Neurorobotics—A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots

Neurorobots are robots whose control has been modeled after some aspect of the brain. Since the brain is so closely coupled to the body and situated in the environment, Neurorobots can be a powerful tool for studying neural function in a holistic fashion. It may also be a means to develop autonomous systems that have some level of biological intelligence. The present article provides my perspective on this field, points out some of the landmark events, and discusses its future potential

A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents’ training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans

Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context

Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown. As such, another objective of this article is to study the effect of merging these two visual processes when contextualized in a neuro-robotic model and an operant conditioning procedure. In this work, the learning task was solved using an artificial spiking neural network, acting as the brain controller for virtual and physical robots, showing a behavior modulation from the integration of both visual processes

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment

Entangled predictive brain: emotion, prediction and embodied cognition

How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called ‘predictive mind’, including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional ‘neurocentric’ stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it’s true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives

Internet and Biometric Web Based Business Management Decision Support

Internet and Biometric Web Based Business Management Decision Support MICROBE MOOC material prepared under IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials Prepared by: A. Kaklauskas, A. Banaitis, I. Ubarte Vilnius Gediminas Technical University, Lithuania Project No: 2020-1-LT01-KA203-07810