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Temporal features in the vocalizations of animals and insects play an important role in a diverse range of species-specific activities such as mate selection, territoriality, and hunting. The neural mechanisms underlying the response to such stimuli remain largely unknown. Two species of anuran amphibian provide a starting point for the investigation of the neurological response to species-specific advertisement calls. Neurons in the anuran midbrain of Rana pipiens and Hyla regilla exhibit an atypical response when presented with a fixed number of advertisement calls. The general response to these calls is mostly inhibitory; only when the correct number of calls is presented at the correct repetition rate will this inhibition be overcome and the neurons reach a spiking threshold. In addition to rate-dependent call-counting, these neurons are sensitive to missed calls: a pause of sufficient duration—the equivalent of two missed calls—effectively resets a neuron to its initial condition. These neurons thus provide a model system for investigating the neural mechanisms underlying call-counting and interval specificity in audition. We present a minimal computational model in which competition between finely-tuned excitatory and inhibitory synaptic currents, combined with a small propagation delay between the two, broadly explains the three key features observed: rate dependence, call counting, and resetting. While limitations in the available data prevent the determination of a single set of parameters, a detailed analysis indicates that these parameters should fall within a certain range of values. Furthermore, while network effects are counter-indicated by the data, the model suggests that recruitment of neurons plays a necessary role in facilitating the excitatory response of counting neurons—although this hypothesis remains untested. Despite these limitations, the model sheds light on the mechanisms underlying the biophysics of counting, and thus provides insight into the neuroethology of amphibians in general.

The attempt to evolve complete embodied and situated artificial creatures in which both morphological and control characteristics are adapted during the evolutionary process has been and still represents a long term goal key for the artificial life and the evolutionary robotics community. Loosely inspired by ancient biological organisms which are not provided with a central nervous system and by simple organisms such as stick insects, this thesis proposes a new genotype encoding which allows development and evolution of mor- phology and neural controller in artificial agents provided with a distributed neural network. In order to understand if this kind of network is appropriate for the evolution of non trivial behaviours in artificial agents, two experiments (description and results will be shown in chapter 3) in which evolution was applied only to the controller’s parameters were performed. The results obtained in the first experiment demonstrated how distributed neural networks can achieve a good level of organization by synchronizing the output of oscillatory elements exploiting acceleration/deceleration mechanisms based on local interactions. In the second experiment few variants on the topology of neural architecture were introduced. Results showed how this new control system was able to coordinate the legs of a simulated hexapod robot on two different gaits on the basis of the external circumstances. After this preliminary and successful investigation, a new genotype encoding able to develop and evolve artificial agents with no fixed morphology and with a distributed neural controller was proposed. A second set of experiments was thus performed and the results obtained confirmed both the effectiveness of genotype encoding and the ability of distributed neural network to perform the given task. The results have also shown the strength of genotype both in generating a wide range of different morphological structures and in favouring a direct co-adaptation between neural controller and morphology during the evolutionary process. Furthermore the simplicity of the proposed model has showed the effective role of specific elements in evolutionary experiments. In particular it has demonstrated the importance of the environment and its complexity in evolving non-trivial behaviours and also how adding an independent component to the fitness function could help the evolutionary process exploring a larger space solutions avoiding a premature convergence towards suboptimal solutions.

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