Abstract
Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.
Highlights
The temporal unfolding of an event is an essential component of episodic memory
We introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, that spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval
By calculating the storage capacity we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes
Summary
The temporal unfolding of an event is an essential component of episodic memory. When we recall past events, or we imagine future ones, we do not produce static images but temporally structured movies, a phenomenon that has been referred to as ”mental time travel” [1], [2]. The quantification of the effect of loops in the dense connectivity regime, developed in [53] and [54] for the case of static, discrete attractors, is beyond the scope of the present work and remains an interesting open direction In both the fully connected and the highly diluted case we study the dependence of the capacity on two important parameters: the map sparsity, i.e. the ratio between the width of the connectivity kernel (fixed to one without loss of generality) and the size L of the stored manifolds, and the asymmetry strength γ. In particular this suggests that the natural tendency of the neural activity to show a rich spontaneous dynamics does not hinder the possibility for multiple memories to coexist in the same population, but can be a crucial ingredient for the correct functioning of memory mechanisms
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