Understanding the Brain's Fundamental Cognitive Algorithms

Our lab aims to develop interpretable models of fundamental cognitive functions in primates: models that can explain basic elements of computations needed in real-world behavior. We study how the brain recognizes objects, makes decisions, and adapts its behavior to context, using a combination of neural population recordings in macaques, computational modeling, and perturbation methods.

Population Dynamics as the Language of Cognition

We approach cortical computation using the language of population geometry and dynamical systems, associating patterns of neural population activity with latent states in cognitive models. This framework is particularly powerful for cortical areas, where neurons are densely interconnected and information is encoded and transmitted across large, entangled networks of cells. In such systems, the relevant computational variables are not easily associated with individual neurons or local circuits because they emerge at the population level, in the geometry and dynamics of collective activity.

Cognitive Models as Computational Hypotheses

A central goal of the lab is to understand the brain's algorithm, in other words, the principles and equations that describe how the brain carries out its computations. We believe that cognitive models such as the drift-diffusion model are exceptionally powerful because they are both interpretable and capable of explaining a broad range of brain computations. We aim to extend these models, test their limits, and discover new principles. To ground these investigations, we primarily use perceptual decision-making tasks with naturalistic object stimuli. We use simple enough tasks for rigorous quantitative modeling, yet rich enough to engage the broad range of computational elements such as generalization, flexibility, identification, and abstraction.

Perturbing Computation to Test and Treat

If we really understand mechanisms, we should be able to manipulate them. We are developing methods to perturb brain function at the level of computation. This will be causal tests of our cognitive models and also become potential therapeutic strategies. Our goal is to selectively modify activity in neural population state space, targeting the dynamical structures that reflect ongoing computations. As one approach, we are developing closed-loop neurofeedback paradigms that train animals to modulate their own population dynamics, or to solve tasks by reshaping the associations between neural states and external events. We believe this approach will open a principled path toward both understanding and amending the computations underlying cognitive dysfunction.

Additional information: https://okazawagouki.github.io/