Affiliations: [a]
Polish Japanese Academy of Information Technology, Koszykowa, Warsaw, Poland
| [b] Department Neurology, UMass Medical School, Worcester, MA, USA
Correspondence:
[*]
Corresponding author. Andrzej W. Przybyszewski. E-mail: [email protected].
Abstract: Object recognition is a complex neuronal process determined by interactions between many visual areas: from the retina, thalamus to the ventral visual pathway. These structures transform variable, single pixel signal in photoreceptors to a stable object representation. Neurons in visual area V4, midway in ventral stream, represent such stable shape detector. A feed forward hierarchy of increasing in size and complexity receptive fields (RF) leads to grand mother cell concept. Our question is how these processes might identify an object or its elements in order to recognize it in new, unseen conditions? We propose a new approach to this problem by extending the classical definition of the RF to a fuzzy detector. RF properties are also determined by the computational properties of the bottom-up and top-down pathways comparing stimulus with many predictions. The “driver-type”.ogic (DTL) of bottom-up computations looks for large number of possible object parts (hypotheses –.ough set (RS) upper approximation), as object’s elements are similar to RF properties. The optimal combination is chosen, in unsupervised, parallel, multi-hierarchical pathways by the “modulator-type”.ogic (MTL) of top-down computations (RS lower approximation). Interactions between DTL (hypotheses) and MTL (predictions) terminates when RS boundary became small - the object is recognized.
