A Dynamic Neural Field Approach to the Covert and Overt Deployment of Spatial Attention

Abstract

The visual exploration of a scene involves the interplay of several competing processes (for example to select the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mechanisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asynchronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elementary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom-up and top-down information in order to perform a visual search task with saccadic eye movements.

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning

“…Based on the functional anatomy of BG, various functional models of BG have been proposed [21,22,45,46,17,47]. Focusing on the computational model [21,22], competing actions are represented throughout the nervous system.…”

DANA: Distributed numerical and adaptive modelling framework

“…Saccadic exploration of a visual environment, Fix et al (2011Fix et al ( , 2007 In (Fix et al 2011(Fix et al , 2007 we proposed a distributed mechanism for exploring a visual environment with saccadic eye movements (see Figure 8). In this section and in the simulation script provided online, we detail how this mechanism can be implemented within the DANA framework.…”

Randomly Spiking Dynamic Neural Fields

“…Dynamic neural fields have been applied to a wide variety of tasks and brain mechanisms: memory [Johnson et al 2009;Vitay et al 2005], visual perception [Wilimzig et al 2006;Zibner et al 2010], visual attention [Vitay et al 2005;Fix et al 2011;Vazquez et al 2011;, video tracking ], classification of motion patterns [Cerda and Girau 2013], object recognition [Faubel and Schöner 2008], multimodal perception ], driving assistance [Edelbrunner et al 2001], sensorimotor control [Erlhagen and Schner 2002], cognitive robotics [Erlhagen and Bicho 2006;Bicho et al 2010], and so on. Several applications require embedded real-time processing; the parallel structure and regularity of DNF is often mentioned to anticipate efficient hardware implementations.…”