@article {1892, title = {Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing}, journal = {Public Library of Science | PLoS ONE }, volume = {1(3): e0150980}, year = {2016}, month = {03/2016}, abstract = {

Faces are an important and unique class of visual stimuli, and have been of interest to neuroscientists\  for many years. Faces are known to elicit certain characteristic behavioral markers, collectively labeled {\textquotedblleft}holistic processing{\textquotedblright}, while non-face objects are not processed\  holistically. However, little is known about the underlying neural mechanisms. The main aim of this computational simulation work is to investigate the neural mechanisms that make
face processing holistic. Using a model of primate visual processing, we show that a single key factor, {\textquotedblleft}neural tuning size{\textquotedblright}, is able to account for three important markers of holistic face processing: the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole-Part Effect (WPE). Our proof-of-principle specifies the precise neurophysiological property that corresponds to the poorly-understood notion of holism, and shows that this one neural property controls three classic behavioral markers of holism. Our work is consistent with neurophysiological evidence, and makes further testable predictions. Overall, we provide a parsimonious account of holistic face processing, connecting computation, behavior and neurophysiology.

}, doi = {10.1371/journal.pone.0150980}, url = {http://journals.plos.org/plosone/article?id=10.1371\%2Fjournal.pone.0150980}, author = {Cheston Tan and Tomaso Poggio} } @article {1371, title = {Deep Convolutional Networks are Hierarchical Kernel Machines}, number = {035}, year = {2015}, month = {06/17/2015}, abstract = {

We extend i-theory to incorporate not only pooling but also rectifying nonlinearities in an extended HW module (eHW) designed for supervised learning. The two operations roughly correspond to invariance and selectivity, respectively. Under the assumption of normalized inputs, we show that appropriate linear combinations of rectifying nonlinearities are equivalent to radial kernels. If pooling is present an equivalent kernel also exist. Thus present-day DCNs (Deep Convolutional Networks) can be exactly equivalent to a hierarchy of kernel machines with pooling and non-pooling layers. Finally, we describe a conjecture for theoretically understanding hierarchies of such modules. A main consequence of the conjecture is that hierarchies of eHW modules minimize memory requirements while computing a selective and invariant representation.

}, author = {F. Anselmi and Lorenzo Rosasco and Cheston Tan and Tomaso Poggio} } @article {450, title = {Neural tuning size is a key factor underlying holistic face processing.}, number = {021}, year = {2014}, month = {06/2014}, abstract = {

Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person{\textquoteright}s life, and face recognition is crucial for daily social interaction. Faces are also unlike any other stimulus class in terms of certain physical stimulus characteristics. Furthermore, faces have been empirically found to elicit certain characteristic behavioral phenomena, which are widely held to be evidence of {\textquotedblleft}holistic{\textquotedblright} processing of faces. However, little is known about the neural mechanisms underlying such holistic face processing. In other words, for the processing of faces by the primate visual system, the input and output characteristics are relatively well known, but the internal neural computations are not. The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects. In this computational modeling work, we show that a single factor {\textendash} {\textquotedblleft}neural tuning size{\textquotedblright} {\textendash} is able to account for three key phenomena that are characteristic of face processing, namely the Composite Face Effect (CFE), Face Inversion Effect (FIE) and Whole - Part Effect (WPE). Our computational proof - of - principle provides specific neural tuning properties that correspond to the poorly - understood notion of holistic face processing, and connects these neural properties to psychophysical behavior. Overall, our work provides a unified and parsimonious theoretical account for the disparate empirical data on face - specific processing, deepening the fundamental understanding of face processing

}, keywords = {Theories for Intelligence}, author = {Cheston Tan and Tomaso Poggio} }