset of lower relative growth rates (RGRs) in the infected plants. Infection also influenced components of RGR: there was a stimulation of leaf area ratio (LAR) and a depression of unit leaf rate (ULR). The data are discussed with respect to the influence of other parasitic weeds on host growth.

Reward-based learning can easily be applied to real life with a prevalence in children teaching methods. It also allows machines and software agents to automatically determine the ideal behavior from a simple reward feedback (e.g., encouragement) to maximize their performance. Advancements in affective computing, especially emotional speech processing (ESP) have allowed for more natural interaction between humans and robots. Our research focuses on integrating a novel ESP system in a relevant virtual neurorobotic (VNR) application. We created an emotional speech classifier that successfully distinguished happy and utterances. The accuracy of the system was 95.3 and 98.7% during the offline mode (using an emotional speech database) and the live mode (using live recordings), respectively. It was then integrated in a neurorobotic scenario, where a virtual neurorobot had to learn a simple exercise through reward-based learning. If the correct decision was made the robot received a spoken reward, which in turn stimulated synapses (in our simulated model) undergoing spike-timing dependent plasticity (STDP) and reinforced the corresponding neural pathways. Both our ESP and neurorobotic systems allowed our neurorobot to successfully and consistently learn the exercise. The integration of ESP in real-time computational neuroscience architecture is a first step toward the combination of human emotions and virtual neurorobotics.

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