ABSTRACT

Humans and animals can easily adapt to their environment with limited information. They sense the world around them and continuously adapt their behavior to the current situation by changing the ?configuration? of their nervous system, a phenomenon called plasticity. Though this ability seems natural to humans, it is very difficult to achieve in software or hardware systems. In addition, current continuous learning methods are trained under unrealistic conditions and require supervision. This project aims to understand how to endow autonomous agents, such as robots, with the adaptability and resiliency of biology. Biological plasticity in weakly electric fish will guide engineering of new machine learning algorithms. These algorithms will enable autonomous agents to continuously sense and adapt to their environment without interrupting operations for manual training. This interdisciplinary project is integrated with a range of outreach activities involving local high schools and undergraduate students. Workshops and demonstrations on biology-inspired machine learning will be organized, aimed at spurring interest of rural students in coding and robotics.

A grand challenge in artificial intelligence (AI) is how to achieve unsupervised continual learning in the open world. Current methods used in AI and machine learning operate with single-modality data, collected and consumed in controlled conditions, typically in a supervised manner. However, biological systems achieve lifelong learning by processing streams of multisensory data that continuously shape their neural networks (plasticity) while retaining previous knowledge (stability). This dynamic adaptation operates unsupervised, on a range of timescales and rules. The project will study those principles observed in the cerebellar feedback pathways of electric fish, which are responsible for driving plasticity, enabling adaptation of its function at different timescales and learning and forgetting at multiple speeds. This will enable the translational development of novel paradigms in continual learning that will support new levels of resiliency and lifelong learning in real-time autonomous systems in the open world. To achieve this goal the project will overcome some key technical hurdles, e.g., in enabling 1) data efficiency in processing inputs continuously as time-variant, potentially correlated, data streams in a fully unsupervised manner; 2) flexibility to learn and forget at different speeds; 3) generation of suitable internal representations from multiple modalities to improve autonomous resilience.

This project is jointly funded by the Emerging Frontiers in Research and Innovation Brain-Inspired Dynamics for Engineering Energy-Efficient Circuits and Artificial Intelligence Program (BRAID) and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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