Neural dynamics of spreading attentional labels in mental contour tracing

Accentuation, Boolean maps and perception of (dis)similarity in a neural model of visual segmentation

We developed an interactive cortical circuit for visual segmentation that integrates bottom-up and top-down processing to segregate or group visual elements. A bottom-up pathway incorporates stimulus-driven saliency computation, top-down feature-based weighting by relevance and winner-take-all selection. A top-down pathway encompasses multiscale feedback projections, an object-based attention network and a visual segmentation network. Computer simulations have shown that a salient element in the stimulus guides spatial attention and further influences the decomposition of the nearby object into its parts, as postulated by the principle of accentuation. By contrast, when no single salient element is present, top-down feature-based attention highlights all locations occupied by the attended feature and the model forms a Boolean map, i.e., a spatial representation that makes the feature-based grouping explicit. The same distinction between bottom-up and top-down influences in perceptual organization can also be applied to texture perception. The model suggests that the principle of accentuation and feature-based similarity grouping are two manifestations of the same cortical circuit designed to detect similarities and dissimilarities of visual elements in a stimulus.

Recurrent neural networks that learn multi-step visual routines with reinforcement learning

Many cognitive problems can be decomposed into series of subproblems that are solved sequentially by the brain. When subproblems are solved, relevant intermediate results need to be stored by neurons and propagated to the next subproblem, until the overarching goal has been completed. We will here consider visual tasks, which can be decomposed into sequences of elemental visual operations. Experimental evidence suggests that intermediate results of the elemental operations are stored in working memory as an enhancement of neural activity in the visual cortex. The focus of enhanced activity is then available for subsequent operations to act upon. The main question at stake is how the elemental operations and their sequencing can emerge in neural networks that are trained with only rewards, in a reinforcement learning setting. We here propose a new recurrent neural network architecture that can learn composite visual tasks that require the application of successive elemental operations. Specifically, we selected three tasks for which electrophysiological recordings of monkeys' visual cortex are available. To train the networks, we used RELEARNN, a biologically plausible four-factor Hebbian learning rule, which is local both in time and space. We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operations, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observed in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subsequent subroutines. Our results demonstrate how a biologically plausible learning rule can train a recurrent neural network on multistep visual tasks.

A multi-scale neurodynamic implementation of incremental grouping

Incremental grouping is a process entailing serial binding of distal image elements into a unified object representation. At the neural level, incremental grouping involves propagation of the enhanced firing rate among feature-tuned neurons in the early visual cortex. Here, we developed a multi-resolution neural model of incremental grouping. In the model, propagation of the enhanced firing rate is achieved by computing the activity difference between two sets of units: attentional or A-units, whose firing rate is modulated by their horizontal collaterals, and non-attentional or N-units that receive only feedforward input. The activity difference is computed on dendrites that act as independent computational subunits. The proposed model employs multiple spatial scales to account for a variable speed of incremental grouping. In addition, the model incorporates the L-junction detection network that enables incremental grouping over L-junctions. Computer simulations show that the timing of attentional modulations in the model is comparable with neurophysiological measurements in monkey primary visual cortex.