Activation and coherence in memory processes: Revisiting the Parallel Distributed Processing approach to retrieval

Abstract

Connectionist models based on activation spreading and attractor dynamics are functionally limited by representational and processing flexibility constraints, the 'feature binding problem' and the need to balance accurately activation and inhibition. We suggest an alternative approach, in which network units are characterized by two variables: activation and phase. Whereas activation evolves according to a 'classical' connectionist rule, the phase variable is characterized by a chaotic evolution. We present a model of memory retrieval with reference to the paradigmatic McClelland's 1981 'Jets and Sharks' model. The model solves the 'multiple reinstantiation problem', i.e. the problem of retrieval of multiple items with overlapping features, implied by its classical predecessor. In our network, multiple pattern reinstantiation in terms of activation spreading is disambiguate through selective and differential coherence patterns. The system flexibly represents pattern similarity and feature relationships by means of graded and intermittent synchrony. The domain-general implications of this approach for connectionist 'interactive activation models' and its neurophysiological plausibility are discussed.