My name is Mikkel Lepperød and I work as a Research Scientist at Simula Research Laboratory where I am currently leading a group called bioAI in the department for Scientific Computing. I previously worked with biologically inspired artificial intelligence (AI) as a Postdoc at the University of Oslo (UiO), Norway, and before that I obtained a PhD in Neuroscience at UiO and a Master’s in Applied Mathematics at the Norwegian University of Life Sciences. For publications visit my Google Scholar page.

Research Overview

I investigate the fascinating bidirectional relationship between neuroscience and artificial intelligence: using insights from brain function to advance AI systems, while leveraging AI research to deepen our understanding of neural processes.

Research Focus

My work explores how biological intelligence emerges from interaction with the world. I study how animals, as exemplars of natural intelligence, learn both physical and abstract relationships through environmental exploration, such as developing efficient foraging strategies. By building interpretable models of these processes, we can bridge the gap between theoretical frameworks and observable behaviors.

Methodological Approach

My multidisciplinary approach allows me to tackle complex questions at the intersection of neuroscience and AI, working toward a deeper understanding of both biological and artificial learning systems.

• Biologically-inspired machine learning
• Computational modeling and simulation
• Causal inference and analysis
• Experimental neuroscience techniques and analysis

This integration enables investigation of neural mechanisms underlying memory, action, and sensory processing.

Research Philosophy

My scientific journey is driven by a deep fascination with how theoretical insights can illuminate nature’s workings. However, I recognize that complex systems like the brain might resist simple explanations. This challenge has shaped my approach: rather than narrowing my focus, I seek to embrace a transdisciplinary perspective, drawing from experimental and computational neuroscience, causal inference, and artificial intelligence.

I actively seek to broaden this perspective further by engaging with insights from philosophy, social sciences, psychology, and cognitive neuroscience. Understanding intelligence—whether natural or artificial—requires us to grapple with questions of consciousness, embodiment, social interaction, and human experience that these fields have long explored.

While this broad scope might appear dispersed, I firmly believe that understanding natural intelligence—and creating truly intelligent artificial systems—requires transcending traditional disciplinary boundaries. It’s precisely at these intersections where the most promising insights can emerge. This belief drives my commitment to collaboration across fields, as I view diverse perspectives and methodologies as essential to advancing our understanding of intelligence in all its forms.