The central focus of our Center is to develop a framework for studying the neural computations underlying object recognition and visual attention in visual cortex. The Center's framework is based on the collaboration of labs working on monkey physiology, cat physiology and human psychophysics with a quantitative computational theory providing the main conduit through which experimental results in one lab affect experiments in another lab. The model that flows from the theory provides a novel way to drive a collaborative enterprise, providing a way to integrate the data, to check their consistency, to suggest new experiments and to interpret the results. The theory itself, based on two existing models for recognition and attentional saliency, will not only guide the experiments and drive synergies between different labs but will also evolve as an effect of the experimental results. The research is organized into three main projects, defined by geographical location and scientific questions, rather than discipline. In the MIT project, the labs of Tomaso Poggio, Earl Miller and James DiCarlo will be guided by a quantitative hierarchical model of recognition, probing the relations between identification and categorization and the properties of selectivity and invariance of recognition, especially with natural image clutter, in IT and PFC cortex of behaving macaque monkeys. In the Northwestern project, the lab of David Ferster will test a key prediction of the model about the nature of the pooling operation (a max operation vs. a linear sum) performed by complex cells in area 17, using very similar stimuli affected by clutter. The experiments will be done in the anesthetized cat, intracellularly, to allow for a characterization of the underlying circuit and biophysical mechanisms. In the Caltech project, the lab of Christof Koch will collaborate with Ferster lab on biophysical simulations of Vl circuits. It will also test -- using human psychophysics with stimuli configurations similar to the ones used by Jim DiCarlo -- the conditions under which attention is needed in recognition of natural objects and scenes; from the data, in collaboration with Tomaso Poggio, Koch's lab will extend the basic model of recognition by integrating it with the existing model of bottom-up saliency. A unique aspect of our Center is that, the computational component, centered around a quantitative theory of recognition, is the generic tool that drives interactions between the investigators, in addition to the standard pairwise interactions: the model suggests experiments and guides their planning and interpretation; the experimental results from one lab impact, through the model, work done in another lab, including model development, as well as their interpretation and what to do next. Ultimately, the whole process should lead to a better and more coherent understanding of the neural mechanisms of visual recognition.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory Grants (P20)
Project #
5P20MH066239-02
Application #
6805105
Study Section
Special Emphasis Panel (ZMH1-NRB-Q (05))
Program Officer
Nadler, Laurie S
Project Start
2003-09-30
Project End
2007-07-31
Budget Start
2004-08-01
Budget End
2005-07-31
Support Year
2
Fiscal Year
2004
Total Cost
$601,144
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Other Basic Sciences
Type
Other Domestic Higher Education
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
Baldassi, Carlo; Alemi-Neissi, Alireza; Pagan, Marino et al. (2013) Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLoS Comput Biol 9:e1003167
Glezer, Laurie S; Jiang, Xiong; Riesenhuber, Maximilian (2009) Evidence for highly selective neuronal tuning to whole words in the ""visual word form area"". Neuron 62:199-204
Riesenhuber, Maximilian; Wolff, Brian S (2009) Task effects, performance levels, features, configurations, and holistic face processing: a reply to Rossion. Acta Psychol (Amst) 132:286-92
Kouh, Minjoon; Poggio, Tomaso (2008) A canonical neural circuit for cortical nonlinear operations. Neural Comput 20:1427-51
Einhauser, Wolfgang; Koch, Christof; Makeig, Scott (2007) The duration of the attentional blink in natural scenes depends on stimulus category. Vision Res 47:597-607
Jiang, Xiong; Bradley, Evan; Rini, Regina A et al. (2007) Categorization training results in shape- and category-selective human neural plasticity. Neuron 53:891-903
Finn, Ian M; Priebe, Nicholas J; Ferster, David (2007) The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. Neuron 54:137-52
Finn, Ian M; Ferster, David (2007) Computational diversity in complex cells of cat primary visual cortex. J Neurosci 27:9638-48
Serre, Thomas; Oliva, Aude; Poggio, Tomaso (2007) A feedforward architecture accounts for rapid categorization. Proc Natl Acad Sci U S A 104:6424-9
Cadieu, Charles; Kouh, Minjoon; Pasupathy, Anitha et al. (2007) A model of V4 shape selectivity and invariance. J Neurophysiol 98:1733-50

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