Proceedings of the Annual Meeting of the Cognitive Science Society

We present a novel method for constructing neurally imple-mented spatial representations that we show to be useful forbuilding models of spatial cognition. This method representscontinuous (i.e., real-valued) spaces using neurons, and iden-tifies a set of operations for manipulating these representa-tions. Specifically, we use “fractional binding” to construct“spatial semantic pointers” (SSPs) that we use to generate andmanipulate representations of spatial maps encoding the posi-tions of objects. We show how these representations can betransformed to answer queries about the location and identitiesof objects, move the relative or global position of items, andanswer queries about regions of space, among other things.We demonstrate that the neural implementation in spiking net-works of SSPs have similar accuracy and capacity as the math-ematical ideal.