Supercritical dynamics at the edge-of-chaos underlies optimal decision-making

Critical dynamics, characterized by scale-free neuronal avalanches, is thought to underlie optimal function in the sensory cortices by maximizing information transmission, capacity, and dynamic range. In contrast, deviations from criticality have not yet been considered to support any cognitive processes. Nonetheless, neocortical areas related to working memory and decision-making seem to rely on long-lasting periods of ignition-like persistent firing. Such firing patterns are reminiscent of supercritical states where runaway excitation dominates the circuit dynamics. In addition, a macroscopic gradient of the relative density of Somatostatin (SST+) and Parvalbumin (PV+) inhibitory interneurons throughout the cortical hierarchy has been suggested to determine the functional specialization of low- versus high-order cortex. These observations thus raise the question of whether persistent activity in high-order areas results from the intrinsic features of the neocortical circuitry. We used an attractor model of the canonical cortical circuit performing a perceptual decision-making task to address this question. Our model reproduces the known saddle-node bifurcation where persistent activity emerges, merely by increasing the SST+/PV+ ratio while keeping the input and recurrent excitation constant. The regime beyond such a phase transition renders the circuit increasingly sensitive to random fluctuations of the inputs -i.e., chaotic-, defining an optimal SST+/PV+ ratio around the edge-of-chaos. Further, we show that both the optimal SST+/PV+ ratio and the region of the phase transition decrease monotonically with increasing input noise. This suggests that cortical circuits regulate their intrinsic dynamics via inhibitory interneurons to attain optimal sensitivity in the face of varying uncertainty. Hence, on the one hand, we link the emergence of supercritical dynamics at the edge-of-chaos to the gradient of the SST+/PV+ ratio along the cortical hierarchy, and, on the other hand, explain the behavioral effects of the differential regulation of SST+ and PV+ interneurons by neuromodulators like acetylcholine in the presence of input uncertainty

An Interactive Space as a Creature:Mechanisms of Agency Attribution and Autotelic Experience

Interacting with an animal is a highly immersing and satisfactory experience. How can interaction with an artifact can be imbued with the quality of an interaction with a living being? The authors propose a theoretical relationship that puts the predictability of the human-artifact interaction at the center of the attribution of agency and experience of “flow.” They empirically explored three modes of interaction that differed in the level of predictability of the interactive space's behavior. The results of the authors' study give support to the notion that there is a sweet spot of predictability in the reactions of the space that leads users to perceive the space as a creature. Flow factors discriminated between the different modes of interaction and showed the expected nonlinear relationship with the predictability of the interaction. The authors' results show that predictability is a key factor to induce an attribution of agency, and they hope that their study can contribute to a more systematic approach to designing satisfactory and rich interaction between humans and machines

Using a multi-task adaptive vr system for upper limb rehabilitation in the acute phase of stroke

Nowadays, stroke has become one the main causes of adult disability leading to life-lasting effects, including motor and cognitive deficits. Here we explore the benefits of the use of virtual reality (VR) for the rehabilitation of motor deficits following stroke. We have developed the Rehabilitation Gaming System (RGS), a VR-based apparatus designed for the treatment of the upper extremities. The RGS is a multi-level adaptive system that provides a task oriented training of graded complexity that is online adjusted to the capabilities of the patients. We show results from an ongoing study that evaluates the impact of this system on the recovery of patients in the acute phase of stroke (n=14). The results suggest that the system induces a sustained improvement during treatment, with observed benefits in the performance of activities of daily living.info:eu-repo/semantics/publishedVersio

Physiological responses during performance within a virtual scenario for the rehabilitation of motor deficits

Real-time physiological feedback can be used to modulate a virtual reality (VR) experience. It is not obvious, however, which parameters are most effective in achieving this such as heart rate variability and or the electrodermal response. Here we address this question by assessing the impact of the events generated by a VR based rehabilitation system on the affective state of human users. We show how the Rehabilitation Gaming System (RGS), a tool developed for the rehabilitation of motor deficits following stroke, can be enhanced using the online monitoring of bodily changes that are not under direct voluntary control. We show specific effects of the RGS on the autonomic nervous system and we propose how to use these for the modulation of the emotional state of the subject and performance.info:eu-repo/semantics/publishedVersio

The EASEL project: Towards educational human-robot symbiotic interaction

This paper presents the EU EASEL project, which explores the potential impact and relevance of a robot in educational settings. We present the project objectives and the theorectical background on which the project builds, briefly introduce the EASEL technological developments, and end with a summary of what we have learned from the evaluation studies carried out in the project so far

A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

Meyer HG, Bertrand O, Paskarbeit J, Lindemann JP, Schneider A, Egelhaaf M. A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot. In: Lepora FN, Mura A, Mangan M, Verschure FMJP, Desmulliez M, Prescott JT, eds. Biomimetic and Biohybrid Systems: 5th International Conference, Living Machines 2016, Edinburgh, UK, July 19-22, 2016. Proceedings. Cham: Springer International Publishing; 2016: 167-178.While navigating their environments it is essential for autonomous mobile robots to actively avoid collisions with obstacles. Flying insects perform this behavioural task with ease relying mainly on information the visual system provides. Here we implement a bioinspired collision avoidance algorithm based on the extraction of nearness information from visual motion on the hexapod walking robot platform HECTOR. The algorithm allows HECTOR to navigate cluttered environments while actively avoiding obstacles

Taking Connectionism Seriously:

Connectionism is drawing much attention as a new paradigm for cognitive science. A n important objective of connectionism has become the definition of a subsymbolic bridge between the mind and the brain. By analyzing an important example of this subsymbolic approach, NETtalk, I will show that this type of connectionism does not fulfil its promises and is applying new techniques in a symbolic approach. It is shown that connectionist models can only become part of such a new approach when they are embedded in an alternative conceptual framework where the emphasis is not placed upon what knowledge a system must posses to be able to accomplish a task but on how a system can develop this knowledge through its interaction with the environment

How? Why? What? Where? When? Who? Grounding Ontology in the Actions of a Situated Social Agent

Robotic agents are spreading, incarnated as embodied entities, exploring the tangible world and interacting with us, or as virtual agents crawling over the web, parsing and generating data. In both cases, they require: (i) processes to acquire information; (ii) structures to model and store information as usable knowledge; (iii) reasoning systems to interpret the information; and (iv) finally, ways to express their interpretations. The H5W (How, Why, What, Where, When, Who) framework is a conceptualization of the problems faced by any agent situated in a social environment, which has defined several robotic studies. We introduce the H5W framework, through a description of its underlying neuroscience and the psychological considerations it embodies, we then demonstrate a specific implementation of the framework. We will focus on the motivation and implication of the pragmatic decisions we have taken. We report the numerous studies that have relied upon this technical implementation as a proof of its robustness and polyvalence; moreover, we conduct an additional validation of its applicability to the natural language domain by designing an information exchange task as a benchmark