Temporal Processing of Vibratory Communication Signals at the Level of Ascending Interneurons in Nezara viridula (Hemiptera: Pentatomidae)

During mating, males and females of N. viridula (Heteroptera: Pentatomidae) produce sex- and species-specific calling and courtship substrate-borne vibratory signals, grouped into songs. Recognition and localization of these signals are fundamental for successful mating. The recognition is mainly based on the temporal pattern, i.e. the amplitude modulation, while the frequency spectrum of the signals usually only plays a minor role. We examined the temporal selectivity for vibratory signals in four types of ascending vibratory interneurons in N. viridula. Using intracellular recording and labelling technique, we analyzed the neurons' responses to 30 pulse duration/interval duration (PD/ID) combinations. Two response arrays were created for each neuron type, showing the intensity of the responses either as time-averaged spike counts or as peak instantaneous spike rates. The mean spike rate response arrays showed preference of the neurons for short PDs (below 600 ms) and no selectivity towards interval duration; while the peak spike rate response arrays exhibited either short PD/long ID selectivity or no selectivity at all. The long PD/short ID combinations elicited the weakest responses in all neurons tested. No response arrays showed the receiver preference for either constant period or duty cycle. The vibratory song pattern selectivity matched the PD of N. viridula male vibratory signals, thus pointing to temporal filtering for the conspecific vibratory signals already at level of the ascending interneurons. In some neurons the responses elicited by the vibratory stimuli were followed by distinct, regular oscillations of the membrane potential. The distance between the oscillation peaks matched the temporal structure of the male calling song, indicating a possible resonance based mechanism for signal recognition

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity

Wildlife and environmental monitoring using RFID and WSN technology

Wireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take considerable effort to replace, it is of major importance to use energy carefully. We present two efficient ways of extending the lifetime of such systems: 1. an adaptive duty cycling protocol and 2. an adaptive data management protocol. Further, we present some details of our deployed sensor network in Wytham Woods, Oxfordshire