Seeking interpretability: The parallels between biological and artificial neural networks

Recent advances in neuroscience and artificial intelligence have highlighted striking parallels in how researchers approach understanding both biological and artificial neural networks, suggesting opportunities for cross-pollination of methods and insights between these fields.

Historical context: The evolution of neural network interpretation has followed remarkably similar paths in both biological and artificial systems, beginning with single-neuron studies and progressing to more complex representational analyses.

• The study of biological neural networks began in the late 19th century with Ramón y Cajal’s groundbreaking neuron doctrine
• Technological advances enabled multi-neuron recording, leading to discoveries about specific cellular responses to visual stimuli
• Recent research has expanded to examine the geometric properties of neural codes and their functional implications

Artificial network interpretation: The concept of monosemanticity has served as a fundamental principle in understanding artificial neural networks, though recent research suggests more complex interpretations are needed.

• Initial research focused on identifying individual neurons corresponding to specific, interpretable concepts
• Subsequent studies revealed that neurons can encode multiple concepts, requiring more sophisticated decoding methods
• Current research explores neural manifolds and geometric approaches to understanding network representations

Methodological convergence: Both fields have developed complementary analytical tools that could benefit from greater cross-disciplinary exchange.

• Manifold geometry has emerged as a key analytical framework in both domains
• Statistical physics and topology provide powerful tools for understanding network structure
• Nonlinear decoding and causal probing techniques offer new ways to understand network function

Future research directions: The frontier of neural network interpretability lies in connecting structural representations to functional outcomes across both biological and artificial systems.

• Researchers are increasingly focusing on how geometric properties relate to network function
• The integration of methods from both fields could accelerate progress in understanding neural networks
• New analytical approaches may help bridge the gap between structure and function

Synergistic potential: The parallel evolution of these fields suggests that closer collaboration between neuroscience and AI interpretability researchers could accelerate progress in both domains, while potentially revealing fundamental principles about how neural networks – both biological and artificial – process and represent information.