Learning to discriminate new shapes is a fundamental visual ability for humans and other primates. It depends on long-term changes in shape computations in the ventral pathway of primate visual cortex, especially at final stages in IT (inferotemporal cortex). Our goal is to investigate these changes at the level of individual neurons and neural circuits, by (i) analyzing progressive shape computation changes in continuously identified neural populations across long timescales (weeks to months) and (ii) correlating these changes with improvements in shape discrimination accuracy and speed. We would achieve this goal by combining methodologies developed in our two laboratories. The Connor lab has developed mathematical analyses of neural shape computations, based on large-scale adaptive stimulus sampling guided by genetic algorithms and multi- dimensional parameterization of stimulus geometry. The Leopold lab has developed the use of microwire bundle implants for long-term electrophysiological recording from populations of IT neurons, continuously identified by their signature response patterns across 100s of stimuli. Adaptive sampling can leverage the order of magnitude increase in sampling time with microwire bundles, offering a new paradigm for high- throughput testing of mathematically tractable object stimuli in ventral pathway cortex. Based on our previous investigations of shape coding and shape processing dynamics, we hypothesize that learning to discriminate a new shape accurately and rapidly is based on a progression through distinct combinatorial computations operating on that shape's constituent fragments: (i) Initial low-accuracy behavior reflects linear combination of shape fragment signals, present in the untrained state, yielding only ambiguous information about complex shape configurations; (ii) Increasing accuracy during early learning reflects recurrent network nonlinear computations, yielding slow but unambiguous signals for shape fragment combinations; (iii) Increasing speed during late learning reflects feed-forward nonlinear computations, yielding accurate, fast performance. Chronic microwire recording will allow us to track this computational progression, for dozens of individual neurons, and correlate computational changes with behavioral improvements through time. This would be the first continuous observation of computational changes in individual IT cells during extended periods of visual learning (weeks to months). Whether or not the specific hypotheses are verified, this will provide the most direct insights to date into how specific changes in IT circuit-level information processing relate to shape learning, which is critical to our understanding of symbols and objects.

Public Health Relevance

Our study will provide a new window onto circuit-level changes that underlie visual learning. The results will help guide future educational strategies in diseases that impair visual shape recognition and discrimination (autism, dyslexia) and rehabilitative and prosthetic strategies for patients with compromised visual shape perception due to lesion-induced blindness or agnosias.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY025223-02
Application #
9035396
Study Section
Mechanisms of Sensory, Perceptual, and Cognitive Processes Study Section (SPC)
Program Officer
Flanders, Martha C
Project Start
2015-04-01
Project End
2019-03-31
Budget Start
2016-04-01
Budget End
2017-03-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Neurosciences
Type
Schools of Arts and Sciences
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Sasikumar, Dennis; Emeric, Erik; Stuphorn, Veit et al. (2018) First-Pass Processing of Value Cues in the Ventral Visual Pathway. Curr Biol 28:538-548.e3