Title :
A biologically plausible model for same/different discrimination
Author :
Rey, Hernán G. ; Gutnisky, Diego ; Zanutto, B. Silvano
Author_Institution :
CONICET, Univ. of Buenos Aires, Buenos Aires, Argentina
fDate :
Aug. 31 2010-Sept. 4 2010
Abstract :
Abstract rules can be learned by several species (not only humans). We propose a biologically plausible model for same/different discrimination, that can point towards the neural basis of abstract concept learning. By including a neural adaptation mechanism to a discriminator model formerly introduced in the literature, selective clusters of neurons fire depending on whether or not the stimuli compared are the same or not. These selective neurons are consistent with experimental findings in the literature. Moreover, reward and attention can modulate the relative strength of each attribute/feature of the stimulus, so more complex abstract discriminations can be achieved using the proposed model as a building block. As a formal model, it can be easily incorporated into several applications in robotics and intelligent machines.
Keywords :
artificial intelligence; learning (artificial intelligence); medical robotics; neurophysiology; abstract concept learning; biologically plausible model; complex abstract discriminations; intelligent machines; neural adaptation mechanism; neuron fire; robotics; Adaptation model; Biological system modeling; Brain modeling; Delay; Neurons; Robots; Animals; Models, Biological; Neurons;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
Print_ISBN :
978-1-4244-4123-5
DOI :
10.1109/IEMBS.2010.5627349