ABSTRACT
The ability to group stimuli into meaningful categories is a fundamental cognitive process. To explore its neural basis, we trained monkeys to categorize computer-generated stimuli as "cats" and "dogs." A morphing system was used to systematically vary stimulus shape and precisely define the category boundary. Neural activity in the lateral prefrontal cortex reflected the category of visual stimuli, even when a monkey was retrained with the stimuli assigned to new categories.
Subject(s)
Mental Processes/physiology , Neurons/physiology , Prefrontal Cortex/physiology , Animals , Brain Mapping , Cats , Cognition , Dogs , Form Perception , Haplorhini , Learning , Photic Stimulation , Temporal Lobe/physiologyABSTRACT
Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.
Subject(s)
Learning/physiology , Models, Neurological , Nerve Net/physiology , Neurons/physiology , Pattern Recognition, Visual/physiology , Temporal Lobe/physiology , Visual Cortex/physiology , Animals , Humans , Nerve Net/cytology , Neurons/cytology , Temporal Lobe/anatomy & histology , Visual Cortex/anatomy & histology , Visual Pathways/cytology , Visual Pathways/physiologyABSTRACT
Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.
Subject(s)
Computer Simulation , Form Perception/physiology , Mental Recall/physiology , Models, Neurological , Visual Cortex/physiology , Animals , Macaca , Neurons/physiology , Visual Cortex/cytology , Visual Fields/physiologySubject(s)
Cerebral Cortex/physiology , Models, Biological , Neurons/physiology , Visual Perception/physiology , Animals , HumansABSTRACT
We analyze the pattern formation behavior of a high&hyphendimensional self&hyphenorganizing map (SOM) model for the competitive projection of ON&hyphencenter&hyphentype and OFF&hyphencenter&hyphentype inputs to a common map layer. We mathematically show, and numerically confirm, that even isotropic stimuli can drive the development of oriented receptive fields and an orientation map in this model. This result provides an important missing link in the spectrum of pattern formation behaviors observed in SOM models. Extending the model by including further layers for binocular inputs, we also investigate the combined development of orientation and ocular dominance maps. A parameter region for combined patterns exists; corresponding maps show a preference for perpendicular intersection angles between iso&hyphenorientation lines and ocularity domain boundaries, consistent with experimental observations.
