Memory retrieval is of central importance to a wide variety of brain functions. To understand the dynamic nature of memory retrieval and its underlying neurophysiological mechanisms, we develop a biologically plausible spiking neural circuit model, and demonstrate that free memory retrieval of sequences of events naturally arises from the model under the condition of excitation-inhibition (E/I) balance. Using the mean-field model of the spiking circuit, we gain further theoretical insights into how such memory retrieval emerges. We show that the spiking neural circuit model quantitatively reproduces several salient features of free memory retrieval, including its semantic proximity effect and log-normal distributions of inter-retrieval intervals. In addition, we demonstrate that our model can serve as a platform to examine memory retrieval deficits observed in neuropsychiatric diseases such as Parkinson's and Alzheimer's diseases. Furthermore, our model allows us to make novel and experimentally testable predictions, such as the prediction that there are long-range correlations in the sequences of retrieved items.
['Yifan Gu', 'Pulin Gong']
The dynamics of memory retrieval in hierarchical networks