## NeuroAI

Welcome to the NeurIPS 2024 NeuroAI Workshop! This workshop aims to bring together researchers and practitioners from the fields of neuroscience and artificial intelligence.

We are in an era of unprecedented advancement in artificial intelligence, driven by the remarkable progress in artificial neural networks (ANNs) over the past decade. The widespread adoption of these techniques across diverse domains, from language generation (e.g., GPT4o, Claude, Pi, etc) to machine vision (e.g., Sora, DALL-E, etc), highlights the rapid pace of innovation and their transformative impact. This momentum paves the way for exploring the intersections of artificial and natural intelligence – NeuroAI – which promises to unlock novel insights into biological neuronal function and drive the development of computationally less intensive artificial systems trained using small-data regimes.

## Topics

This burgeoning NeuroAI field is anchored by several key research areas including but not limited to:

- Neuro-inspired Computations: This research focuses on developing hardware and algorithms inspired by the biological neuronal structure and function, such as spiking neural networks and Hebbian plasticity. Incorporating neuro-inspired mechanisms such as continual learning principles allows these systems to adapt and improve over time without requiring retraining from scratch, further enhancing their robustness and applicability in dynamic scenarios. For instance, recent advancements in neuromorphic computing, particularly the development of Intel’s Loihi chip, demonstrate the potential of spiking neural networks to improve the efficiency and capabilities of artificial systems. The Loihi chip mimics the brain’s neuronal architectures and synaptic plasticity mechanisms, enabling more efficient processing and learning in real-time environments.

- Explainable AI in Neuroscience: This research explores the integration of AI models with neuroscientific principles to enhance interpretability and explainability. For instance, the use of neural network architectures inspired by the human brain’s hierarchical processing can improve the interpretability of complex models by aligning them with known neural mechanisms. Techniques such as using neuro-symbolic AI, where symbolic reasoning is combined with neural networks, allow for the creation of models that can explain their reasoning in human-understandable terms. Additionally, methods like neural activity mapping and brain-inspired learning algorithms, such as those leveraging Hebbian learning principles, offer ways to trace AI decision paths back to their origins in the model’s structure.

- Self-supervised Systems in NeuroAI: This area of research investigates emerging paradigms in biological intelligence systems and their integration into artificial systems, focusing on areas such as predictive coding, active inference, and self-supervised learning. Integrating self-supervised and unsupervised learning methods inspired by neurobiological processes enables AI systems to learn from unstructured data without explicit labels, mirroring how biological intelligent systems adapt in real-world environments. This approach enhances the adaptability and intuition of AI technologies, paving the way for more human-like processing capabilities. The unsupervised learning mechanisms of the brain or other synthetic biological intelligent systems may be explained via predictive coding. The theory posits that the brain continuously generates predictions about incoming sensory data and updates these predictions based on prediction errors. This framework aligns with active inference, where the brain not only predicts sensory inputs but also acts to minimize surprise, making perception and action two sides of the same coin. These principles have been exemplified in computational models that emulate how the brain processes visual information, leading to advancements in computer vision, autonomous systems, and object recognition.

- Neuro-inspired reasoning and decision-making: This topic is an interdisciplinary research area that draws insights from neuroscience, cognitive science, and artificial intelligence to create computational models that emulate the brain’s problem-solving capabilities. Here, the aim is to develop systems that can process information, learn from experience, and make decisions in ways that closely resemble human cognition. By incorporating principles such as distributed representations, adaptive learning, and context-sensitive architectures, the aim is to create more flexible, robust, and human-like reasoning systems. Importantly, these models have potential applications across various domains, including robotics, cognitive computing, etc.

- Cognitive functions in AI: This research looks to understand the range of mental processes that AI systems emulate from human cognition e.g., language processing, problem-solving, and creativity. For this new methods and benchmarks need to be designed. For example, language processing is evaluated using natural language understanding and generation tests, including translation and summarisation tasks. Reasoning and problem-solving abilities are tested through logic puzzles, mathematical problems, and strategic games. Creativity is a more challenging domain to evaluate, but it’s often tested through tasks like generating original stories, artwork, or music. These assessments help researchers gauge the progress of AI systems in replicating human-like cognitive processes and identify new areas for improvement