Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike Timing Dependent Plasticity

Abstract

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors, but it is not clear how attractors are formed or used in processing. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike timing dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern, or become disordered, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8Hz. Upon restimulation, the attractors selfoscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10-100Hz.