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.
Selectivity and invariance in dynamic movie stimuli
This project aims to use naturalistic dynamic stimuli in movies to study selectivity and invariance during visual recognition. The questions include “What/who is there?”, “What is the person doing?”, “What is where?”. These efforts combine computational modeling to evaluate biologically plausible algorithms and physiological recordings to investigate the neural circuits along the ventral visual stream involved in visual recognition for dynamic and complex stimuli. These data sets are also used to investigate eye movements while watching movies and to evaluate how well biologically plausible algorithms can predict saccades (i.e., addressing the question of “What will happen next?” in terms of eye movements).
Associated Research Thrust(s):
Principal Investigators:
Postdoctoral Associates and Fellows:
Graduate Students: