Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning
Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, “memories-as-bifurcations,” that differs from the traditional “memories-as-attractors” viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple Hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in our previous study. The neural activity without explicit stimuli shows highly structured patterns in space and time, known as spontaneous activity. This spontaneous activity plays a key role in the behavior of the response to external stimuli generated by the interplay between the spontaneous activity and external input. Studying this interplay and how it is shaped by learning is an essential step toward understanding the principles of neural processing. To address this, we proposed a novel viewpoint, memories-as-bifurcations, in which the appropriate changes in the activity upon the input are embedded through learning. Based on this viewpoint, we introduce here an associative memory model with sequential learning by a simple Hebbian-type rule. In spite of its simplicity, the model memorizes the input/output mappings successively, as long as the input is sufficiently large and the synaptic change is slow. The spontaneous neural activity shaped after learning is shown to itinerate over the memorized targets in remarkable agreement with the experimental reports. These dynamics may prepare and facilitate to generate the learned response to the input. Our results suggest that this is the possible functional role of the spontaneous neural activity, while the uncovered network structure inspires a design principle for the memories-as-bifurcations.