Catálogo de publicaciones - libros

Taking neuromodulation as a mechanism underlying emotions, this paper investigates how such a mechanism can bias an artificial neural network towards exploration of new courses of action, as seems to be the case in positive emotions, or exploitation of known possibilities, as in negative emotions such as predatory fear. We use neural networks of spiking leaky integrate-and-fire neurons acting as minimal disturbance systems, and test them with continuous actions. The networks have to balance the activations of all their output neurons concurrently. We have found that having the middle layer modulate the output layer helps balance the activations of the output neurons. A second discovery is that when the network is modulated in this way, it performs better at tasks requiring the exploitation of actions that are found to be rewarding. This is complementary to previous findings where having the input layer modulate the middle layer biases the network towards exploration of alternative actions. We conclude that a network can be biased towards either exploration of exploitation depending on which layers are being modulated.

We discuss evidence for the existence of mirror systems in the brain, including recent experimental results that demonstrate the use of shared pathways for the observation and execution of reaching and grasping actions. We then describe a brain based model of observational learning that explains the similarities and differences in levels of activation of brain regions during observation and execution of actions. We simulate a very simple paradigm whereby an actor performs an action which is observed and then repeated by the simulated animal. We discuss the implications and possible extensions of our model.

It is well known that our prior knowledge and experiences affect how we learn new concepts. Although several formal modeling attempts have been made to quantitatively describe the mechanisms about how prior knowledge influences concept learning behaviors, the underlying cognitive mechanisms that give rise to the prior knowledge effects remains unclear. In this paper, we introduce a computational cognitive modeling framework that is intended to describe how prior knowledge and experiences influence learning behaviors. In particular, we assume that it is not simply the prior knowledge stored in our memory trace influencing our behaviors, but it is also the learning strategies acquired through previous learning experiences that affect our learning behaviors. Two simulation studies were conducted and the results showed promising outcomes.

The paper discusses a novel model for concept learning and representation. Two levels of representation are used: exemplars and (generalized concepts) prototypes. The internal structure of the model is based on a semantic network using spreading activation. Categorisation and addition operations are supported in parallel. Forgetting of learned concepts is used in order to track dynamic and novel environments. The model is inspired by the corresponding psychological theories of exemplars and prototypes. Simulation results support the formulation of the model.

We present a model of a recurrent neural network, embodied in a minimalist articulated agent with a single link and joint. The configuration of the agent defined by one angle (degree of freedom), is determined by the activation state of the neural network. This is done by contracting a muscle with many muscular fibers, whose contraction state needs to be coordinated to generate high amplitude link displacements. In networks without homeostasic (self-regulatory) mechanism the neural state dynamics and the configuration state dynamics converges to a fixed point. Introduction of random noise, shows that fixed points are meta-stable. When neural units are endowed with homeostasic mechanisms in the form of threshold adjustment, the dynamics of the configuration angle and neural state becomes aperiodic. Learning mechanisms foster functional and structural cluster formation, and modifies the distribution of the kinetic energy of the network. We also present a meta-model of embodied neural agents, that identifies self-perturbation as a mechanism for neural development without a teacher.

Language skills are dominantly implemented in one hemisphere (usually the left), with the pre-frontal areas playing a critical part (the inferior frontal area of Broca and the superior temporal area of Wernicke), but a network of additional regions in the brain, including some from the non-dominant hemisphere, are necessary for complete language functionality. This paper presents a neural architecture built on spiking neurons which implements a mechanism of associating representations of concepts in different modalities; as well as integrating sequential language input into a coherent representation/interpretation of an instruction. It follows the paradigm of temporal binding, namely synchronisation and phase locking of distributed representations in nested gamma-theta oscillations. The functionality of the architecture is presented in a set of experiments of language instructions given to a real robot.

Neurocognitive processes responsible for representation of meaning and understanding of words are investigated. First a review of current knowledge about word representation, recent experiments linking it to associative memory and to right hemisphere synchronous activity is presented. Various conjectures on how meaning arises and how reasoning and problem solving is done are presented. These inspirations are used to make systematic approximation to spreading activation in semantic memory networks. Using hierarchical ontologies representations of short texts are enhanced and it is shown that high-dimensional vector models may be treated as a snapshot approximation of the neural activity. Clustering short medical texts into different categories is greatly enhanced by this process, thus facilitating understanding of the text.

The purpose of this study is to construct a computational model of the metaphor understanding process. This study assumes that metaphor understanding consists of two processes. The first is a categorization process; a target is assigned to an ad hoc category of which the vehicle is a prototypical member. The second is a dynamic interaction process; the target assigned to the ad hoc category is influenced by dynamic interaction among features. Feature emergence is extracted through this dynamic interaction. In this study, a model of metaphor understanding is constructed based on this assumption by applying a statistical analysis of large-scale corpus. Further a psychological experiment is conducted in order to verify the psychological validity of the constructed model of metaphor understanding. Reflecting the fact that the constructed model represents more appropriate features of a metaphor than a model incorporating only the categorization process, the experimental results support its validity.

We develop a model of a set of novelty and familiarity detectors in the hippocampus which possess unique properties, and have been recently reported in [1]. The model uses both inhibition and disinhibition, together with a suitable output function of prefrontal object representations, to create the separate novelty and familiarity detectors with the observed properties. We conclude the paper with a discussion of the relation of this novelty system with that presented by numerous other techniques.