Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

A method for creating interactive, user-resembling avatars

Virtual reality (VR) applications have disseminated throughout several fields, with a special quest for immersion. The avatar is one of the key constituents of immersive applications, and avatar resemblance can provoke diverse emotional responses from the user. Yet a lot a virtual reality systems struggle to implement real life-like avatars. In this work, we propose a novel method for creating interactive, user-resembling avatars using available commercial hardware and software. Avatar visualization is possible with a point-cloud or a contiguous polygon surface, and avatar interactions with the virtual scenario happens through a body joint-approximation for contact. In addition, the implementation could be easily extended to other systems and its modular architecture admits improvement both on visualization and physical interactions. The code is under Apache License 2.0 and is freely available as Supplemental Information 1 in this article

The dynamics of a neural network of coupled phase oscillators with synaptic plasticity controlling a minimally cognitive agent

This work explores the neuronal synchronisation and phase information dynamics of an enhanced version of the widely used Kuramoto model of phase interacting oscillators. The framework is applied to a simulated robotic agent engaged in a minimally cognitive benchmark task. The outcomes of this research contribute not only to uncover the role of neuronal synchronisation and phase information in the generation of cognitive behaviours but also to the understanding of oscillatory properties in neural networks

A Multiple Hormone Approach to the Homeostatic Control of Conflicting Behaviours in an Autonomous Mobile Robot

This work proposes a biologically inspired system for the coordination of multiple and possible conflicting behaviours in an autonomous mobile robot, devoted to explore novel scenarios while ensuring its internal variables dynamics. The proposed evolutionary artificial homeostatic system, derived from the study of how an organism would self-regulate in order to keep its essential variables within a limited range (homeostasis), is composed of an artificial endocrine system, including two hormones and two hormone receptors, and also three previously evolved NSGasNet artificial neural networks. It is shown that the integration of receptors enhance the system robustness without incorporating to the three evolved NSGasNets more a priori knowledge. The experiments conducted also show that the proposed multi-hormone evolutionary artificial homeostatic system is able to successfully coordinate a multiple and conflicting behaviours task, being also robust enough to cope with internal and external disruptions

Evolving an Artificial Homeostatic System

Theory presented by Ashby states that the process of homeostasis is directly related to intelligence and to the ability of an individual in successfully adapting to dynamic environments or disruptions. This paper presents an artificial homeostatic system under evolutionary control, composed of an extended model of the GasNet artificial neural network framework, named NSGasNet, and an artificial endocrine system. Mimicking properties of the neuro-endocrine interaction, the system is shown to be able to properly coordinate the behaviour of a simulated agent that, presents internal dynamics and is devoted to explore the scenario without endangering its essential organization. Moreover, sensorimotor disruptions are applied, impelling the system to adapt in order to maintain some variables within limits, ensuring the agent survival. It is envisaged that the proposed framework is a step towards the design of a generic model for coordinating more complex behaviours, and potentially coping with further severe disruptions

Homeostasis and evolution together dealing with novelties and managing disruptions

Purpose ¿ The purpose of this paper is to present an artificial homeostatic system whose parameters are defined by means of an evolutionary process. The objective is to design a more biologically plausible system inspired by homeostatic regulations observed in nature, which is capable of exploring key issues in the context of robot behaviour adaptation and coordination. Design/methodology/approach ¿ The proposed system consists of an artificial endocrine system that coordinates two spatially unconstrained GasNet artificial neural network models, called non-spatial GasNets. Both systems are dedicated to the definition of control actions in autonomous navigation tasks via the use of an artificial hormone and a hormone receptor. A series of experiments are performed in a real and simulated scenario in order to investigate the performance of the system and its robustness to novel environmental conditions and internal sensory disruptions. Findings ¿ The designed system shows to be robust enough to self-adapt to a wider variety of disruptions and novel environments by making full use of its in-built homeostatic mechanisms. The system is also successfully tested on a real robot, indicating the viability of the proposed method for coping with the reality gap, a well-known issue for the evolutionary robotics community. Originality/value ¿ The proposed framework is inspired by the homeostatic regulations and gaseous neuro-modulation that are intrinsic to the human body. The incorporation of an artificial hormone receptor stands for the novelty of this paper. This hormone receptor proves to be vital to control the network's response to the signalling promoted by the presence of the artificial hormone. It is envisaged that the proposed framework is a step forward in the design of a generic model for coordinating many and more complex behaviours in simulated and real robots, employing multiple hormones and potentially coping with further severe disruptions