Diamond Certificate Report: The Ghana Project

The Ghana Project is a civic engagement and service learning project. This poster presentation will be about the happenings of the project, which is in its first year, starting last semester (it is to be a long term project). Born out of GOLD’s civic engagement efforts, The Ghana Project was created with two main goals: the first being to share Ghanaian culture, help educate others on life in Ghana, and share experiences with another country; the second, long term goal, is to raise money to help build a school in Ghana. The first year of The Ghana Project has been one of getting people used to Ghana, introducing them to the blossoming African country. The poster will review the events of the Project – such as the educational model of last semester, and the cultural events & speakers of this year – as well as discuss the nature of the project itself. It will also talk about the fundraising efforts of the Project, and relate them to the goal of assisting development in Ghana by means of building a school. Finally, there will be mention of the future plans of the Project, continuing into next year

Braitenberg Vehicles as Developmental Neurosimulation

The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.Comment: 32 pages, 8 figures, 2 table

Embodied Continual Learning Across Developmental Time Via Developmental Braitenberg Vehicles

There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. One lesson we can learn from this perspective is that the initialization of intelligent programs cannot solely rely on manipulation of numerous parameters. Our path forward is to present a design for developmentally-inspired learning agents based on the Braitenberg Vehicle. Using these agents to exemplify artificial embodied intelligence, we move closer to modeling embodied experience and morphogenetic growth as components of cognitive developmental capacity. We consider various factors regarding biological and cognitive development which influence the generation of adult phenotypes and the contingency of available developmental pathways. These mechanisms produce emergent connectivity with shifting weights and adaptive network topography, thus illustrating the importance of developmental processes in training neural networks. This approach provides a blueprint for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neural networks and active learning on these networks.Comment: 14 pages, 3 figure

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

State of the art. Overview of concepts, indicators and methodologies used for analyzing the social OMC.

This paper is a detailed analysis about the literature on the Social OMC from 2006-2010, focusing on how OMC research has been carried out. It specifically points to which theoretical framework/concepts are used, and how change is conceptualised and measured. It is organised in five sections. The first concerns visibility and awareness about the OMC; the second analyses research on the EU level coordination process; the third scrutinizes how features of the OMC have been analysed. The fourth and fifth sections, addressing how national integration of the OMC has been researched, respectively address substantive policy change as well as national policy-making. Strikingly, virtually all OMC research adopts theoretical frameworks derived from literature on Europeanisation and/or institutionalisation. Also, as the OMC is voluntary and sanction-free, it depends heavily on how and the the extent to which actors use it (agenda-setting, conflict resolution, maintaining focus on a policy issue, developing a policy dialogue, etc). OMC research has become nuanced and does highlight how, for which purpose and with which outcome actors engage with the OMC. Another finding is that there is data on policy issues addressed through the OMC, learning does take place and there is knowledge about domestic policy problems. However, the linkage between knowledge of an issue and direct use of the OMC for policy change in social policy is weak, but that may change with EU2020, where social policy has received a higher profile. Most research covers the EU-15, much more research needs to be undertaken in newer EU member states

Ethics and Bias in AI: Bridging the Gap via Interdisciplinary Collaboration

As decision-making becomes increasingly automated in the modern world, how do we ensure that Artificial Intelligence (AI) is applied ethically? One approach involves answering the calls for greater collaboration, between humanities and STEM, to address the gap between the two cultures and endeavor towards a more unified, holistic approach to developing and regulating advanced technology. Here, we look at a specific issue of AI Bias, both in terms of technical inception and managing legal and social impacts. Furthermore, this research investigates a broader question: what can we do to ensure that technological developments are aligned with an inclusive and improvement-oriented future - rather than merely regurgitating the historical injustice, bias, or marginalization? We examine these issues as a transdisciplinary pair of undergraduate researchers in Informatics & Computer Science and Law & Humanities

Multidisciplinary Research in Brains, Minds, and Machines

The main thrust of this research is about acquiring diverse foundational training in order to better understand the workings of biological and artificial systems. This presentation covers a year\u27s worth of research experiences from one Informatics major seeking to apply their skills in two different labs: computer science and neuroscience. Within applied informatics and computer science, the lab work focused on autonomous systems and drone swarm behavior - looking particularly at basic elements of autonomy and systems communication. In the neuroscience lab, the main project involved monitoring a major biological system feature (circadian rhythm) indirectly via EEG analysis of brain wave activity, including set-up, testing, and operating hardware and software. Together, these experiences led to gaining insight about systemic functioning in the animal and machine, embodied intelligence, and a greater respect for interdisciplinary research environments

Meta-brain Models: biologically-inspired cognitive agents

Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.Comment: 20 pages, 3 figure