The visual brain infers a three-dimensional world from twodimensional images and organizes the visual information in terms of objects in three-dimensional space, representing even objects that are partially occluded and appear fragmented in the retinal image. This organization is the basis for attentive selection, action planning and object recognition. A combination of experimental and theoretical studies together with model implementations in neuromorphic hardware will be used to elucidate the interface between visual feature representations and attentive cognitive processes. Previous findings on the neural coding of figure-ground structure can be understood in terms of grouping mechanisms that structure the incoming sensory information as proto-objects (objects as defined by the system at this stage). The grouping mechanisms also provide handles for top-down mechanisms to address and select object-related information. The proposed work will explain how neuronal circuitry organizes spatially disconnected visual features into perceptual objects. How is this implemented neurally to lead to a coherent representation? Detailed computational models of the underlying circuitry will be developed, both as standard numerical simulations and in fast, neuromorphic hardware, and then tested by multiple single-cell recordings in awake non-human primates. Specifically, while prior studies examined spike time correlations indiscriminately in all neurons, our recent studies differentiated neurons according to their role in the grouping circuits. The grouping hypothesis predicts elevated synchrony only in pairs of neurons that belong to the same grouping circuit, but not in other pairs. These model predictions were confirmed in a recent study which showed that spike-spike correlation functions are in qualitative agreement with the idea that perceptual grouping is implemented by feedback from populations of dedicated grouping cells. Quantitative understanding requires the development of explicit spiking models, which is one of the main foci of this proposal. Models will be implemented on neuromorphic spiking hardware since the complexity of the cortical circuitry makes realistic model simulations on CPU/GPU system impossible. Predictions of integrate-and-fire type models of this circuitry will be compared with rate and synchrony observed in our recordings and deviates used to fine-tune the models. We will pursue the educational and broader impacts aims on five fronts. 1) Students will be crosstrained and mentored in biological, mathematical and engineering sciences, which will lead to graduates with unique skill sets. 2) We will contribute to the development of the nascent neuromorphic engineering field, providing new research problems that can benefit from the crosstraining and collaboration. We plan to participate in the NSF sponsored Telluride Neuromorphic Cognition Engineering and Capo Caccia Neuromorphic Cognitive Engineering Workshops for this purpose. 3)We will provide an opportunity for undergraduate students to participate in the research as part of our Site REU (managed by one of the PIs). They are trained in communications, research ethics and project management, which are crucial for success in todays biotechnology and bioscience work and market place. 4) We currently host students from local high-schools who conduct STEM research practicum rotations in our labs. This project will provide a perfect venue for the rotators to get exposed and mentored on multi-disciplinary research problems. We will use a tiered mentoring structure, where undergraduates mentor K-12 rotators, graduate students mentor undergraduates, and faculty members mentor all participants. 5) Our student recruitment plans will build on our current partnerships with MARC, LSAMP, McNair, SWE, SHPE and other similar programs and minority-serving institutions and local community colleges, to help develop a pipeline of qualified, diverse individuals who will contribute to the workforce in the area of STEM.
| Ko, Hee-Kyoung; von der Heydt, Rüdiger (2018) Figure-ground organization in the visual cortex: does meaning matter? J Neurophysiol 119:160-176 |
| Hu, Brian; Niebur, Ernst (2017) A recurrent neural model for proto-object based contour integration and figure-ground segregation. J Comput Neurosci 43:227-242 |
