Mathematical modelling and application of frog choruses as an autonomous distributed communication system

Collective signalling is shaped by feedbacks between signaller variation, receiver perception and acoustic environment in a simulated communication network

Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally and identifies aspects of information processing in complex environments that remain to be investigated. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Excitatory and inhibitory interactions affect the balance of chorus activity and energy efficiency in the aggregations of male frogs: Numerical simulations using a hybrid dynamical model

We numerically study the role of excitatory and inhibitory interactions in the aggregations of male frogs. In most frogs, males produce sounds to attract conspecific females, which activates the calling behavior of other males and results in collective choruses. While the calling behavior is effective for mate attraction, it requires high energy consumption. In contrast, satellite behavior is an alternative mating strategy in which males deliberately stay silent in the vicinity of a calling male and attempt to intercept the female attracted to the caller, allowing the satellite males to reduce their energy consumption while having a chance of mating. Here we propose a hybrid dynamical model in which male frogs autonomously switch among three behavioral states (i.e., calling state, resting state, and satellite state) due to the excitatory and inhibitory interactions. Numerical simulations of the proposed model demonstrated that (1) both collective choruses and satellite behavior can be reproduced and (2) the satellite males can prolong the energy depletion time of the whole aggregation while they split the maximum chorus activity into two levels over the whole chorusing period. This study highlights the importance of the multiple behavioral types and their transitions for the performance of the whole aggregation.

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

Visualization of a chorus structure in multiple frog species by a sound discrimination device

We developed a sound discrimination device to identify and localize the species of nocturnal animals in their natural habitat. The sound discrimination device is equipped with a microphone, a light-emitting diode, and a band-pass filter. By tuning the center frequency of the filter to include a dominant frequency of the calls of a focal species, we enable the device to be illuminated only when detecting the calls of the focal species. In experiments in a laboratory room, we tuned the sound discrimination devices to detect the calls of Hyla japonica or Rhacophorus schlegelii and broadcast the frog calls from loudspeakers. By analyzing the illumination pattern of the devices, we successfully identified and localized the two kinds of sound sources. Next, we placed the sound discrimination devices in a field site where actual male frogs (H. japonica and R. schlegelii) produced sounds. The analysis of the illumination pattern demonstrates the efficacy of the developed devices in a natural environment and also enables us to extract pairs of male frogs that significantly overlapped or alternated their calls.

Interaction mechanisms quantified from dynamical features of frog choruses

We employ a mathematical model (a phase oscillator model) to describe the deterministic and stochastic features of frog choruses in which male frogs attempt to avoid call overlaps. The mathematical model with a general interaction term is identified using a Bayesian approach, and it qualitatively reproduces the stationary and dynamical features of the empirical data. In addition, we quantify the magnitude of attention paid among the male frogs from the identified model, and then analyse the relationship between attention and behavioural parameters using a statistical approach. Our analysis demonstrates a negative correlation between attention and inter-frog distance, and also suggests a behavioural strategy in which male frogs selectively attend to a less attractive male frog (i.e. a male producing calls at longer intervals) in order to more effectively advertise their superior relative attractiveness to females.