Learning invariant features in modulatory neural networks through conflict and ambiguity

2018-03-02This work develops a new unsupervised learning rule and framework for learning rotation, scale, and translation invariant visual features. The new learning rule, called conflict learning, is designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, it is shown that a Hebbian learning rule, which has long been a key component in understanding neural plasticity, fails to properly learn modulatory connections, while the proposed rule correctly learns a stimulus-driven model. This is the first time a border ownership network has been learned. Additionally, the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. ❧ Following the development of conflict learning, this thesis lays the foundation for a general framework of cortical learning based upon the idea of a ‘competitive column’. This column describes a prototypical organization for neurons that gives rise to an ability to learn scale, rotation, and translation invariant features. This is empowered by conflict learning and a novel notion of neural ambiguity, which states that ’too many decisions lead to indecision’. The competitive column architecture is used to create a large scale (54,000 neurons and 18 million synapses), invariant model of border ownership that is trained on simple shapes such as squares and rectangles yet generalizes to multiple scales and complexities of input, including a demonstration on contours of objects taken from natural images. ❧ Conflict learning and ambiguity lead to testable predictions related to the learning of modulatory connections in the brain and associated activation dynamics. ❧ These contributions of conflict learning, the competitive column, and ambiguity give a better intuitive understanding of how feedback, modulation, and inhibition may interact in the brain to influence activation and learning. Additionally, they provide a promising avenue for advancing object recognition systems