Autoassociative dynamics in the generation of sequences of hippocampal place cells

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Science  10 Jul 2015:
Vol. 349, Issue 6244, pp. 180-183
DOI: 10.1126/science.aaa9633

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Memory storage in neural networks

Neuronal networks can store and retrieve discrete memories, but often fail to retrieve stored sequences. This is because error decreases over time for a static attractor, but builds up drastically over time if patterns are not trained to retrieve themselves but to retrieve the next item in a sequence. Pfeiffer and Foster studied brain activity in awake but immobile rats. Recording simultaneously from a large number of place cells in the hippocampal formation, they found that internally generated sequences alternated between periods of hovering in place while being strengthened, and periods of abrupt transition to a new place.

Science, this issue p. 180


Neuronal circuits produce self-sustaining sequences of activity patterns, but the precise mechanisms remain unknown. Here we provide evidence for autoassociative dynamics in sequence generation. During sharp-wave ripple (SWR) events, hippocampal neurons express sequenced reactivations, which we show are composed of discrete attractors. Each attractor corresponds to a single location, the representation of which sharpens over the course of several milliseconds, as the reactivation focuses at that location. Subsequently, the reactivation transitions rapidly to a spatially discontiguous location. This alternation between sharpening and transition occurs repeatedly within individual SWRs and is locked to the slow-gamma (25 to 50 hertz) rhythm. These findings support theoretical notions of neural network function and reveal a fundamental discretization in the retrieval of memory in the hippocampus, together with a function for gamma oscillations in the control of attractor dynamics.

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