From Neuron to Mind - Finding Meaning in Complex Brains

Abstract:

Intelligent behavior appears to require large and complex brains, whose functions are difficult to understand. In order to gain mechanistic understanding of the neural circuits supporting intelligent functions, we have developed a vertical circuit-dissection approach that allows us to find reproducible regions and networks of interest supporting specific functions and then target detailed electrophysiological investigations to these circuit nodes to uncover their function. I will illustrate this approach with one example, face recognition, and then show how we have applied it to other domains of cognition and parts of the brain. I will then focus on our current attempt to tackle a core problem in neuroscience, how networks of neurons generate abstract symbolic representations and the programs that operate on them. Intelligence is the capacity to solve new problems. To do so, the brain must generate novel thoughts and behaviors in a goal-directed manner. This creative capacity has been hypothesized to depend on cognitive operations resembling symbolic grammars, generative models with discrete atomic objects (“symbols”) that use rule-based operations to generate newly composed representations. Yet, whether and how symbolic grammars are implemented in neural substrates remains unknown. We tackle this question with an interdisciplinary approach to study the neural implementation of a grammar for action, centered on a novel drawing task.