The goal of this research is to discover the cortical computations embodied in the responses of neurons in ventral visual area V2. Since V2 is heavily dependent on input from V1 for its visual responsiveness, we will develop a two-stage model, in which responses are constructed from a suitable combination of V1 afferents, with the design of each stage following a common canonical form. This model is intended to account for the visual response properties of neurons as economically as possible, while not necessarily reflecting the details of neuronal circuitry. This simplicity is deliberate, as it will allow the mdel to be fit to data recorded from single neurons, and, when assembled into a population, to predict perceptual capabilities. The motivation for the structure of the model, and our confidence in its success, comes from the convergence of three strands of previous work: (1) we have developed, fit, and validated a similar two-stage model for neuronal responses in area MT, a dorsal stream area which also receives primary afferent drive from area V1; (2) we have developed a two-stage model for visual texture representation that captures perceptually recognizable structures of natural images using spatial integration regions matched in size to those of V2 cells. We've shown that images synthesized to have matching model responses are indistinguishable to human observers; and (3) we've obtained preliminary data indicating that most of V2 cells respond more vigorously to synthetic texture stimuli than to spectrally matched noise stimuli, whereas V1 cells do not. The research is divided into three parts. First, we will gather electrophysiological data to dissect those model-generated features that underlie the increased responsiveness of V2 to texture stimuli. We will, in parallel, gather evidence for the increased responsiveness using fMRI, which will allow us to compare simultaneously measured responses averaged over neural populations in V1 and V2. Second, we will develop a physiologically plausible instantiation of the texture model and develop the methods to fit it to data from single neurons. Finally, we will link the novel functional response properties we have discovered to perception by simultaneously measuring neuronal responses and perceptual judgments in awake behaving macaques. To explore sensitivity to naturalistic features, we will relate psychometric and single-neuron neurometric functions, and use choice probability to link responses to behavioral performance.
Disorders of the visual nervous system are a major cause of visual disability: Damage to the primary visual cortex (area V1) causes a loss of vision over part or all of the visual field, although parts of the visual cortex outside V1 may, in some cases, be able to substitute for some lost functions after V1 damage. Other disorders affecting these same areas outside V1 are associated with specific forms of visual loss such as akinetopsia (motion blindness) and agnosia (form blindness). Our research seeks to understand the organization and function of V2, the first and largest cortical visual area beyond area V1, both because of its potential as a substrate for visual loss and recovery after brain damage, and because it is a key part of the processing by which sensory signals are transformed to form decisions, guide actions, and create enduring memories of evanescent events.
|Ziemba, Corey M; Freeman, Jeremy; Simoncelli, Eero P et al. (2018) Contextual modulation of sensitivity to naturalistic image structure in macaque V2. J Neurophysiol 120:409-420|
|Goris, Robbe L T; Ziemba, Corey M; Movshon, J Anthony et al. (2018) Slow gain fluctuations limit benefits of temporal integration in visual cortex. J Vis 18:8|
|Goris, Robbe L T; Ziemba, Corey M; Stine, Gabriel M et al. (2017) Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex. J Neurosci 37:5195-5203|
|Hallum, Luke E; Shooner, Christopher; Kumbhani, Romesh D et al. (2017) Altered Balance of Receptive Field Excitation and Suppression in Visual Cortex of Amblyopic Macaque Monkeys. J Neurosci 37:8216-8226|
|Shooner, Christopher; Hallum, Luke E; Kumbhani, Romesh D et al. (2017) Asymmetric Dichoptic Masking in Visual Cortex of Amblyopic Macaque Monkeys. J Neurosci 37:8734-8741|
|Ziemba, Corey M; Freeman, Jeremy; Movshon, J Anthony et al. (2016) Selectivity and tolerance for visual texture in macaque V2. Proc Natl Acad Sci U S A 113:E3140-9|
|Shooner, Christopher; Hallum, Luke E; Kumbhani, Romesh D et al. (2015) Population representation of visual information in areas V1 and V2 of amblyopic macaques. Vision Res 114:56-67|
|Goris, Robbe L T; Simoncelli, Eero P; Movshon, J Anthony (2015) Origin and Function of Tuning Diversity in Macaque Visual Cortex. Neuron 88:819-31|
|Vintch, Brett; Movshon, J Anthony; Simoncelli, Eero P (2015) A Convolutional Subunit Model for Neuronal Responses in Macaque V1. J Neurosci 35:14829-41|
|Goris, Robbe L T; Movshon, J Anthony; Simoncelli, Eero P (2014) Partitioning neuronal variability. Nat Neurosci 17:858-65|
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