Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention

Abstract

The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson-Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single 'noise term'; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.

Recognition of Abstract Objects Via Neural Oscillators: Interaction Among Topological Organization, Associative Memory and Gamma Band Synchronization

“…Hence, our model does not intend to represent image processing in the visual cortex, but rather processing in higher cortical associative areas. This aspect clearly distinguishes our model from other models [40], [50], [51], [54], [65], which are explicitly devoted to image segmentation and in which different objects are separated on the basis of spatial properties (such as proximity, smoothness, common fate, etc.). As summarized by Wang in a recent review paper [66], these models were especially focused on the figure-ground separation problem.…”

“…In this study, we adopted an exemplary network with four areas and 400 neural groups per area . As already described in our previous works [50], [51], each single oscillator consists of a feedback connection between an excitatory unit and an inhibitory unit (see Fig. 2), while the output of the network is the activity of all excitatory units.…”

“…represents the activity of a global inhibitor. This is described with the following algebraic equation (see [50] and [51] for more details): (4) According to (4), the global inhibitor computes the overall excitatory activity in the network, and sends back an inhibitory signal when this activity overcomes a given threshold (say ). Its role is to ensure separation among the objects simultaneously present.…”