No abstract

This paper presents the methods for sensing vehicles (localization and tracking) implemented on the ARGO vehicle. The perception of the environment is performed through the sole processing of images acquired from a stereo vision system installed on board of the vehicle.

The human brain is a remarkable computing machine, i.e. vastly parallel, self-organizing, robust, and energy efficient. To gain a better understanding into how the brain works, a cyborg (cybernetic organism, a combination of machine and living tissue) is currently being made in an interdisciplinary effort, known as the Cyborg project. In this paper we describe how living cultures of neurons (biological neural networks) are successfully grown in-vitro over Micro-Electrode Arrays (MEAs), which allow them to be interfaced to a robotic body through electrical stimulation and neural recordings. Furthermore, we describe the bio-and nano-technological procedures utilized for the culture of such dissociated neural networks and the interface software and hardware framework used for creating a closed-loop hybrid neuro-system. A Reservoir Computing (RC) approach is used to harness the computational power of the neuronal culture.

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.