Building machines that adapt and compute like brains

TL;DR

This paper emphasizes the importance of developing cognitive and neural models that can learn and think like humans, integrating data from both brain activity and behavior to advance understanding in cognitive science and neuroscience.

Contribution

It advocates for a new approach in cognitive computational neuroscience that combines cognitive-level and neural-level models, and emphasizes testing these models with brain and behavioral data.

Findings

Proposes integrating cognitive and neural models.

Highlights the importance of testing models with brain and behavioral data.

Aims to understand how cognition is implemented in biological brains.

Abstract

Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural- level models, understand their relationships, and test both types of models with both brain and behavioral data.