There is remarkable variation in how each of our brains are wired and how we each experience the world. Yet, neuroscience has traditionally focused on understanding the general principles of neural processing, often overlooking the diversity of cognitive experience. The course centers on a timely and fundamental question: what does “neurodiversity” mean? In what ways can neural architecture exhibit meaningful forms of diversity?
To address this question, we will draw on philosophical, historical, neurobiological, and computational perspectives. We begin by tracing the historical construction of “mental difference,” from early psychiatric categories to modern diagnoses such as autism. Moving beyond a medicalized model of thinking about psychiatric conditions, this course introduces a computational framework for neurodiversity, focusing on canonical neural computations and tuning parameters that may vary across individuals and give rise to different cognitive styles.
The core thesis of the course is that neurodiversity can be understood as variation in the parameterization of shared neural computations that shape how minds perceive, learn, and decide. We will examine key dimensions of neurodiversity through this lens, asking how variation in these parameters is expressed at cognitive, computational, and neural levels. For each dimension, we will consider the relevant theories, the neural circuits that support it, and evidence for atypical parameter tuning in conditions such as autism, schizophrenia, ADHD, OCD, and anxiety. We will also consider how these dimensions emerge over development, and close by asking what it would mean to build institutions, interventions, and technologies that are responsive to different kinds of minds.
This course is designed for students interested in cognitive science, cognitive neuroscience, and computational psychiatry. It emphasizes close reading of primary literature, collaborative synthesis across papers, and the development of mechanistic explanations linking behavior, computation, brain circuits, and clinical variation.