Seminario IMC: How and why does the brain use multiple systems to learn individual skills?

El Instituto de Ingeniería Matemática y Computacional (IMC) invita al seminario que se dictará el miércoles 10 de junio.

Miércoles 10 de junio de 2026, 13:40 hrs. (Presencial en sala segundo piso (DILAB), Edificio Carreras Interdisciplinarias). Link Zoom disponible escribiendo a imc@uc.cl

ABSTRACT

It has been proposed that the brain integrates flexible, computationally expensive cortical processing with simpler, lower-cost subcortical mechanisms to achieve resource-efficient performance greater than that of either system alone. This alluring perspective is currently driving the development of theoretical frameworks that explore this hypothesis. I will present a normative framework for investigating this problem that combines three key features: (1) an environment with structure that can be leveraged to accelerate learning, (2) a nonstationary environment (i.e., changing reward contingencies), and (3) resource constraints (i.e. limited memory for the model). I will next present a simple example within this framework, showing how nonstationarity and memory constraints drive the cortical module to favor learning reward-agnostic information about the environment, and argue that this helps explain the separation of cortex and subcortical regions such as the basal ganglia, which are strongly reward-driven. Finally, I will introduce and analyze a specific mechanism by which information can be transferred from cortical to subcortical systems in the form of a Hebbian sequence-learning model.

BIO

Matthew Farrell graduated with a bachelor's degree in Mathematics from Cornell University and received his PhD from the University of Washington in the field of Applied Mathematics. His PhD thesis was supervised by Eric Shea-Brown, as well as Stefan Mihalas at the Allen Institute for Brain Science, with the topic of training artificial neural networks on simple tasks and analyzing these networks through the lens of representational dimensionality. He then moved to the research group of Cengiz Pehlevan at Harvard University as a Postdoctoral Fellow and later Swartz Postdoctoral Fellow, where he used mathematical tools such as group representation theory and the theory of large Toeplitz matrices to elucidate how neural representations can faithfully encode structure in the world and drive sequential motor outputs. Matthew is currently a Special Postdoctoral Researcher at the Japanese national research institute RIKEN, where he is working in the lab of Taro Toyoizumi to build a theory for neural module specialization and coordination that can help explain the diversity of areas found in the brain and support future developments in artificial intelligence.

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