Integrated Intrinsic and Dedicated Representations of Time: A Computational Study Involving Robotic Agents

Abstract

The computational modeling of cognitive processes provides a systematic means to study hidden and particularly complex aspects of brain functionality. Given our rather limited understanding of how the brain deals with the notion of time, the implementation of computational models addressing duration processing can be particularly informative for studying possible time representations in our brain. In the present work we adopt a connectionist modeling approach to study how time experiencing and time processing may be encoded in a simple neural network trained to accomplish time-based robotic tasks. A particularly interesting characteristic of the present study is the implementation of a single computational model to accomplish not only one but three different behavioral tasks that assume diverse manipulation of time intervals. This setup enables a multifaceted exploration of duration-processing mechanisms, revealing a rather plausible hypothesis of how our brain deals with time. The model is implemented through an evolutionary design procedure, making a very limited set of a priori assumptions regarding its internal structure and machinery. Artificial evolution facilitates the unconstrained self-organization of time representation and processing mechanisms in the brain of simulated robotic agents. Careful examination of the artificial brains has shown that the implemented mechanisms incorporate characteristics from both the ‘intrinsic’ time representation scheme and the ‘dedicated’ time representation scheme. Even though these two schemes are widely considered as contradictory, the present study shows that it is possible to effectively integrate them in the same cognitive system. This provides a new view on the possible representation of time in the brain, and paves the way for new and more comprehensive theories to address interval timing.

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

“…The neuro-evolutionary framework considered in the present study provides increased flexibility in designing the internal mechanisms of the model, accomplishing to easily bridge oscillatory input and ramping activity in a single model. While the two mechanisms have been frequently considered contradictory in the literature, the use of oscillations with gradually adapted characteristics provides the basis for implementing effective interval timing mechanisms (Simen et al, 2013 ) and has been used for accomplishing multiple interval timing tasks (Maniadakis and Trahanias, 2015 ).…”

“…We employ the coevolutionary neural network framework that has been described in detail in (Maniadakis and Trahanias, 2008 , 2009 ) to develop a modular neural network system for interval timing. In the past, we have used the same technology to develop cognitive models for artificial agents, which have been capable of time-informed behavior switching (Maniadakis et al, 2009 ) and multi-context duration processing (Maniadakis and Trahanias, 2015 ).…”

“…Thus, rather than using a time-dedicated neural circuit, time coexists with the representation and processing of other external stimuli. Recent models combine intrinsic and dedicated representations into active oscillations that do not only produce “ticks” but additionally adjust their characteristics to perceive, measure, and process time in order to facilitate the accomplishment of a variety of temporal tasks (Maniadakis and Trahanias, 2015 ). Similarly, models assuming oscillations with adaptive pulse rates extent the classic pacemaker-accumulator model to accomplish timescale invariance in interval timing (Simen et al, 2013 ).…”

Time-Aware Multi-Agent Symbiosis

“…In example, robotic agents face difficulties in distinguishing between the entities involved in different past events or implement reasoning on past event sequencing, cannot feel rush or adapt to human temporal expectations and cannot effectively plan not only how, but also when tasks should be accomplished (Wilcox et al, 2012 ). Our recent work has addressed artificial temporal cognition, with a focus on human-like time representations and duration processing mechanisms for robots (Maniadakis et al, 2009 , 2011 ; Maniadakis and Trahanias, 2012 , 2015 ).…”

Hierarchical neural network model with intrinsic timing

“…However, timing within the brain itself is not well understood to date [2], but is of capital importance in controlling the signalling between neurons, which is key in neural functioning [3]. Many scientists believe that timing in the brain lies within the neurons themselves [4] whereby the spikes not only carry information but would also provide the necessary triggers for the brain's "clocking mechanism".…”