GABA Regulation of Burst Firing in Hippocampal Astrocyte Neural Circuit: A Biophysical Model.

It is now widely accepted that glia cells and gamma-aminobutyric acidergic (GABA) interneurons dynamically regulate synaptic transmission and neuronal activity in time and space. This paper presents a biophysical model that captures the interaction between an astrocyte cell, a GABA interneuron and pre/postsynaptic neurons. Specifically, GABA released from a GABA interneuron triggers in astrocytes the release of calcium (Ca 2+) from the endoplasmic reticulum via the inositol 1, 4, 5-trisphosphate (IP 3) pathway. This results in gliotransmission which elevates the presynaptic transmission probability rate (PR) causing weight potentiation and a gradual increase in postsynaptic neuronal firing, that eventually stabilizes. However, by capturing the complex interactions between IP 3, generated from both GABA and the 2-arachidonyl glycerol (2-AG) pathway, and PR, this paper shows that this interaction not only gives rise to an initial weight potentiation phase but also this phase is followed by postsynaptic bursting behavior. Moreover, the model will show that there is a presynaptic frequency range over which burst firing can occur. The proposed model offers a novel cellular level mechanism that may underpin both seizure-like activity and neuronal synchrony across different brain regions.

Exploring Self-Repair in a Coupled Spiking Astrocyte Neural Network.

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input-output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and γ -GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations.

The Need for Combining Implicit and Explicit Communication in Cooperative Robotic Systems.

As the number of robots used in warehouses and manufacturing increases, so too does the need for robots to be able to manipulate objects, not only independently, but also in collaboration with humans and other robots. Our ability to effectively coordinate our actions with fellow humans encompasses several behaviours that are collectively referred to as joint action, and has inspired advances in human-robot interaction by leveraging our natural ability to interpret implicit cues. However, our capacity to efficiently coordinate on object manipulation tasks remains an advantageous process that is yet to be fully exploited in robotic applications. Humans achieve this form of coordination by combining implicit communication (where information is inferred) and explicit communication (direct communication through an established channel) in varying degrees according to the task at hand. Although these two forms of communication have previously been implemented in robotic systems, no system exists that integrates the two in a task-dependent adaptive manner. In this paper, we review existing work on joint action in human-robot interaction, and analyse the state-of-the-art in robot-robot interaction that could act as a foundation for future cooperative object manipulation approaches. We identify key mechanisms that must be developed in order for robots to collaborate more effectively, with other robots and humans, on object manipulation tasks in shared autonomy spaces.

ARDebug: An Augmented Reality Tool for Analysing and Debugging Swarm Robotic Systems.

Despite growing interest in collective robotics over the past few years, analysing and debugging the behaviour of swarm robotic systems remains a challenge due to the lack of appropriate tools. We present a solution to this problem-ARDebug: an open-source, cross-platform, and modular tool that allows the user to visualise the internal state of a robot swarm using graphical augmented reality techniques. In this paper we describe the key features of the software, the hardware required to support it, its implementation, and usage examples. ARDebug is specifically designed with adoption by other institutions in mind, and aims to provide an extensible tool that other researchers can easily integrate with their own experimental infrastructure.

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms.

Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviors. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.