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Abstract
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.
Highlights
The way in which neural processing of sensory inputs leads to cognitive functions is one of the most important issues in neuroscience
We focus on the dependence of the neural and synaptic dynamics on two parameters: the learning parameter a and the input strength clrn
We have proposed an associative memory model with a simple learning rule that realizes the viewpoint of memories-as-bifurcations in which neural activities are transformed appropriately by each input to generate the requested targets
Summary
The way in which neural processing of sensory inputs leads to cognitive functions is one of the most important issues in neuroscience. Neural activity in the presence of sensory stimuli [1,2,3,4] and during the execution of cognitive tasks in response to sensory inputs have been measured experimentally [5,6], and neural network models that exhibit the requested responses to the inputs have been investigated theoretically [7,8,9,10,11,12]. Many observations have revealed that the response activities to external stimuli [20,21] or cognitive tasks depend on the spontaneous activity [22,23]. Evoked responses are generated by external inputs and through the interplay of the spontaneous activity and external stimuli. To establish a neural basis for the cognition and computation in a neural system, it is important to understand the nature of this interplay
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