CAREER: Characterizing feature selectivity and invariance in deep neural architectures

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The goals of this CAREER proposal are to help elucidate the principles that make robust object recognition possible. Object recognition is a problem that must be solved by all living organisms, from single-cell organisms to humans. Although the physical signals for recognition based on chemical events, light or sound waves are different, the computational requirements for analyzing these events appear to be similar. Specifically, there are two main properties that any system that mediates robust object recognition must have. The first property is known as "invariance." It endows neurons with a similar response to the same object observed from different viewpoints. The second property is known as "selectivity." Selectivity requires that neurons produce different responses to potentially quite similar objects (such as different faces) even when presented from similar viewpoints. It is straightforward to make detectors that are invariant but not selective or selective but not invariant. The difficulty lies in making detectors that are both selective and invariant. This CAREER project will develop statistical methods for simultaneously characterizing both the invariance properties of neurons and their selectivity to specific features in the environment. The developed methods will have three distinguishing characteristics. First, it will be possible to recover new types of invariance without any prior assumptions of what the dominant type of invariance is for any given neuron or brain region. Second, they will make it possible to characterize imperfect and approximate types of invariance. Third, the methods will be geared towards stimuli typical of the natural sensory environment that are rich in objects and elicit robust responses from neurons from all stages of sensory processing. These three properties of the developed methods will make it possible to simultaneously study multiple neurons both within and across different regions, without the need to adjust stimuli to a particular neuron or brain region. Application of the developed methods to responses of neurons that mediate visual and auditory object recognition in the brain will help reveal the common principles of sensory processing in the brain and may ultimately lead to improved designs of artificial recognition systems, including sensory prostheses.This research will be integrated into education and outreach activities involving K-12 students, undergraduate and graduate students. The educational component will help integrate knowledge acquired in computer science, physics, and neuroscience, training a new generation of scientists that are proficient in these disciplines. Outreach to local schools and museums, as well as the creation of an online course will help reach a diverse range of students both locally and worldwide.