Change-based inference for invariant discrimination

Under a conventional view of information processing in recurrently connected populations of neurons, computations consist in mapping inputs onto terminal attractor states of the dynamical interactions. However, there is evidence that substantial information representation and processing can occur over the course of the initial evolution of the dynamical states of such populations, a possibility that has attractive computational properties. Here, we suggest a model that explores one such property, namely, the invariance to an irrelevant feature dimension that arises from monitoring not the state of the population, but rather (a statistic of) the change in this state over time. We illustrate our proposal in the context of the bisection task, a paradigmatic example of perceptual learning for which an attractor-state recurrent model has previously been suggested. We show a change-based inference scheme that achieves near optimal performance in the task (with invariance to translation), is robust to high levels of dynamical noise and variations of the synaptic weight matrix, and indeed admits a computationally straightforward learning rule.