Evolving neural models of path integration

Vickerstaff, Robert J.; Paolo, Ezequiel A. Di

doi:10.1242/jeb.01772

Cited by 60 publications

(65 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This appears to be similar to observed characteristics of search behavior in desert ants (Wehner & Srinivasan, 1981) and to previous models of mixed homing-search strategies (Vickerstaff & Di Paolo, 2005;, but in a more parsimonious account (without an explicit parameter to control the spread of the search strategy). In other words, in the probabilistic model, the expansion of covered terrain during search is explicitly tied to the length N of the complete trajectory, via Eq.…”

Section: Updating the Homing Direction Along The Return Tripsupporting

confidence: 87%

Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination

Diard

Bessìère

Berthoz

2013

Spatial Cognition & Computation

View full text Add to dashboard Cite

Section: Updating the Homing Direction Along The Return Tripsupporting

confidence: 87%

Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination

Diard

Bessìère

Berthoz

2013

Spatial Cognition & Computation

View full text Add to dashboard Cite

“…A great deal of work has focused on sensorimotor behavior, such as orientation, legged locomotion, object avoidance, and navigation (Beer and Gallagher 1992;Kodjabachian and Meyer 1998;Vickerstaff and Di Paolo 2005). Another line of work has focused on the evolution of learning behavior (Yamauchi and Beer 1994;Floreano and Mondada 1996;Tuci, Quinn, and Harvey 2002;Izquierdo-Torres and Harvey 2006).…”

Section: Methodological Issuesmentioning

confidence: 99%

“…A more radical claim that is sometimes associated with biological embodiment is that the living state itself is fundamental to cognition (Maturana and Varela 1980;Varela et al 1991;Di Paolo 2005). The idea here is generally not that the material or biochemistry of life is essential, but rather that the organization of living systems is indispensable to their cognitive capabilities.…”

Section: Embodimentmentioning

confidence: 99%

Dynamical systems and embedded cognition

Beer

2014

The Cambridge Handbook of Artificial Intelligence

View full text Add to dashboard Cite

The conceptual frameworks that we bring to our study of cognition can have a tremendous impact on the nature of that study. They provide a set of filters through which we view the world, influencing our choice of phenomena to study, the language in which we describe these phenomena, the questions we ask about them, and our interpretations of the answers we receive. For much of the last fifty years, thinking about thinking has been dominated by the computational framework, the idea that systems are intelligent to the extent that they can encode knowledge in symbolic representations which are then algorithmically manipulated so as to produce solutions to the problems that these systems encounter (see Chapter 4 of this volume). More recently, the connectionist framework forced an important refinement of the computational framework, in which representation and computation could be distributed across a large number of loosely neuron-like units (see Chapter 5).Beginning around the mid 1980s, just as the popularity of connectionism was rising, another conceptual framework appeared (or, as in the case of connectionism, reappeared) on the scene. This framework, which, for want of a catchier label, I will call the situated, embodied, dynamical (SED) framework, focuses on concrete action and emphasizes the way in which an agent's behavior arises from the dynamical interaction between its brain, its body, and its environment. In this chapter, I will attempt to trace some of the history of the individual intellectual threads of situated activity, embodiment, and dynamics that underlie the SED approach. I will particularly focus on the years 1985-1995. Although there were important precursors to the SED approach (some of which I will briefly mention), and work in this area has grown rapidly in recent years, many of the pivotal ideas were first given their modern form during this ten-year period. Situated activityThe first intellectual thread making up the SED approach is situated activity. Roughly speaking, situated activity stresses three ideas that have been traditionally neglected in AI and cognitive science.

show abstract

“…They replicated a minimalist experiment of perceptual crossing performed with human subjects (Auvray et al, 2009), showing that evolved agents could solve the problem solely on the basis of the interaction dynamics. Vickerstaff and Di Paolo (2005) present a model of path integration displayed by desert ants (Cataglyphis fortis). Contrary to previous approaches, they make no a priori assumption about the neural encoding of path information, and instead use ER to build a complete brain-body-environment model.…”

Section: Modelmentioning

confidence: 99%

Evolutionary Robotics: Model or Design?

Trianni

2014

Front. Robot. AI

View full text Add to dashboard Cite

In this paper, I review recent work in evolutionary robotics (ER), and discuss the perspectives and future directions of the field. First, I propose to draw a crisp distinction between studies that exploit ER as a design methodology on the one hand, and studies that instead use ER as a modeling tool to better understand phenomena observed in biology. Such a distinction is not always that obvious in the literature, however. It is my conviction that ER would profit from an explicit commitment to one or the other approach. Indeed, I believe that the constraints imposed by the specific approach would guide the experimental design and the analysis of the results obtained, therefore reducing arbitrary choices and promoting the adoption of principled methods that are common practice in the target domain, be it within engineering or the life sciences. Additionally, this would improve dissemination and the impact of ER studies on other disciplines, leading to the establishment of ER as a valid tool either for design or modeling purposes.

show abstract

Evolving neural models of path integration

Cited by 60 publications

References 47 publications

Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination

Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination

Dynamical systems and embedded cognition

Evolutionary Robotics: Model or Design?

Contact Info

Product

Resources

About