CNRG Research Examples

Function Attractors (Memory)

There are currently a number of models that use spiking neurons in recurrent networks to encode a stable Gaussian 'bump' of activation. These models successfully capture some behaviors of various neural systems (e.g., storing a single spatial location in parietal cortex). We extend this previous work by showing how to construct and analyze realistic spiking networks that encode smooth n-dimensional functions drawn from a finite functional space. These new networks can capture additional experimentally observed behavior (e.g., storing multiple spatial locations at the same time). [Detailed Example][Paper]