When and How-Long: A Unified Approach for Time Perception

The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted gradually increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments. The present work highlights two basic capacities that are necessary for developing temporal cognition in artificial systems. In particular, the seamless integration of agents in the environment assumes they are able to consider when events have occurred and how long they have lasted. This information, although rather standard in humans, is largely missing from artificial cognitive systems. In the present work we consider how a time perception model that is based on neural networks and the Streatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory. The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events

Temporal Cognition: A Key Ingredient of Intelligent Systems

Experiencing the flow of time is an important capacity of biological systems that is involved in many ways in the daily activities of humans and animals. However, in the field of robotics, the key role of time in cognition is not adequately considered in contemporary research, with artificial agents focusing mainly on the spatial extent of sensory information, almost always neglecting its temporal dimension. This fact significantly obstructs the development of high-level robotic cognitive skills, as well as the autonomous and seamless operation of artificial agents in human environments. Taking inspiration from biological cognition, the present work puts forward time perception as a vital capacity of artificial intelligent systems and contemplates the research path for incorporating temporal cognition in the repertoire of robotic skills

Robotic Interval Timing based on Active Oscillations

AbstractInterval timing is crucially involved in many of the daily activities of humans and animals. However, the cognitive mechanisms enabling the encoding and processing of time in the brain remain largely unknown. In the present work, we follow a self- organized modeling approach to study unconventional representations of time in neural network based cognitive system. A particularly interesting feature of our study regards the implementation of a single computational model to accomplish two different robotic behavioral tasks, which assume diverse manipulation of time intervals. The examination of the implemented cognitive system revealed that it is possible to integrate the two main theoretical models of time representation existing today - the dedicated and intrinsic representations - into a new theory that effectively combines their key characteristics

Learning Feed-Forward and Recurrent Fuzzy Systems: A Genetic Approach

In this paper we present a new learning method for rule-based feed-forward and recurrent fuzzy systems. Recurrent fuzzy systems have hidden fuzzy variables and can approximate the temporal relation embedded in dynamic processes of unknown order. The learning method is universal i.e. it selects optimal width and position of Gaussian like membership functions and it selects a minimal set of fuzzy rules as well as the structure of the rules. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture low complexity and minimal rule base. Optimization of the "entropy" of a fuzzy rule base leads to a minimal number of rules, of membership functions and of sub-premises together with an optimal input/output behavior. Most of the resulting Fuzzy Systems are comparable to systems designed by an expert but offers a better performance. The approach is compared to others by a standard benchmark (a system identification process). Different results for feed-forward and first order recurrent Fuzzy Systems with symmetric and non-symmetric membership functions are presented. Key words: Fuzzy logic controller, recurrent fuzzy systems, genetic algorithm, entropy of fuzzy rule, machine learning, dynamic processes.

A hierarchical coevolutionary method to support brain-lesion modelling

Abstract — The current work addresses the development of cognitive abilities in artificial organisms, a topic that has attracted many research efforts recently. In our approach, neural networkbased agent structures are employed to represent distinct brain areas. We introduce a Hierarchical Collaborative CoEvolutionary (HCCE) approach to design autonomous, yet cooperating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The HCCE is appropriately designed to support systematic modelling of brain structures, able to reproduce biological lesion data. The proposed approach designs cooperating agents properly, by considering the desired pre- and post- lesion performance of the model. The effectiveness of the proposed approach is illustrated on the design of a computational model of Primary Motor cortex and Premotor cortex interactions in the mammalian brain. The model is successfully tested in driving a simulated robot, with different pre- and post- lesion performance. I

P.: Experiencing and processing time with neural networks

Abstract-The sense of time is directly involved in most of the daily activities of humans and animals. However, the cognitive mechanisms that support experiencing and processing time remain unknown, with the assumption of the clock-like tick accumulation dominating the field. The present work aims to explore whether temporal cognition may be developed without the use of clock-like mechanisms. We evolve ordinary neural network structures that (i) monitor the length of two time intervals, (ii) compare their durations and (iii) express different behaviors depending on whether the first or the second duration was larger. We study the mechanisms selforganized internally in the network and we compare them with leading hypothesis in brain science, showing that tickaccumulation may not be a prerequisite for experiencing and processing time