Learning and memory in machines and animals : an AI model that accounts for some neurobiological data

The CEL model of learning and memory (Components of Episodic Learning) [Granger 1982, 1983a, 1983b] provides a process model of certain aspects of learning and memory in animals and humans. The model consists of a set of asynchronous and semi-independent functional operators that collectively create and modify memory traces as a result of experience. The model conforms to relevant results in the learning literature of psychology and neurobiology. There are two goals to this work: one is to create a set of working learning systems that will improve their performance on the basis of experience, and the other is to compare these systems' performance with that of living systems, as a step towards the eventual comparative characterizations of different learning systems.Parts of the model have been implemented in the CEL-0 program, which operates in a 'Maze-World' simulated maze environment. The program exhibits simple exploratory behavior that leads to the acquisition of predictive and discriminatory schemata. A number of interesting theoretical predictions have arisen in part from observation of the operation of the program, some of which are currently being tested in neurobiological experiments. In particular, some neurobiological evidence for the existence of multiple, seperable memory systems in humans and animals is interpreted in terms of the model, and some new experiments are suggested arising from the model's predictions

Elements of latent learning in a maze environment

A general purpose learning program is described which demonstrates a latent learning ability by operating at two separate goal pursuit levels. At one level are the constant, implicit goals associated with the system's memory management mechanisms. At the higher level are the dynamic, explicit behavioral goals which the implicit goals enable by manipulating memory representations to conform to the external surroundings. The program is shown to negotiate a simulated maze environment by the step-wise refinement of its latently learned experiences

What should a robot learn from an infant? Mechanisms of action interpretation and observational learning in infancy

The paper provides a summary of our recent research on preverbal infants (using violation-of-expectation and observational learning paradigms) demonstrating that one-year-olds interpret and draw systematic inferences about other’s goal-directed actions, and can rely on such inferences when imitating other’s actions or emulating their goals. To account for these findings it is proposed that one-year-olds apply a non-mentalistic action interpretational system, the ’teleological stance’ that represents actions by relating relevant aspects of reality (action, goal-state, and situational constraints) through the principle of rational action, which assumes that actions function to realize goal-states by the most efficient means available in the actor’s situation. The relevance of these research findings and the proposed theoretical model for how to realize the goal of epigenetic robotics of building a ’socially relevant’ humanoid robot is discussed

LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature

The Versatile Wayfinder: Prefrontal Contributions to Spatial Navigation

Highlights: Navigation is a behavior fundamental to all mobile animals, and incorporates various cognitive functions, including memory, planning, decision-making, and updating models of the world. Historically, the neural underpinnings of flexible navigation have focused on the hippocampal formation, but recent evidence suggests that regions of the prefrontal cortex (PFC) are crucial to many aspects of navigation, especially when environments are complex or dynamic. This review summarizes what we know from recent human, non-human primate, and rodent studies, proposing a novel perspective that incorporates our knowledge across species and brain regions seeking to avoid tunnel vision in understanding the multifaceted behavior in navigation

Understanding creativity

We have never seen creativity. More precisely, we have never seen the creative process; what we have seen is the creative individual (ex ante) and the outcome of creativity (ex post). Therefore we try to understand creativity by examining creative individuals and their creations. In this paper we only consider the creation of new knowledge. We draw on a wide variety of backgrounds. We wander into the area of cognitive psychology to investigate who is talented for creativity. We also draw on arts, history and philosophy of science, stories of mystics, some great novels and essays we have read as well as our experience in both working with creatives and creating new knowledge. Based on this shaky foundation we will describe creativity as illumination, through jokes, as a quest for harmony, as being kissed by the muse