Neural representations of compositional structures: representing and manipulating vector spaces with spiking neurons
This paper re-examines the question of localist vs. distributed neural representations using a biologically realistic framework based on the central notion of neurons having a preferred direction vector. A preferred direction vector captures the general observation that neurons fire most vigorously when the stimulus lies in a particular direction in a represented vector space. This framework has been successful in capturing a wide variety of detailed neural data, although here we focus on cognitive representation. In particular, we describe methods for constructing spiking networks that can represent and manipulate structured, symbol-like representations. In the context of such networks, neuron activities can seem both localist and distributed, depending on the space of inputs being considered. This analysis suggests that claims of a set of neurons being localist or distributed cannot be made sense of without specifying the particular stimulus set used to examine the neurons.