One crucial step in the construction of the human representation of the world is found at the boundary between two basic stimuli: visual experience and the sounds of language. In the developmental stage when the ability of recognizing objects consolidates, and that of segmenting streams of sounds into familiar chunks emerges, the mind gradually grasps the idea that utterances are related to the visible entities of the world. The model presented here is an attempt to reproduce this process, in its basic form, simulating the visual and auditory pathways, and a portion of the prefrontal cortex putatively responsible for more abstract representations of object classes. Simulations have been performed with the model, using a set of images of 100 real world objects seen from many different viewpoints and waveforms of labels of various classes of objects. Subsequently, categorization processes with and without language are also compared.
A binocular model for the prenatal development of the visual nervous
systém is proposed. The model is able to reproduce some properties observed in mammals at the moment of birth such as retinotopy, oriented receptive fields, and ocular dominance. One of the outstanding features of the model architecture is the existence of dendrodendritic interaction within each layer. The spontaneous activity of the neurons of the input layer is modeled by spatially and temporally decorrelated activity. The evolution of a connection depends on the output activity of both connected neurons. Hebbian learning has been used for the afferent excitatory connections and anti-Hebbian learning for the lateral inhibitory connections. The model is reduced to a set of ordinary differential equations obtained from a statistical treatment of the dynamics that avoids its explicit dependence on the spontaneous activity.
This tutorial summarizes the new approach to complex system theory that comes basically from physical information analogies. The information components and gates are defined in a similar way as components in electrical or mechanical engineering. Such approach enables the creation of complex networks through their serial, parallel or feedback ordering. Taking into account wave probabilistic functions in analogy with quantum physics, we can enrich the system theory with features such as entanglement. It is shown that such approach can explain emergencies and self-organization properties of complex systems.