A neural model of quantity discrimination.
A neural network model is proposed with the ability to extract abstract numerical representation from visual input. It simulates properties of a number detection system which is hypothesized to underlie simple language-independent numerical abilities. The network has three layers where the first layer computes the sum of the nearest neighbour inputs. The first layer is also augmented with multiplicative gating and gradient tonic activation which prevents interference. The second layer implements local lateral inhibition which enables a single node to represent a single object. The third layer exhibits number-tuning similar to recently described responses of neurons in the prefrontal cortex. Computer simulations showed that network response does not depend on visual attributes like the object's size, position or shape. The model is based on several biophysical mechanisms such as multiplicative interaction on dendrites, independent processing on different dendritic branches and disinhibition by glutamate spill-over on kainate receptors on inhibitory axons.
Domijan
Drazen
D
Department of Psychology, Faculty of Philosophy, University of Rijeka, I. Klobucarica 1, HR-51000 Rijeka, Croatia. ddomijan@human.pefri.hrA
D000818:Animals; D003198:Computer Simulation; D004192:Discrimination, Psychological / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D017397:Prefrontal Cortex / Q000166:cytology*; D014796:Visual Perception / Q000502:physiology*