Alessio Plebe

A model of angle selectivity development in visual area V2

The role of area V2 in visual processing is still almost unexplored. Recently the selectivity of V2 neurons in the macaque to stimuli consisting of angled lines has been studied systematically, responses were definitely selective in one-fourth of neurons. In this work the emergence of a computational organization supporting similar responses is explored, using an artificial model of cortical maps. This model, called LISSOM (Laterally Interconnected Synergetically Self-Organizing Map), includes excitatory and inhibitory lateral connections. In this simulation two LISSOM maps are arranged as V1 and V2 areas, in the first area the classical domains of orientation selectivity will develop, while in V2 several neurons become sensitive to pairs of angled segments. The stimuli experiences favoring both developments are investigated.

Object recognition by artificial cortical maps

Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.

First Words Learning: A Cortical Model

Humans come to recognize an infinite variety of natural and man-made objects in their lifetime and make use of sounds to identify and categorize them. How does this lifelong learning process begin? Many hypotheses have been proposed to explain the learning of first words with some emerging from the particular characteristics observed in child development. One is the peculiar trend in the speed with which words are learned, which have been referred to in the literature as “fast mapping”. We present a neural network model trained in stages that parallel developmental ones and that simulates cortical processes of self-organization during an early crucial stage of first word learning. This is done by taking into account strictly visual and acoustic perceptions only. The results obtained show evidence of the emergence in the artificial maps used in the model, of cortical functions similar to those found in the biological correlates in the brain. Evidence of non-catastrophic fast mapping based on the quantity of objects and labels gradually learned by the model is also found. We interpret these results as meaning that early stages of first word learning may be explained by strictly perceptual learning processes, coupled with cortical processes of self-organization and of fast mapping. Specialized word-learning mechanisms thus need not be invoked, at least not at an early word-learning stage.

A model of the response of visual area V2 to combinations of orientations

The role of the V2 area in visual processing is still almost entirely unexplored. Recently, several studies revealed the tuning of V2 neurons in the macaque to stimuli consisting of two segments with different orientations. By measuring orientation tuning inside subunits of the overall receptive field, units with non uniform orientation selectivity have been found. In this work, the emergence of a computational organization supporting similar responses is explored, using an artificial model of cortical maps. This model, called LISSOM (Laterally Interconnected Synergetically Self-Organizing Map) includes excitatory and inhibitory lateral connections. In this simulation two LISSOM maps are arranged as V1 and V2 areas. In the first area, the classical domains of orientation selectivity develop, while in V2 most neurons become sensitive to pairs of orientations. The overall activation of these units depend on the presence of oriented segments at a finer grain than the whole receptive fields, with complex nonlinear interactions.

The emergence of visual object recognition

The model here proposed simulates the development of the object recognition capability, assuming that recognition does not imply any sort of explicit geometrical reconstruction and emerges as result of interactions between epigenetic in.uences and basic neural plasticity mechanisms. The model is a hierarchy of arti.cial neural maps, mainly based on the LISSOM architecture, achieving self-organization through simulated intercortical lateral connections.

A neural model of human object recognition development

The human capability of recognizing objects visually is here held to be a function emerging as result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model here proposed simulates the development of the main neural processes of the visual system giving rise to the higher function of recognizing objects. It is a hierarchy of artificial neural maps, mainly based on the LISSOM architecture, achieving self-organization through simulated intercortical lateral connections.

A BIOLOGICALLY INSPIRED NEURAL MODEL OF VISION-LANGUAGE INTEGRATION

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 recog- nizing 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 repro- duce this process, in its basic form, simulating the visual and auditory pathways, and a portion of the prefrontal cortex putatively responsible for more abstract rep- resentations of object classes. Simulations have been performed with the model, using a set of images of 100 real world objects seen from many difierent viewpoints and waveforms of labels of various classes of objects. Subsequently, categorization processes with and without language are also compared.

A Neural Model of Moral Decisions

In this paper a neural model of moral decisions is proposed. It is based on the fact, supported by neuroimaging studies as well as theoretical analysis, that moral behavior is supported by brain circuits engaged more generally in emotional responses and in decision making. The model has two components, the first is composed by artificial counterpart of the orbitofrontal cortex, connected with sensorial cortical sheets and with the ventral striatum, the second by the ventromedial prefrontal cortex, that evaluate representations of values from the orbitofrontal cortex, comparing with negative values, encoded in the amygdala. The model is embedded in a simple environmental context, in which it learns that certain actions, although potentially rewarding, are morally forbidden.

In Learning Nouns and Adjectives Remembering Matters: A Cortical Model

The approach used and discussed here is one that simulates early lexical acquisition from a neural point of view. We use a hierarchy of artificial cortical maps that builds and develops models of artificial learners that are subsequently trained to recognize objects, their names, and then the adjectives pertaining to their color. Results of the model can explain what has emerged in a series of developmental research studies in early language acquisition, and can account for the different developmental patterns followed by children in acquiring nouns and adjectives, by perceptually driven associational learning processes at the synaptic level.

Artificial learners of objects and names

Naming requires recognition. Recognition requires the ability to categorize objects and events. What mechanisms in the brain underlie the unfolding of these capacities? In this article, we describe a neural network model in which artificially created individuals are exposed to visual stimuli and vocal sounds and are tested in experiments like human children. The model simulates, in a biologically plausible way, the process by which infants learn how to recognize objects and words through experience.