We propose to study how information is represented across the network of cortical areas and the circuitry by which this information is encoded. Even the earliest levels of the cortical visual hierarchy perform integrative functions for contour integration, scene segmentation and analysis of shape. Moreover, the functional roles of cortical neurons in early visual cortex show changes with perceptual learning and are subject to powerful top-down influences. These influences include attention, expectation and perceptual task. As a consequence every cortical area acts as an adaptive processor, undergoing continuing cycles of state change and functional switching, with earlier stages influencing the way in which the bottom up sensory information is interpreted in subsequent states. In the current proposal we will extend our findings on the role of top-down influences in primary visual cortex (V1) to other areas in the visual hierarchy, notably V2 and V4. Using chronically implanted electrode arrays, we will compare how information relevant to a given perceptual task is encoded in different cortical areas during the course of perceptual learning. We will measure how different perceptual tasks alter interactions within and between cortical areas through measures of cross-correlation of spiking activity and coherence of local field potentials. These experiments are designed to test the hypothesis that the effective connectivity of intrinsic and reentrant cortical connections changes with perceptual task, and that through a process of input selection, neurons are capable of activating contextual influences that are relevant for performing the specific task at hand. This provides a general theory of cortical function, whereby horizontal cortical connections mediate an association field of potential interactions across the cortical sheet, and that at any given time subcomponents of this field can be selected for executing different operations.
The work outlined in this proposal will develop the idea that cortical areas are adaptive processors that run different computational operations as dictated by interactions between feedback and intrinsic connections. It will establish how information acquired during the course of perceptual learning is distributed across multiple cortical areas. It will provide a basis, at the level of cortical circuitry, for understanding how dysfunction of experience dependent change and top-down influences plays a role in visual disorders such as amblyopia and behavioral disorders in general.
| van Kerkoerle, Timo; Marik, Sally A; Meyer Zum Alten Borgloh, Stephan et al. (2018) Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex. Proc Natl Acad Sci U S A 115:10464-10469 |
| Abe, Hiroshi; McManus, Justin N J; Ramalingam, Nirmala et al. (2015) Adult cortical plasticity studied with chronically implanted electrode arrays. J Neurosci 35:2778-90 |
| Piëch, Valentin; Li, Wu; Reeke, George N et al. (2013) Network model of top-down influences on local gain and contextual interactions in visual cortex. Proc Natl Acad Sci U S A 110:E4108-17 |
| Ramalingam, Nirmala; McManus, Justin N J; Li, Wu et al. (2013) Top-down modulation of lateral interactions in visual cortex. J Neurosci 33:1773-89 |
| Gilbert, Charles D; Li, Wu (2013) Top-down influences on visual processing. Nat Rev Neurosci 14:350-63 |
| McManus, Justin N J; Li, Wu; Gilbert, Charles D (2011) Adaptive shape processing in primary visual cortex. Proc Natl Acad Sci U S A 108:9739-46 |
| Gölcü, Doruk; Gilbert, Charles D (2009) Perceptual learning of object shape. J Neurosci 29:13621-9 |
| Kinoshita, Masaharu; Gilbert, Charles D; Das, Aniruddha (2009) Optical imaging of contextual interactions in V1 of the behaving monkey. J Neurophysiol 102:1930-44 |
| Li, Wu; Piech, Valentin; Gilbert, Charles D (2008) Learning to link visual contours. Neuron 57:442-51 |
| McManus, Justin N J; Ullman, Shimon; Gilbert, Charles D (2008) A computational model of perceptual fill-in following retinal degeneration. J Neurophysiol 99:2086-100 |
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