Generalized memory of STDP-driven spiking neural network

We propose a memory model based on the spiking neural network with Spike-Timing-Dependent Plasticity (STDP). In the model, information is recorded using local external stimulation. The memory decoding is a functional response in the form of population bursts of action potentials synchronized with the applied stimuli. In our model, STDP-mediated weights rearrangements are able to encode the localization of the applied stimulation, while the stimulation focus forms the source of the vector field of synaptic connections. Based on the characteristics of this field, we propose a measure of generalized network memory.
With repeated stimulations, we can observe a decrease in time until synchronous activity occurs. In this case, the obtained average learning curve and the dependence of the generalized memory on the stimulation number are characterized by a power-law. We show that the maximum time to reach a functional response is determined by the generalized memory remaining as a result of previous stimulations. Thus, the properly learning curves are due to the presence of incomplete forgetting of previous influences.
We study the reliability of generalized network memory, determined by the storage time of memory traces after the termination of external stimulation. The reliability depends on the level of neural noise, and this dependence is also power-law. We found that hubs – neurons that can initiate the generation of population bursts in the absence of noise – play a key role in maintaining generalized network memory. The inclusion of neural noise leads to the occurrence of random bursts initiated by neurons that are not hubs. This noise activity destroys memory traces and reduces the reliability of generalized network memory.