Abstract thinking and complex problem solving constitute paradigmatic examples of computation emerging from interconnected neuronal circuits. The biological hardware represents the output of millions of years of evolution leading to neuronal circuits that provide fast, efficient, and fault-tolerant solutions to complex problems. Progress toward a quantitative understanding of emergent intelligent computations in cortical circuits faces several empirical challenges (e.g., simultaneous recording and analysis of large ensembles of neurons and their interactions), and theoretical challenges (e.g., mathematical synthesis and modeling of the neuronal ensemble activity). Our team of theoreticians and neurophysiologists is focused on systematic, novel, and integrative approaches to deciphering the neuronal circuits underlying intelligence. Understanding neuronal circuits that implement solutions to complex challenges is an essential part of scientific reductionism, leading to insights useful for developing intelligent machines.
Task Dependence of Visual Representations
Given an image, there is essentially an infinite number of questions that could be posed, all of which a human can answer effortlessly. This naturally leads to the question, is the visual representation of an image fixed regardless of the task at hand? We are probing this question by combining visual psychophysics, invasive neurophysiological recordings in humans, and computational modeling. Our experimental paradigm consists of presenting images to patients undergoing surgery for epilepsy and asking a series of distinct questions. By understanding the brain's capability to perform this task, we hope to inform computational models that can accomplish the core CBMM Challenge.
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