Retrieval of branching sequences in associative memory model with common external input and bias input

摘要

We investigate a recurrent neural network model with common external and biasinputs that can retrieve branching sequences. Retrieval of memory sequences isone of the most important functions of the brain. A lot of research has beendone on neural networks that process memory sequences. Most of it has focusedon fixed memory sequences. However, many animals can remember and recallbranching sequences. Therefore, we propose an associative memory model that canretrieve branching sequences. Our model has bias input and common externalinput. Kawamura and Okada reported that common external input enablessequential memory retrieval in an associative memory model with auto- and weakcross-correlation connections. We show that retrieval processes along branchingsequences are controllable with both the bias input and the common externalinput. To analyze the behaviors of our model, we derived the macroscopicdynamical description as a probability density function. The results obtainedby our theory agree with those obtained by computer simulations.