This is a resubmission of a research proposal to uncover the basis of timing estimation in the brain based on recent experimental and theoretical advances. This is a combined experimental and computational proposal. A fundamental task accomplished by the brain is the formation of adaptive behaviors generated in response to learned contingencies between environmental stimuli. Yet, the neural process by which we learn the behavioral relevance of environmental cues, specifically visual cues, and the mechanism by which brains generate temporal expectancies based on such visual evidence, is unknown. Exemplifying this process and motivating the work proposed here is the unexpected finding by Shuler and Bear that pairing visual stimuli with delayed reward leads to the emergence of reward-timing activity in the primary visual cortex (V1). This finding suggests that V1 does not act simply as a passive filter bank surveying the visual world, but instead contains complex internal programs that signal the behavioral relevance of visual events, and participates in computing the animals'behavioral response. Based on these recent findings, we developed a network-based theory of timing computation in cortex. This theory can account both for how times are computed and how they are learned. In this proposal we develop and test this model for understanding this issue, proffering a series of experimentally tractable predictions that test a radical notion: Learning visually-cued expectancies occurs locally within the primary visual cortex (V1) as a result of an interaction between an impinging reinforcement signal conveying the outcome of behavior with prior synaptic activity. We will test using optogenetic techniques, the identity and nature of the reward signal in V1, and consequently also test if the computation is local to V1. Using a combination of behavioral electrophysiology and computational techniques we will establish if there is a relationship between the observed dynamics in V1 and behavior and manipulate that relationship ontogenetically, and also account for the experimentally observed scalar property in time perception on the basis of the observed physiology and the computational model, all hallmarks of our model. The neural mechanism by which V1 - and more broadly, cortex - comes to express cue-reward intervals is unknown and is addressed in this study. Consequently, the results of this study will bear greatly on neural processes of memory and learning, forming a basis of understanding for cognitive dysfunction.
The proposal will address the neural mechanism by which the brain can learn the behavioral significance of environmental cues, specifically visual cues, and generate neural and behavioral temporal expectancies based on such visual evidence. These discoveries will provide new insight into our current understanding of reinforcement learning. Consequently, the results of this study will bear greatly on neural processes of memory, learning, and reward, forming a basis of understanding for cognitive dysfunction.
|Shouval, Harel Z; Agarwal, Animesh; Gavornik, Jeffrey P (2013) Scaling of perceptual errors can predict the shape of neural tuning curves. Phys Rev Lett 110:168102|