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NEWS_

03.2012_ Invited talk at Brooklyn College

01.2012_ My paper has been accepted by PLoS Computational Biology

11.2011_ Invited talk at SFN Minisymposium on "Context-dependent Neural Representation of Value: Gain Control, Adaptation, and Efficient Coding".

10.2011_ Invited talk at Thinking through drawing. Columbia University, NYC.

12.2009_ Invited talk at Bridge The Gap - Storefront for Art and Architecture, NYC

03.2009_ I co-organized a workshop at Cosyne 2009, on 'The role of spatial context in biological and computational vision'

02.2009_ I created the cover image for Journal of Neuroscience, Vol. 28, issue 48 with Processing

Our senses are piano keys upon which surrounding nature plays, and which often play upon themselves.
Denis Diderot, Conversation between D'Alembert and Diderot (1769)

GENERATIVE MODELING OF NATURAL SCENE STATISTICS AND SPATIAL CONTEXT EFFECTS

We address spatial contextual effects in early vision from the point of view of a well-found generative model of natural scene statistics, known as the Gaussian Scale Mixture (GSM) model, which can capture the statistical coordination amongst linear oriented filters, and is closely related to descriptive models of cortical nonolinearities based on divisive gain control. We introduced a mixture variant of the GSM to address the issue of which surround locations are statistically coordinated with a given center location, and therefore subject to a common divisive gain control. We also addressed how surround filters that are in the gain pool, can have either suppressive or facilitatory influence, depending on their covariance with the center filter. We have shown that the model can reproduce a range of existing data on surround modulation in visual cortical neurons in response to composite gratings, as well as perceptual effects such related to saliency pop-out and grouping [1,2]. We are currently testing detailed predictions of the model on neural responses to natural images and on the contrast dependence of surround tuning and asymmetry in the primary visual cortex of primates [3].

[1] R.C.C., P. Dayan, O. Schwartz (2012) Cortical Surround Interactions and Perceptual Salience Via Natural Scene Statistics. PLoS Computational Biology, in press.

[2] R.C.C., P. Dayan, O. Schwartz (2009) Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing. Advances in Neural Information Processing Systems, 22. Preprint (480K, pdf)

[3] R.C.C., A. Kohn, O. Schwartz (2011) Testing a Principled Statistical Models of Spatial Contextual Interactions in V1. COSYNE 2011.

PROBING THE EARLY VISUAL SYSTEM WITH SYNTHETIC, NATURALISTIC IMAGES

We are developing a novel class of experimental stimuli for studying the physiology of the early visual system, synthetic images that have the statistical richness of natural images while still allowing full parametric manipulation.

[1] R.C.C., S. Wissig, A. Kohn, O. Schwartz (2009) Probing the early visual system with naturalistic, synthetic images. Cosyne 2009. (abstract) (poster .JPG)

SENSORIMOTOR COORDINATION IN DRAWING - A BAYESIAN ACCOUNT

I received the PhD degree in Physics in december 2007 from the University of Napoli, Italy. My thesis focused on analyzing visual creativity from the vantage point of computational Cognitive Science. Based on the idea that creative processes are the result of specific sensorimotor skills, my doctoral research exploited eye tracking experiments and Bayesian techniques to characterize and model visuomotor coordination in the activity of drawing [1,2], and to develop an artificial agent with such capabilities [3].

[1] R.C.C., P. Coraggio, P. Napoletano, O. Schwartz, M. Ferraro, G. Boccignone (2009) Visuomotor Characterization of Eye Movements in a Drawing Task. Vision Research 49, 810-818, 2009. (.PDF)
[2] R.C.C., P. Napoletano, P. Coraggio, G. Boccignone (2008) What the Draughtsman's Hand Tells the Draughtsman's Eye: A Sensorimotor Account of Drawing. International Journal of Pattern Recognition and Artificial Intelligence, IJPRAI 22(5): 1015-1029. (.PDF)
[3] R.C.C., P. Coraggio, P. Napoletano, G. Boccignone, A.De Santis (2008) Sensorimotor coupling via Dynamic Bayesian Networks. Proceedings of the IEEE International Conference on Robotics and Automation 2008, ICRA. (.PDF)